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ke2C8vfjgV/Y9kkRQOJIn/xM9INZSpiBnqX0Q9GlQPpPKAyO5y+W5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W/N6l54qOynCqD3DpWQ+mpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE+7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv/8lbTIkkYpqKM9VOhp65ktYk+Q46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI+ejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5+7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu+vtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY/iOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v+6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ/qDQK+bfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam+WpHG+0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3+J1eCBay8IYZ0wQRKGAqvCuZ/UgbQPyllosq+XtfKIZOzmeJqRazpmmoP/76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t/I4Jktu0XSgifO2ozFOiwd/0SssJDn0dn4xqk4GDTTKX73/wQyBLdqgJ+Wx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy/qqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh/wkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml/R4yrzow1Q2A5G+kzo/RhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj/UMMc34IBqTKLDTp76WzL/dMjCxK7MjhiGjeYAC/kj/jY/Rde7hpSM1xChrog6yZ7OWTuD56xBJnGFE+pT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G+9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36/dBySis4m9/DR8izaLJW6bWCkVgm5T+ius3ZXq4xI+GnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te/r4dPYMCl5qtiHNTn+TPbh1jCBHH+dMJNhwNgs3nT+OhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF+uRIWyXjQMAs2chdpaKPNaB+kSezYt0+CA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6/TzoA1/ZBG9bIUVHLAbi/kdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh+epgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF+zkJHIA7PwCDk1gGVmGUZSSoPhNf+Tklauz98QofOlCIQ/tCD4dosHYPqtPCXB3agggQQIqQJsSkB+qn0rkQ1toJjON/OtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU+TQ6NIw3ej+AtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb++W6Uk4q6F7/rg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK+EfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l+wM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l+DMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg+EWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb+Mw76Qy29iQ5up/X7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa/xnsMYcIO/vEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz/Chp/Vlpj2P7jJQmQRwGnltkTV5dbF9fE3/fxoSqTROgq9wFUlbuYzYcasE0ouzBo+dDCDzxKAfhbAZYxQiHrLzV2iVexnDX/QnT1fsT/xuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR+CtGdkPwYN2o7DOw/VGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO/f9Qua+pDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD/prQ84B1pVGkIpVUAHCG+iz3Bn3qm2AVrYcYWhock4jso5+J7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk+UILT7+VoX5mdxxA5fS42gISQVi/HTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8/6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk/wdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt+Su9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ/BfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk/gc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B//lHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO+CvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd/p1gi/07h8qfwHrByuSxglc9cI4QIg2oqvC/qm0i7tjPLTgDhoWTAKDO2ONW5oe+/eKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2/j4ODUwRkqrtBBCrDsDpt8jhZdXoy/1BCqw3sSGhgGGy0a5Jw6BP/TExoCmNFYjZl248A0osgPyGEmRA+fAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd/ocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE/EZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ/Ugd/JZQK8lvAm43uDRAbyW8gZ+ZGq0EVerVGUKUSm/Idn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ+QATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm+oOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724+zUQ7+vkCpZB+pGA562hYQiDxHVWOq0oDQl/QsoiY+cuI7iWq/ZIBtHcXJ7kks+h2fCNUPA82BzjnqktNts+RLdk1VSu+tqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy/44XYXdI5noQoRcvjZ1RMPACRqYg2V1+OwOepcOknRLLFdYgTkT5UApt/JhLM3jeFYprZV+Zow2g8fP+U68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr/A1SY9dXFz4RjzoU9ExfJCmx/I9FKEGT3n2cmzl2X42L3Jh+AbQq6sA+Ss1kitoa4TAYgKHaoybHUDJ51oETdeI/9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB+Un44zExFE4vLytcu5NwpWrUxO/0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL+BDqMyaN3RJPh/anbx+Iv+qgQdAa3M9Z5JmvYlh4qop+Ho1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs+gs37sFvi0PPVvA5dnCBgILTwoKd/+DoL9F6inlM7H4rOTzD79KJgKlZO/Zgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno+gBoKVXgIL/VI8dB1O5o/R3Suez/gD7M781ShjKpIIORM/nxG+jjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4/QftDbEn+AucIr1oxrLabRj9q4ae0+fXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd+eNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz/6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT+mAUmiYbV3YQVqFVp9dorv+TsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp+xJyYLv1OsxQCZwTB4a8BZ/5EdxTBJthApqyfd9u3ifr/WILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj/qn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9+W8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4/YtowhEmTs0vrvlD/CrisnoBNDAcUi/teY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O/4DcgV/dZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk+tgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb/n7qwhvGnrHuf5bX6Vh/n3xffU3PeHdR+FA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E+YbfL0adwNtHP7dT7t7b46DVZIkzaRJOM+S6KcrzYVg+T3wSRFRQashjfU18NutrKa/7PXbtuJvpIjbgPeqd+pjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir/8tNXJ/OsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG+FZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx/GdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD/Qi/EmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4/asthNMK5UQKCOhU97oaOYNGsTah+jfCKsZnTRn5TbhFX8ghg8CBYt/BjeYYYUrtUZ5jVij/op7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM+3SW6Opll/wgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy+QbSKVJcqkia+GvDefFwMOmgnD7h81TUtMn+mRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d/QK7Cr4uoCeOQ7/8JfKT77KiDzLImESHw/0wf73QeHu74hxv7uihi4fTX+XEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo/oVH5ww5OzLFsiTPDns7fS6EURSSWd/92BxMYQ8sBaH+j+wthQPdVgDGpTfi+JQIWMD8xKqULliRH01rTeyF8x8q/GBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE+/7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV/yTDRRP8Y2ww5RO6d2D94o+6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt+kqdae76ViWe3STan69yaF9+fESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw/A1zEdjWquIsQXXGIVEH0thC5M+W9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF+RVmI8L4HUYk4x+67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8/p5qIQrEo/H+1l/0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud+tJUfdaZ4CWNijzZtlRa8+CkmO/EwHYfPZFU/hzjFWH7vnzHRMo+aF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce/+/9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs/GWJS6SwEN/ULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e+G2zor8l+YaMxSEomDdLHGcD6YVQPegTaA74H8+V4WvJkFUrjMLGLlvSZQWvi8/QA7yzQ8GPno//5SJHRP/OqKObPCo81s/+6WgLqykYpGAgQZhVDEBPXWgU/WzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M+GWn6ASobIWC+LbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg/kpf3+CnAXKiMgIE8Jk/Mhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo+RJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B+SkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE+VMd3b1fhCynD0pQNhCG6/WCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp/YbHjo++7/Wj8S4YNa+ZdqAw1hDrKWFXv9+zaXpf8ZTDSbiqsxnwN/CzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m/NCW/HILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO+5EJ7Z6bCiRoPedRZ/P0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn/LvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl+11PoFYnNv2HwAODeNRkHj+8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij+bsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur/eWHjiB7SOVdqMAVmpBvfRiebsFjger7DC+8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l+kXRZ0KLZaGsFSIdQz/HXDxf3/TE30+DgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH+RptvRMVRaahu4cySjS3P5wxAUCPkmn+rhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik/zbrTQk5KmgxCg/f45L0jywebOWUYFJQAJia7XzCV0x89rpp/f3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl+5tfUWZNwBpEPXghzbBggYCw/dhy0ntds2yeHCDKkF/YxQjNIL/F/37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD+qNOhwMlfARQUdJ2tUX+MNJqOwIciWalZsmEjt07tfa8ma4cji9sqz+Q9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe+jGDiNJQLWnfx+drTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf/bkvo8PLVBsZl152y5S8+HRDfZIMCbYZ1WDp4yrdchOJw8k6R+/2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB+M4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5/iYp3ZdrCf7fL+en+sIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv+NRiJc8JAKqqgCA/PNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN/hCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj+bYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5/zBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8/i+jHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2+JrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk+Mz7wwstg6RFZb+TZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm/7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk+9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx+whVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC+XYuqMBMUun5YezKbRKmEPwuK+CLzijPEQgfhahQswBBLfg/GBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX+RCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk/4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug/AbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM+Zu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX/pcsfwXbLze2+D+u33OGBoJyAAL3jn3RuEcdp5If8O+a4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT++tm+haBCikRUUMrMhYKZJKYoVuv/bsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV/DMUxd9uFZmBfKXMCn/SqkWJyKPnT6lq+4zBZni6fYRByJn6OK+OgPBGRAJluwGSk4wxjOOzyce/PKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO/cEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm+bddRQu5F9s0XftGEJ9JSuSk+ZachCbdU45fEqbugzTIUokwoAKvpUQF/CvLbWW5BNQFqFkJg2f30E/48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J+1pT0tLkvFVZaNzfhs/Kd3+A9YsImlO4XK4vpCo/elHQi/9gkFg07xxnuXLt21unCIpDV+bbRxb7FC6nWYTsMFF8+1LUg4JFjVt3vqbuhHmDKbgQ4e+RGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB/KVijd1ARWkFEf3yiUw1v/WaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G+ZManTqDLPjyrOse7WiiwOJCG+J0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj/6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j/N46f+S2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb/0jQbaHJ2YRK8A+ls6WMhWmpCXYG5jqapGs5/eOJErxi2/2KWVHiPellTgh/fNl/2KYPKb7DUcAg+mCOPQFCiU9Mq/WLcU1xxC8aLePFZZlE+PCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh/nFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW+oKFLvpyvTBMM69tN1Ydwv1LIEhHsC+ueVG+w+kyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw+H/AuOx+aH+tBL88H57D0MsqyiZxhOEQkF/8DR1d2hSPMj/sNOa5rxcUnBgH8ictv2J+cb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd/PjMMtQfyDNZsOPd6XcAsnBE/mRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl/XPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG/VanIvcwycVA7+BE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP/MVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX/5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c/F1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J/5wkjpkre727p5PTRX5FGrSBIfJqhJE/IS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug+oRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U/5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7/BQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN+nA7pvF78/RII5ZHA09OAiE/66MF6HQ+qVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe+hXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz+JV/4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB+K3wBP/ineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q/9uocGsx41O4IZhViw/2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY/cQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE/om7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi/caHSM3FPRGRf7dB7YC+cD2ho6oL2zGDCkjlf/DFoQVl8GS/56wur3rdV6ggtzZW60MRB3g+U1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg/gBQIZMG/YcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK/G7F3mUc3GOAKqh60zM0v34v+ELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND+/GTk6M56Ig4yMsU6LUW1EWE+fIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP/IoRIZ/F6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg/RrPD/d3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl+Mu4xf0ezqeXD2PtPDnwMPo86sbwDV+9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD/OwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d/UfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH+14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC+OA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6+vAUUBoGhY3CT2tgwehdPqU/4Q7ZLYvhRl/ogOvR9O2+wkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn/dFSWBWzQ/VYk+Gezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n/yJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET/Hh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j+DXfpi12m1RbzYLg9J2wFergEwOxFyD0/JstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT/cUP6pE/mujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB+HEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu+vqQ02+KpJBjaLt9ye1Ab+BbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC+UwkXOoAjneU/xHplMQo2cXUlrVNqJYczgYlaOEczVCs/OCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38+xsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9+b8fH6+b8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj/0Q7PFUcC8hDrxESWdfgFRm+7vvWbkEppHB4T/1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y+g3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR/Rrs/JLKXgEx+qkmeDlFOD1/yTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW+xz+5FElFxWB28VjYIGZ0Yd+5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ+lT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ+2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1+JpI9psyNYIFuJogZa0/1AhOWdlHQxdAgbwacsHqPZo8u/ngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2+RdM+MAaYaZ0Y/ADkbNCZuAyAVQa2OcXOeICmDn9Q/eFkDeFQg5MgHEDXq/tVjj+jtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2/Bc0UxvseQCO2pQ2i+Krfhu/WeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt/U0Wf+phiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7+ExseJauyqo30hs+1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j/e1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la/QCTiVelFnU6O/GCvykqS/wZJDhKN9gBtSOp/1SP5VRgJcoVj+kmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn+8p6+vBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H+gDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D/GvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P+jAgN5TB3haSifDcApp6yymEi6Ij/GsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x/pChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11/yxyw0UnGig3MFdZklN5FI/qiT65T+jOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5+bqWiAYiAv6Jsf79/VUs4cIl+n6+WOjcgB/2l5TreoAV2717JzZbQIR0W1cl/dEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW/PhoawJDrGAP0JYWHgAVUByo/bGdiv2T2EMg8gsS14/rAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq+SCcTSx5NDtbFwNlh8VhjGGDu7JG5/TAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9+ogD8Kk/W7QoRN1UWPqM4+xdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c+4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS+o3F8YVVeikw13w+OEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX++7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9/O53DYi/5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD+P8sNh6e+XYHJXT/lkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp+pT0KYV5dGtknU64JhabdqcVQbG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) 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role="separator" class="divider"></li> <li><a id="rmd-download-source" href="#">Download Rmd</a></li> </ul> </div> <h1 class="title toc-ignore">IMPC Mouse data - Variance in sex differences</h1> <h3 class="subtitle">Electronic Supplementary Material</h3> <h4 class="author">Susanne Zajitschek, Felix Zajitschek, Russell Bonduriansky,Robert Brooks, Will Cornwell, Daniel Falster, Malgortaza Lagisz, Jeremy Mason, Daniel Noble, Alistair Senior & Shinichi Nakagawa</h4> <h4 class="date">August 2019</h4> </div> <div id="TOC"> <ul> <li><a href="#set-up">Set-up</a><ul> <li><a href="#loading-packages-custom-functions">Loading packages & custom functions</a></li> <li><a href="#functions">Functions</a><ul> <li><a href="#subsetting-data">1) Subsetting data</a></li> <li><a href="#population-statistics">2) “Population statistics”</a></li> <li><a href="#extraction-of-effect-sizes-and-sample-variances">3) Extraction of effect sizes and sample variances</a></li> <li><a href="#calculate-meta-analysis-statistics">4) Calculate meta-analysis statistics</a></li> </ul></li> <li><a href="#load-clean-data">Load & clean data</a><ul> <li><a href="#data-loading-and-cleaning-of-the-csv-file">1) Data loading and cleaning of the csv file</a></li> <li><a href="#table-1-strains-and-center-sample-sizes">Table 1: “Strains and Center Sample Sizes”</a></li> </ul></li> </ul></li> <li><a href="#meta-analyses">Meta-analyses</a><ul> <li><a href="#population-as-analysis-unit">1. Population as analysis unit</a><ul> <li><a href="#loop-meta-analyses-on-all-traits">Loop: Meta-analyses on all traits</a></li> <li><a href="#merging-datasets-removal-of-non-converged-traits">Merging datasets & removal of non-converged traits</a></li> <li><a href="#removal-of-traits">Removal of traits</a></li> </ul></li> <li><a href="#meta-analysis-condensing-non-independent-traits">2. Meta-analysis: condensing non-independent traits</a><ul> <li><a href="#dealing-with-correlated-parameters-preparation">Dealing with Correlated Parameters, preparation</a></li> <li><a href="#table-2-numbers-of-correlated-and-uncorrelated-traits">Table 2: Numbers of correlated and uncorrelated traits</a></li> <li><a href="#table-3-full-corrected-dataset">Table 3: Full corrected dataset</a></li> </ul></li> <li><a href="#second-order-meta-analysis-for-functional-groups">3. Second-order meta-analysis for functional groups</a><ul> <li><a href="#performing-meta-analyses-3-for-each-of-the-9-grouping-terms-lncvr-lnvr-lnrr">Performing meta-analyses (3 for each of the 9 grouping terms: lnCVR, lnVR, lnRR)</a></li> <li><a href="#re-structuring-the-data-for-each-grouping-term">Re-structuring the data for each grouping term</a></li> </ul></li> </ul></li> <li><a href="#visualisation">Visualisation</a><ul> <li><a href="#figure-4">Figure 4</a><ul> <li><a href="#overall-results-of-second-order-meta-analysis-figure-4-panel-b">Overall results of second order meta-analysis (Figure 4, Panel B)</a></li> <li><a href="#fig-4-sz-still-to-do">Fig 4 # SZ STILL TO DO</a></li> <li><a href="#figure-4-1">Figure 4</a></li> </ul></li> <li><a href="#figure-5">Figure 5</a><ul> <li><a href="#preparation-for-plots-on-significant-sex-bias-second-order-meta-analysis-results">Preparation for Plots on significant sex-bias (Second-order meta analysis results)</a></li> </ul></li> <li><a href="#join-code-missing">JOIN!!! CODE MISSING??</a></li> </ul></li> <li><a href="#supplemental-plots">Supplemental Plots</a><ul> <li><a href="#figure-s1">Figure S1</a><ul> <li><a href="#including-lnvr">Including lnVR</a></li> <li><a href="#count-data-including-lnvr-fig-s1-panel-a">Count data, including lnVR (Fig S1 panel A)</a></li> <li><a href="#overall-results-of-second-order-meta-analysis-including-vr">Overall results of second order meta analysis, INCLUDING VR</a></li> <li><a href="#heterogeneity">Heterogeneity</a></li> </ul></li> <li><a href="#figure-s2">Figure S2</a><ul> <li><a href="#prepare-data-for-traits-with-effect-size-ratios-10-larger-in-males-supplemental-figure-s2">Prepare data for traits with effect size ratios > 10% larger in males, supplemental Figure S2</a></li> <li><a href="#felix-all-missing.-felix-added-1122020-done">FELIX: “ALL” missing. Felix added 11/2/2020: done</a></li> <li><a href="#over-10-male-bias-count-data-first--order-metanalysis">Over 10% male bias, count data (first- order metanalysis)</a></li> </ul></li> <li><a href="#not-sure-what-this-below-is-felix-added-1122020-ich-glaube-dass-das-von-vorher-war.-wenn-wir-alles-oben-haben-dann-unten-loeschen">NOT SURE WHAT THIS BELOW IS?? Felix added 11/2/2020: Ich glaube, dass das von vorher war. wenn wir alles oben haben, dann unten loeschen</a></li> <li><a href="#figure-s2-sex-bias-including-vr">Figure S2: sex-bias, including VR</a><ul> <li><a href="#perc-sex-difference-male-bias">10 % Perc sex difference, male bias</a></li> </ul></li> <li><a href="#acknowledgements">Acknowledgements</a></li> <li><a href="#r-session-information">R Session Information</a></li> </ul></li> </ul> </div> <!-- rnb-text-begin --> <div id="set-up" class="section level1"> <h1>Set-up</h1> <div id="loading-packages-custom-functions" class="section level2"> <h2>Loading packages & custom functions</h2> <!-- rnb-text-end --> <!-- rnb-text-begin --> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGlicmFyeShyZWFkcilcbmxpYnJhcnkoZHBseXIpXG5gYGAifQ== --> <pre class="r"><code>library(readr) library(dplyr)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiXG5BdHRhY2hpbmcgcGFja2FnZTog4oCYZHBseXLigJlcblxuVGhlIGZvbGxvd2luZyBvYmplY3RzIGFyZSBtYXNrZWQgZnJvbSDigJhwYWNrYWdlOnN0YXRz4oCZOlxuXG4gICAgZmlsdGVyLCBsYWdcblxuVGhlIGZvbGxvd2luZyBvYmplY3RzIGFyZSBtYXNrZWQgZnJvbSDigJhwYWNrYWdlOmJhc2XigJk6XG5cbiAgICBpbnRlcnNlY3QsIHNldGRpZmYsIHNldGVxdWFsLCB1bmlvblxuIn0= --> <pre><code> Attaching package: ‘dplyr’ The following objects are masked from ‘package:stats’: filter, lag The following objects are masked from ‘package:base’: intersect, setdiff, setequal, union</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGlicmFyeShtZXRhZm9yKVxuYGBgIn0= --> <pre class="r"><code>library(metafor)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiTG9hZGluZyByZXF1aXJlZCBwYWNrYWdlOiBNYXRyaXhcbkxvYWRpbmcgJ21ldGFmb3InIHBhY2thZ2UgKHZlcnNpb24gMi4xLTApLiBGb3IgYW4gb3ZlcnZpZXcgXG5hbmQgaW50cm9kdWN0aW9uIHRvIHRoZSBwYWNrYWdlIHBsZWFzZSB0eXBlOiBoZWxwKG1ldGFmb3IpLlxuIn0= --> <pre><code>Loading required package: Matrix Loading 'metafor' package (version 2.1-0). For an overview and introduction to the package please type: help(metafor).</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGlicmFyeShkZXZ0b29scylcbmBgYCJ9 --> <pre class="r"><code>library(devtools)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiTG9hZGluZyByZXF1aXJlZCBwYWNrYWdlOiB1c2V0aGlzXG4ifQ== --> <pre><code>Loading required package: usethis</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGlicmFyeShwdXJycilcbmxpYnJhcnkodGlkeXZlcnNlKVxuYGBgIn0= --> <pre class="r"><code>library(purrr) library(tidyverse)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin 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 --> <pre><code>[30m── [1mAttaching packages[22m ────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──[39m [30m[32m✔[30m [34mggplot2[30m 3.2.0 [32m✔[30m [34mtidyr [30m 1.0.2 [32m✔[30m [34mtibble [30m 2.1.3 [32m✔[30m [34mstringr[30m 1.4.0 [32m✔[30m [34mggplot2[30m 3.2.0 [32m✔[30m [34mforcats[30m 0.4.0[39m [30m── [1mConflicts[22m ───────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ── [31m✖[30m [34mtidyr[30m::[32mexpand()[30m masks [34mMatrix[30m::expand() [31m✖[30m [34mdplyr[30m::[32mfilter()[30m masks [34mstats[30m::filter() [31m✖[30m [34mdplyr[30m::[32mlag()[30m masks [34mstats[30m::lag() [31m✖[30m [34mtidyr[30m::[32mpack()[30m masks [34mMatrix[30m::pack() [31m✖[30m [34mtidyr[30m::[32munpack()[30m masks [34mMatrix[30m::unpack()[39m</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGlicmFyeSh0aWR5cilcbmxpYnJhcnkodGliYmxlKVxubGlicmFyeShrYWJsZUV4dHJhKVxuYGBgIn0= --> <pre class="r"><code>library(tidyr) library(tibble) library(kableExtra)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiXG5BdHRhY2hpbmcgcGFja2FnZTog4oCYa2FibGVFeHRyYeKAmVxuXG5UaGUgZm9sbG93aW5nIG9iamVjdCBpcyBtYXNrZWQgZnJvbSDigJhwYWNrYWdlOmRwbHly4oCZOlxuXG4gICAgZ3JvdXBfcm93c1xuIn0= --> <pre><code> Attaching package: ‘kableExtra’ The following object is masked from ‘package:dplyr’: group_rows</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGlicmFyeShyb2J1bWV0YSlcbmxpYnJhcnkoZ2dwdWJyKVxuYGBgIn0= --> <pre class="r"><code>library(robumeta) library(ggpubr)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiTG9hZGluZyByZXF1aXJlZCBwYWNrYWdlOiBtYWdyaXR0clxuXG5BdHRhY2hpbmcgcGFja2FnZTog4oCYbWFncml0dHLigJlcblxuVGhlIGZvbGxvd2luZyBvYmplY3QgaXMgbWFza2VkIGZyb20g4oCYcGFja2FnZTp0aWR5cuKAmTpcblxuICAgIGV4dHJhY3RcblxuVGhlIGZvbGxvd2luZyBvYmplY3QgaXMgbWFza2VkIGZyb20g4oCYcGFja2FnZTpwdXJycuKAmTpcblxuICAgIHNldF9uYW1lc1xuIn0= --> <pre><code>Loading required package: magrittr Attaching package: ‘magrittr’ The following object is masked from ‘package:tidyr’: extract The following object is masked from ‘package:purrr’: set_names</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGlicmFyeShnZ3Bsb3QyKVxubGlicmFyeShoZXJlKVxuYGBgIn0= --> <pre class="r"><code>library(ggplot2) library(here)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiaGVyZSgpIHN0YXJ0cyBhdCAvVXNlcnMvc3ovc3VzaS9nYXJ2YW4vR2l0aHViL0lNUEMgc2V4RGlmZnMvbWljZV9zZXhfZGlmZl9zeWRcbiJ9 --> <pre><code>here() starts at /Users/sz/susi/garvan/Github/IMPC sexDiffs/mice_sex_diff_syd</code></pre> <!-- rnb-output-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="functions" class="section level2"> <h2>Functions</h2> <p>for preparing the data for meta analyses</p> <div id="subsetting-data" class="section level3"> <h3>1) Subsetting data</h3> <p>Create function for sub-setting the data to choose only one data point per individual per trait: “data_subset_parameterid_individual_by_age”</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>data_subset_parameterid_individual_by_age <- function(mydata, parameter, age_min=0, age_center=100) { tmp <- mydata %>% filter( age_in_days >= age_min, id == parameter ) %>% # take results for single individual closest to age_center mutate(age_diff = abs(age_center - age_in_days)) %>% group_by(biological_sample_id) %>% filter(age_diff == min(age_diff)) %>% select(-age_diff)# %>% # filter(!duplicated(biological_sample_id)) #Felix 6/2/2020: this line can be deleted # still some individuals with multiple records (because same individual appear under different procedures, so filter to one record) j <- match(unique(tmp$biological_sample_id), tmp$biological_sample_id) tmp[j, ] }</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="population-statistics" class="section level3"> <h3>2) “Population statistics”</h3> <p>Create function called: “calculate_population_stats” This function groups animals from the same strain and same insitiution together. This is done for each trait seoarately, and only for traits that have been measured in both sexes. Any group containing fewer than 5 individuals is excluded.</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>calculate_population_stats <- function(mydata, min_individuals = 5) { mydata %>% group_by(population, strain_name, production_center, sex) %>% summarise( trait = parameter_name[1], x_bar = mean(data_point), x_sd = sd(data_point), n_ind = n() ) %>% ungroup() %>% filter(n_ind > min_individuals) %>% # Check both sexes present & filter those missing group_by(population) %>% mutate( n_sex = n_distinct(sex) ) %>% ungroup() %>% filter(n_sex == 2) %>% select(-n_sex) %>% arrange(production_center, strain_name, population, sex) }</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="extraction-of-effect-sizes-and-sample-variances" class="section level3"> <h3>3) Extraction of effect sizes and sample variances</h3> <p>Function: “create_meta_analysis_effect_sizes”</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>create_meta_analysis_effect_sizes <- function(mydata) { i <- seq(1, nrow(mydata), by = 2) input <- data.frame( n1i = mydata$n_ind[i], n2i = mydata$n_ind[i + 1], x1i = mydata$x_bar[i], x2i = mydata$x_bar[i + 1], sd1i = mydata$x_sd[i], sd2i = mydata$x_sd[i + 1] ) mydata[i, ] %>% select(strain_name, production_center, trait) %>% mutate( effect_size_CVR = calculate_lnCVR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i), sample_variance_CVR = calculate_var_lnCVR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i), effect_size_VR = calculate_lnVR(CSD = input$sd1i, CN = input$n1i, ESD = input$sd2i, EN = input$n2i), sample_variance_VR = calculate_var_lnVR(CN = input$n1i, EN = input$n2i), effect_size_RR = calculate_lnRR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i), sample_variance_RR = calculate_var_lnRR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i), err = as.factor(seq_len(n())) ) }</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="calculate-meta-analysis-statistics" class="section level3"> <h3>4) Calculate meta-analysis statistics</h3> <p>Based on a function created by A M Senior @ the University of Otago NZ 03/01/2014:</p> <ul> <li>Calculates effect sizes for meta-analysis of variance. All functions take the mean, sd and n from the control and experimental groups.</li> <li>The first function, calculate_lnCVR, calculates the the log response-ratio of the coefficient of variance (lnCVR) - see Nakagawa et al 2015.</li> <li>The second function calculates the measurement error variance for lnCVR. As well as the aforementioned parameters, this function also takes Equal_E_C_Corr (default = T), which must be True or False. If true, the function assumes that the correlation between mean and sd (Taylor’s Law) is equal for the mean and control groups, and, thus these data are pooled. If False the mean-SD correlation for the experimental and control groups are calculated separately from one another.</li> <li>Similar functions are then implemented for lnVR (for comparison of standard deviations) and ln RR (for comparison of means)</li> </ul> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code> calculate_lnCVR <- function(CMean, CSD, CN, EMean, ESD, EN) { log(ESD) - log(EMean) + 1 / (2 * (EN - 1)) - (log(CSD) - log(CMean) + 1 / (2 * (CN - 1))) } calculate_var_lnCVR <- function(CMean, CSD, CN, EMean, ESD, EN, Equal_E_C_Corr = T) { if (Equal_E_C_Corr == T) { mvcorr <- 0 # cor.test(log(c(CMean, EMean)), log(c(CSD, ESD)))$estimate old, slightly incorrect S2 <- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * mvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * mvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1)))) } else { Cmvcorr <- cor.test(log(CMean), log(CSD))$estimate Emvcorr <- cor.test(log(EMean), (ESD))$estimate S2 <- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * Cmvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * Emvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1)))) } S2 } calculate_lnVR <- function(CSD, CN, ESD, EN) { log(ESD) - log(CSD) + 1 / (2 * (EN - 1)) - 1 / (2 * (CN - 1)) } calculate_var_lnVR <- function(CN, EN) { 1 / (2 * (EN - 1)) + 1 / (2 * (CN - 1)) } calculate_lnRR <- function(CMean, CSD, CN, EMean, ESD, EN) { log(EMean) - log(CMean) } calculate_var_lnRR <- function(CMean, CSD, CN, EMean, ESD, EN) { CSD^2 / (CN * CMean^2) + ESD^2 / (EN * EMean^2) }</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> <div id="load-clean-data" class="section level2"> <h2>Load & clean data</h2> <div id="data-loading-and-cleaning-of-the-csv-file" class="section level3"> <h3>1) Data loading and cleaning of the csv file</h3> <p>This step we have already done and provide a cleaned up file which is less computing intensive and which we have saved in a folder called <code>export</code>. However, the cvs is provided in case this is preferred to be attempted, following the steps below:</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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--> <pre class="r"><code># loads the raw data, setting some default types for various columns load_raw <- function(filename) { read_csv(filename, col_types = cols( .default = col_character(), project_id = col_character(), id = col_character(), parameter_id = col_character(), age_in_days = col_integer(), date_of_experiment = col_datetime(format = ""), weight = col_double(), phenotyping_center_id = col_character(), production_center_id = col_character(), weight_date = col_datetime(format = ""), date_of_birth = col_datetime(format = ""), procedure_id = col_character(), pipeline_id = col_character(), biological_sample_id = col_character(), biological_model_id = col_character(), weight_days_old = col_integer(), datasource_id = col_character(), experiment_id = col_character(), data_point = col_double(), age_in_weeks = col_integer(), `_version_` = col_character() ) ) } # Apply some standard cleaning to the data clean_raw_data <- function(mydata) { group <- read_csv(here("data", "ParameterGrouping.csv")) tmp <- mydata %>% # Filter to IMPC source (recommend by Jeremey in email to Susi on 20 Aug 2018) filter(datasource_name == "IMPC") %>% # standardise trait names mutate(parameter_name = tolower(parameter_name)) %>% # remove extreme ages filter(age_in_days > 0 & age_in_days < 500) %>% # remove NAs filter(!is.na(data_point)) %>% # subset to reasonable set of variables, date_of_experiment used as an indicator of batch-level effects select(production_center, strain_name, strain_accession_id, biological_sample_id, pipeline_stable_id, procedure_group, procedure_name, sex, date_of_experiment, age_in_days, weight, parameter_name, data_point) %>% # sort arrange(production_center, biological_sample_id, age_in_days) # filter to groups with > 1 centre merge(tmp, tmp %>% group_by(parameter_name) %>% summarise(center_per_trait = length(unique(production_center, na.rm = TRUE))) )%>% filter(center_per_trait >= 2) %>% # Define population variable mutate(population = sprintf("%s-%s", production_center, strain_name)) %>% # add grouping variable: these were decided based on functional groups and procedures mutate(parameter_group = group$parameter[match(parameter_name, group$parameter_name)] ) %>% # Assign unique IDs (per trait) # each unique parameter_name (=trait,use trait variable) gets a unique number ('id') # We add a new variable, where redundant traits are combined #[note however, at this stage the dataset still contains nonsensical traits, i.e. traits that may not contain any information on variance] mutate(id = match(parameter_name, unique(parameter_name))) %>% as_tibble() } # Load raw data - save cleaned dataset as RDS for reuse data_raw <- load_raw(here("data","dr7.0_all_control_data.csv.gz")) dir.create("export", F, F) data <- data_raw %>% clean_raw_data() saveRDS(data, "export/data_clean.rds")</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>For analysis we load the RDS created above and other datasets:</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuZGF0YSA8LSByZWFkUkRTKGhlcmUoXCJleHBvcnRcIiwgXCJkYXRhX2NsZWFuLnJkc1wiKSkgXG5cbnByb2NlZHVyZXMgPC0gcmVhZF9jc3YoaGVyZShcImRhdGFcIiwgXCJwcm9jZWR1cmVzLmNzdlwiKSlcbmBgYCJ9 --> <pre class="r"><code>data <- readRDS(here("export", "data_clean.rds")) procedures <- read_csv(here("data", "procedures.csv"))</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiUGFyc2VkIHdpdGggY29sdW1uIHNwZWNpZmljYXRpb246XG5jb2xzKFxuICBwcm9jZWR1cmUgPSBcdTAwMWJbMzFtY29sX2NoYXJhY3RlcigpXHUwMDFiWzM5bSxcbiAgR3JvdXBpbmdUZXJtID0gXHUwMDFiWzMxbWNvbF9jaGFyYWN0ZXIoKVx1MDAxYlszOW1cbilcbiJ9 --> <pre><code>Parsed with column specification: cols( procedure = [31mcol_character()[39m, GroupingTerm = [31mcol_character()[39m )</code></pre> <!-- rnb-output-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Checking length of different variables and sample sizes.</p> </div> <div id="table-1-strains-and-center-sample-sizes" class="section level3"> <h3>Table 1: “Strains and Center Sample Sizes”</h3> <p>This table summarises the available numbers of male and female mice from each strain and originating institution.</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-frame-begin 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--> <div style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:200px; overflow-x: scroll; width:70%; "><table class="table" style="margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> production_center </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> strain_name </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> sex </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> n </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 653 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 639 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6N;C57BL/6NTac </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 47 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6N;C57BL/6NTac </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 52 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 4 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6NJ </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 6 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6NJ </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 6 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 5 </td> </tr> <tr> <td style="text-align:left;"> HMGU </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 313 </td> </tr> <tr> <td style="text-align:left;"> HMGU </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 311 </td> </tr> <tr> <td style="text-align:left;"> HMGU </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 1045 </td> </tr> <tr> <td style="text-align:left;"> HMGU </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 1062 </td> </tr> <tr> <td style="text-align:left;"> ICS </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 1025 </td> </tr> <tr> <td style="text-align:left;"> ICS </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 1050 </td> </tr> <tr> <td style="text-align:left;"> JAX </td> <td style="text-align:left;"> C57BL/6NJ </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 2025 </td> </tr> <tr> <td style="text-align:left;"> JAX </td> <td style="text-align:left;"> C57BL/6NJ </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 2022 </td> </tr> <tr> <td style="text-align:left;"> KMPC </td> <td style="text-align:left;"> C57BL/6N;C57BL/6NTac </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 271 </td> </tr> <tr> <td style="text-align:left;"> KMPC </td> <td style="text-align:left;"> C57BL/6N;C57BL/6NTac </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 266 </td> </tr> <tr> <td style="text-align:left;"> MARC </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 936 </td> </tr> <tr> <td style="text-align:left;"> MARC </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 926 </td> </tr> <tr> <td style="text-align:left;"> MRC Harwell </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 2639 </td> </tr> <tr> <td style="text-align:left;"> MRC Harwell </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 2661 </td> </tr> <tr> <td style="text-align:left;"> MRC Harwell </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> no_data </td> <td style="text-align:right;"> 3 </td> </tr> <tr> <td style="text-align:left;"> RBRC </td> <td style="text-align:left;"> C57BL/6NJcl </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 222 </td> </tr> <tr> <td style="text-align:left;"> RBRC </td> <td style="text-align:left;"> C57BL/6NJcl </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 222 </td> </tr> <tr> <td style="text-align:left;"> RBRC </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 526 </td> </tr> <tr> <td style="text-align:left;"> RBRC </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 523 </td> </tr> <tr> <td style="text-align:left;"> TCP </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 552 </td> </tr> <tr> <td style="text-align:left;"> TCP </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 524 </td> </tr> <tr> <td style="text-align:left;"> TCP </td> <td style="text-align:left;"> C57BL6/NCrl </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> TCP </td> <td style="text-align:left;"> C57BL6/NCrl </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> UC Davis </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> UC Davis </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 1155 </td> </tr> <tr> <td style="text-align:left;"> UC Davis </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 1158 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> B6Brd;B6Dnk;B6N-Tyr<c-Brd> </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 97 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> B6Brd;B6Dnk;B6N-Tyr<c-Brd> </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 87 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> C57BL/6J-Tyr<c-Brd> or C57BL/6NTac/USA </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 3 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 1951 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 2008 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> C57BL/6N;C57BL/6NTac </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 41 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> C57BL/6N;C57BL/6NTac </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 7 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 13 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 49 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 34 </td> </tr> </tbody> </table></div> <!-- rnb-htmlwidget-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> </div> <div id="meta-analyses" class="section level1"> <h1>Meta-analyses</h1> <div id="population-as-analysis-unit" class="section level2"> <h2>1. Population as analysis unit</h2> <p>(Step C, Figure 3 in main document)</p> <div id="loop-meta-analyses-on-all-traits" class="section level3"> <h3>Loop: Meta-analyses on all traits</h3> <ul> <li>The loop combines the functions mentioned above and fills the data matrix with results from our meta analysis.</li> <li>Error messages indicate traits that either did not reach convergence, or that did not return meaningful results in the meta-analysis, due to absence of variance. Those traits will be removed in later steps, outlined below.</li> </ul> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code> n <- length(unique(data$id)) # Create dataframe to store results results_alltraits_grouping <- tibble(id = 1:n, lnCVR=0, lnCVR_lower=0, lnCVR_upper=0, lnCVR_se=0, lnVR=0, lnVR_lower=0, lnVR_upper=0, lnVR_se=0, lnRR=0, lnRR_lower=0, lnRR_upper=0, lnRR_se=0, sampleSize=0, trait=0) for (t in 1:n) { tryCatch( { results <- data %>% data_subset_parameterid_individual_by_age(t) %>% calculate_population_stats() %>% create_meta_analysis_effect_sizes() # lnCVR, log repsonse-ratio of the coefficient of variance cvr <- metafor::rma.mv(yi = effect_size_CVR, V = sample_variance_CVR, random = list(~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F, data = results) # lnVR, comparison of standard deviations cv <- metafor::rma.mv(yi = effect_size_VR, V = sample_variance_VR, random = list(~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F, data = results) # for means, lnRR means <- metafor::rma.mv(yi = effect_size_RR, V = sample_variance_RR, random = list(~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F, data = results) f <- function(x) unlist(x[c("b", "ci.lb", "ci.ub", "se")]) results_alltraits_grouping[t, 2:14] <- c(f(cvr), f(cv), f(means), means$k) results_alltraits_grouping[t, 15] <- unique(results$trait) }, error = function(e) { cat("ERROR :", t, conditionMessage(e), "\n") } ) }</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiA4NCBPcHRpbWl6ZXIgKG9wdGltKSBkaWQgbm90IGFjaGlldmUgY29udmVyZ2VuY2UgKGNvbnZlcmdlbmNlID0gMTApLiBcbiJ9 --> <pre><code>ERROR : 84 Optimizer (optim) did not achieve convergence (convergence = 10). </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiU2luZ2xlLWxldmVsIGZhY3RvcihzKSBmb3VuZCBpbiAncmFuZG9tJyBhcmd1bWVudC4gQ29ycmVzcG9uZGluZyAnc2lnbWEyJyB2YWx1ZShzKSBmaXhlZCB0byAwLlNpbmdsZS1sZXZlbCBmYWN0b3IocykgZm91bmQgaW4gJ3JhbmRvbScgYXJndW1lbnQuIENvcnJlc3BvbmRpbmcgJ3NpZ21hMicgdmFsdWUocykgZml4ZWQgdG8gMC5TaW5nbGUtbGV2ZWwgZmFjdG9yKHMpIGZvdW5kIGluICdyYW5kb20nIGFyZ3VtZW50LiBDb3JyZXNwb25kaW5nICdzaWdtYTInIHZhbHVlKHMpIGZpeGVkIHRvIDAuU2luZ2xlLWxldmVsIGZhY3RvcihzKSBmb3VuZCBpbiAncmFuZG9tJyBhcmd1bWVudC4gQ29ycmVzcG9uZGluZyAnc2lnbWEyJyB2YWx1ZShzKSBmaXhlZCB0byAwLlNpbmdsZS1sZXZlbCBmYWN0b3IocykgZm91bmQgaW4gJ3JhbmRvbScgYXJndW1lbnQuIENvcnJlc3BvbmRpbmcgJ3NpZ21hMicgdmFsdWUocykgZml4ZWQgdG8gMC5TaW5nbGUtbGV2ZWwgZmFjdG9yKHMpIGZvdW5kIGluICdyYW5kb20nIGFyZ3VtZW50LiBDb3JyZXNwb25kaW5nICdzaWdtYTInIHZhbHVlKHMpIGZpeGVkIHRvIDAuU2luZ2xlLWxldmVsIGZhY3RvcihzKSBmb3VuZCBpbiAncmFuZG9tJyBhcmd1bWVudC4gQ29ycmVzcG9uZGluZyAnc2lnbWEyJyB2YWx1ZShzKSBmaXhlZCB0byAwLlNpbmdsZS1sZXZlbCBmYWN0b3IocykgZm91bmQgaW4gJ3JhbmRvbScgYXJndW1lbnQuIENvcnJlc3BvbmRpbmcgJ3NpZ21hMicgdmFsdWUocykgZml4ZWQgdG8gMC5TaW5nbGUtbGV2ZWwgZmFjdG9yKHMpIGZvdW5kIGluICdyYW5kb20nIGFyZ3VtZW50LiBDb3JyZXNwb25kaW5nICdzaWdtYTInIHZhbHVlKHMpIGZpeGVkIHRvIDAuU2luZ2xlLWxldmVsIGZhY3RvcihzKSBmb3VuZCBpbiAncmFuZG9tJyBhcmd1bWVudC4gQ29ycmVzcG9uZGluZyAnc2lnbWEyJyB2YWx1ZShzKSBmaXhlZCB0byAwLlNpbmdsZS1sZXZlbCBmYWN0b3IocykgZm91bmQgaW4gJ3JhbmRvbScgYXJndW1lbnQuIENvcnJlc3BvbmRpbmcgJ3NpZ21hMicgdmFsdWUocykgZml4ZWQgdG8gMC5TaW5nbGUtbGV2ZWwgZmFjdG9yKHMpIGZvdW5kIGluICdyYW5kb20nIGFyZ3VtZW50LiBDb3JyZXNwb25kaW5nICdzaWdtYTInIHZhbHVlKHMpIGZpeGVkIHRvIDAuU2luZ2xlLWxldmVsIGZhY3RvcihzKSBmb3VuZCBpbiAncmFuZG9tJyBhcmd1bWVudC4gQ29ycmVzcG9uZGluZyAnc2lnbWEyJyB2YWx1ZShzKSBmaXhlZCB0byAwLlNpbmdsZS1sZXZlbCBmYWN0b3IocykgZm91bmQgaW4gJ3JhbmRvbScgYXJndW1lbnQuIENvcnJlc3BvbmRpbmcgJ3NpZ21hMicgdmFsdWUocykgZml4ZWQgdG8gMC5TaW5nbGUtbGV2ZWwgZmFjdG9yKHMpIGZvdW5kIGluICdyYW5kb20nIGFyZ3VtZW50LiBDb3JyZXNwb25kaW5nICdzaWdtYTInIHZhbHVlKHMpIGZpeGVkIHRvIDAuUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5Sb3dzIHdpdGggTkFzIG9taXR0ZWQgZnJvbSBtb2RlbCBmaXR0aW5nLlJvd3Mgd2l0aCBOQXMgb21pdHRlZCBmcm9tIG1vZGVsIGZpdHRpbmcuUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5Sb3dzIHdpdGggTkFzIG9taXR0ZWQgZnJvbSBtb2RlbCBmaXR0aW5nLlRoZXJlIGFyZSBvdXRjb21lcyB3aXRoIG5vbi1wb3NpdGl2ZSBzYW1wbGluZyB2YXJpYW5jZXMuJ1YnIGFwcGVhcnMgdG8gYmUgbm90IHBvc2l0aXZlIGRlZmluaXRlLlxuIn0= --> <pre><code>Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.There are outcomes with non-positive sampling variances.'V' appears to be not positive definite.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAxNTggT3B0aW1pemVyIChvcHRpbSkgZGlkIG5vdCBhY2hpZXZlIGNvbnZlcmdlbmNlIChjb252ZXJnZW5jZSA9IDEwKS4gXG4ifQ== --> <pre><code>ERROR : 158 Optimizer (optim) did not achieve convergence (convergence = 10). </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAxNjAgTkEvTmFOL0luZiBpbiAneScgXG4ifQ== --> <pre><code>ERROR : 160 NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAxNjEgTkEvTmFOL0luZiBpbiAneScgXG4ifQ== --> <pre><code>ERROR : 161 NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAxNjIgTkEvTmFOL0luZiBpbiAneScgXG4ifQ== --> <pre><code>ERROR : 162 NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAxNjMgTkEvTmFOL0luZiBpbiAneScgXG4ifQ== --> <pre><code>ERROR : 163 NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAxNjUgTkEvTmFOL0luZiBpbiAneScgXG4ifQ== --> <pre><code>ERROR : 165 NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAxNjYgTkEvTmFOL0luZiBpbiAneScgXG4ifQ== --> <pre><code>ERROR : 166 NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5Sb3dzIHdpdGggTkFzIG9taXR0ZWQgZnJvbSBtb2RlbCBmaXR0aW5nLlRoZXJlIGFyZSBvdXRjb21lcyB3aXRoIG5vbi1wb3NpdGl2ZSBzYW1wbGluZyB2YXJpYW5jZXMuJ1YnIGFwcGVhcnMgdG8gYmUgbm90IHBvc2l0aXZlIGRlZmluaXRlLlJvd3Mgd2l0aCBOQXMgb21pdHRlZCBmcm9tIG1vZGVsIGZpdHRpbmcuXG4ifQ== --> <pre><code>Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.There are outcomes with non-positive sampling variances.'V' appears to be not positive definite.Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAxNjggTkEvTmFOL0luZiBpbiAneScgXG4ifQ== --> <pre><code>ERROR : 168 NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin 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 --> <pre><code>Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.There are outcomes with non-positive sampling variances.'V' appears to be not positive definite.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.There are outcomes with non-positive sampling variances.'V' appears to be not positive definite.Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAyMzEgTkEvTmFOL0luZiBpbiAneScgXG4ifQ== --> <pre><code>ERROR : 231 NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>In the above function, we use ‘tryCatch’ and ‘conditionMessage’ to prevent the loop from aborting when the first error at row 84 is produced. As convergence in the two listed non-converging cases can’t be achieved by sensibly tweaking (other optim etc.), and we only learn about non-convergence in the loop, it is not possible to exclude the traits (N=2) beforehand. Similarly, there are 8 traits with very low variation, which can not be excluded prior to running the loop.</p> <p>The produced “Warnings” indicate cases where variance components are set to zero during likelihood optimization.</p> </div> <div id="merging-datasets-removal-of-non-converged-traits" class="section level3"> <h3>Merging datasets & removal of non-converged traits</h3> <p>Procedure names, grouping variables and trait names (“parameter_names”) are merged back together with the results from the metafor analysis above.</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>results_alltraits_grouping2 <- results_alltraits_grouping %>% left_join(by="id", data %>% select(id, parameter_group, procedure = procedure_name, procedure_name, parameter_name) %>% # We filter duplicated id's to get only one unique row per id (and there is one id per parameter_name) filter(!duplicated(id)) ) %>% # Below we add 'procedure' (from the previously loaded 'procedures.csv') as a variable left_join(by="procedure", procedures %>% distinct() ) #(n <- length(unique(results_alltraits_grouping2$parameter_name))) # 232</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="removal-of-traits" class="section level3"> <h3>Removal of traits</h3> <p>14 traits from the originally 232 that had been included are removed because they either did not achieve convergence or are nonsensical for analysis of variance (such as traits that show no variation, see list below).</p> <p>Not converged: “dp t cells”, “mzb (cd21/35 high)”</p> <p>Not enough variation: “number of caudal vertebrae”, “number of cervical vertebrae”, “number of digits”, “number of lumbar vertebrae”, “number of pelvic vertebrae”, “number of ribs left”,“number of ribs right”, “number of signals”, “number of thoracic vertebrae”, “total number of acquired events in panel a”,“total number of acquired events in panel b”, “whole arena permanence”.</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code># We exclude 14 parameter names for which metafor models didn't converge ("dp t cells", "mzb (cd21/35 high)"), and of parameters that don't harbour enough variation meta_clean <- results_alltraits_grouping2 %>% filter(!parameter_name %in% c("dp t cells", "mzb (cd21/35 high)", "number of caudal vertebrae", "number of cervical vertebrae", "number of digits", "number of lumbar vertebrae", "number of pelvic vertebrae", "number of ribs left", "number of ribs right", "number of signals", "number of thoracic vertebrae", "total number of acquired events in panel a", "total number of acquired events in panel b", "whole arena permanence")) </code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> <div id="meta-analysis-condensing-non-independent-traits" class="section level2"> <h2>2. Meta-analysis: condensing non-independent traits</h2> <p>(Step F in Figure 3 in main article)</p> <div id="dealing-with-correlated-parameters-preparation" class="section level3"> <h3>Dealing with Correlated Parameters, preparation</h3> <p>This dataset contained a number of highly correlated traits, such as different kinds of cell counts (for example hierarchical parameterization within immunological assays). As those data-points are not independent of each other, we conducted meta analyses on these correlated parameters to collapse the number of levels.</p> <div id="collapsing-and-merging-correlated-parameters" class="section level4"> <h4>Collapsing and merging correlated parameters</h4> <p>Here we double check numbers of trait parameters in the dataset</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuXG5tZXRhMSA8LSBtZXRhX2NsZWFuIFxubGVuZ3RoKHVuaXF1ZShtZXRhMSRwcm9jZWR1cmUpKSAjMThcbmBgYCJ9 --> <pre class="r"><code> meta1 <- meta_clean length(unique(meta1$procedure)) #18</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiWzFdIDE4XG4ifQ== --> <pre><code>[1] 18</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGVuZ3RoKHVuaXF1ZShtZXRhMSRHcm91cGluZ1Rlcm0pKSAjOVxuYGBgIn0= --> <pre class="r"><code>length(unique(meta1$GroupingTerm)) #9</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiWzFdIDlcbiJ9 --> <pre><code>[1] 9</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGVuZ3RoKHVuaXF1ZShtZXRhMSRwYXJhbWV0ZXJfZ3JvdXApKSAjIDE0OCBsZXZlbHMuIFRvIGJlIHVzZWQgYXMgZ3JvdXBpbmcgZmFjdG9yIGZvciBtZXRhLW1ldGEtYW5hbHlzaXMgLyBjb2xsYXBzaW5nIGRvd24gYmFzZWQgb24gdGhpbmdzIHRoYXQgYXJlIGNsYXNzaWZpZWQgaWRlbnRpY2FsbHkgaW4gXCJwYXJhbWV0ZXJfZ3JvdXBcIiBidXQgaGF2ZSBkaWZmZXJlbnQgXCJwYXJhbWV0ZXJfbmFtZVwiXG5gYGAifQ== --> <pre class="r"><code>length(unique(meta1$parameter_group)) # 148 levels. To be used as grouping factor for meta-meta-analysis / collapsing down based on things that are classified identically in "parameter_group" but have different "parameter_name"</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiWzFdIDE0OFxuIn0= --> <pre><code>[1] 148</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGVuZ3RoKHVuaXF1ZShtZXRhMSRwYXJhbWV0ZXJfbmFtZSkpICMyMThcbmBgYCJ9 --> <pre class="r"><code>length(unique(meta1$parameter_name)) #218</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiWzFdIDIxOFxuIn0= --> <pre><code>[1] 218</code></pre> <!-- rnb-output-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="count-of-number-of-parameter-names-correlated-sub-traits-in-each-parameter-group-par_group_size" class="section level4"> <h4>Count of number of parameter names (correlated sub-traits) in each parameter group (par_group_size)</h4> </div> </div> <div id="table-2-numbers-of-correlated-and-uncorrelated-traits" class="section level3"> <h3>Table 2: Numbers of correlated and uncorrelated traits</h3> <p>This serves to identify and separate the traits that are correlated from the full dataset that can be processed as is. If the sample size (n) for a given “parameter group” equals 1, the trait is unique and uncorrelated. All instances, where there are 2 or more traits associated with the same parameter group (90 cases), are selected for a “mini-meta analysis”, which removes the issue of correlation.</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxua2FibGUoY2JpbmQobWV0YTEgJT4lIGNvdW50KHBhcmFtZXRlcl9ncm91cCkpKSAlPiVcbiAga2FibGVfc3R5bGluZygpICU+JVxuICBzY3JvbGxfYm94KHdpZHRoID0gXCI2MCVcIiwgaGVpZ2h0ID0gXCIyMDBweFwiKVxuYGBgIn0= --> <pre class="r"><code>kable(cbind(meta1 %>% count(parameter_group))) %>% kable_styling() %>% scroll_box(width = "60%", height = "200px")</code></pre> <!-- rnb-source-end --> <!-- rnb-htmlwidget-begin 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 --> <div style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:200px; overflow-x: scroll; width:60%; "><table class="table" style="margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter_group </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> n </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> 12khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> 18khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> 24khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> 30khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> 6khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> alanine aminotransferase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> albumin </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> alkaline phosphatase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> alpha-amylase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> area under glucose response curve </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> aspartate aminotransferase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> B cells </td> <td style="text-align:right;"> 4 </td> </tr> <tr> <td style="text-align:left;"> basophil cell count </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> basophil differential count </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> bmc/body weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> body length </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> body temp </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> body weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> body weight after experiment </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> body weight before experiment </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> bone area </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> bone mineral content (excluding skull) </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> bone mineral density (excluding skull) </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> calcium </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> cardiac output </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> cd4 nkt </td> <td style="text-align:right;"> 6 </td> </tr> <tr> <td style="text-align:left;"> cd4 t </td> <td style="text-align:right;"> 7 </td> </tr> <tr> <td style="text-align:left;"> cd8 nkt </td> <td style="text-align:right;"> 6 </td> </tr> <tr> <td style="text-align:left;"> cd8 t </td> <td style="text-align:right;"> 7 </td> </tr> <tr> <td style="text-align:left;"> cdcs </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> center average speed </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> center distance travelled </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> center permanence time </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> center resting time </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> chloride </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> click-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> creatine kinase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> creatinine </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> cv </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> distance travelled - total </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> dn nkt </td> <td style="text-align:right;"> 6 </td> </tr> <tr> <td style="text-align:left;"> dn t </td> <td style="text-align:right;"> 7 </td> </tr> <tr> <td style="text-align:left;"> ejection fraction </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> end-diastolic diameter </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> end-systolic diameter </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> eosinophils </td> <td style="text-align:right;"> 3 </td> </tr> <tr> <td style="text-align:left;"> fasted blood glucose concentration </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> fat mass </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> fat/body weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> follicular b cells </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> forelimb and hindlimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> forelimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> fractional shortening </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> free fatty acids </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> fructosamine </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> glucose </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> hdl-cholesterol </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> heart weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> heart weight normalised against body weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> hematocrit </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> hemoglobin </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> hr </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> hrv </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> initial response to glucose challenge </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> insulin </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> iron </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lactate dehydrogenase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> latency to center entry </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> ldl-cholesterol </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lean mass </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lean/body weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left anterior chamber depth </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left corneal thickness </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left inner nuclear layer </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left outer nuclear layer </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left posterior chamber depth </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left total retinal thickness </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> locomotor activity </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> luc </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> lvawd </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lvaws </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lvidd </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lvids </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lvpwd </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lvpws </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lymphocytes </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> magnesium </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean cell hemoglobin concentration </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean cell volume </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean corpuscular hemoglobin </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean platelet volume </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean r amplitude </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean sr amplitude </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> monocytes </td> <td style="text-align:right;"> 3 </td> </tr> <tr> <td style="text-align:left;"> neutrophils </td> <td style="text-align:right;"> 3 </td> </tr> <tr> <td style="text-align:left;"> nk cells </td> <td style="text-align:right;"> 6 </td> </tr> <tr> <td style="text-align:left;"> nkt cells </td> <td style="text-align:right;"> 4 </td> </tr> <tr> <td style="text-align:left;"> number of center entries </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> number of rears - total </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> others </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> pdcs </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> percentage center time </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> percentage of live gated events </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> periphery average speed </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> periphery distance travelled </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> periphery permanence time </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> periphery resting time </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> phosphorus </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> platelet count </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> pnn5(6>ms) </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> potassium </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> pq </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> pr </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> pre-pulse inhibition </td> <td style="text-align:right;"> 5 </td> </tr> <tr> <td style="text-align:left;"> qrs </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> qtc </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> qtc dispersion </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> red blood cell count </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> red blood cell distribution width </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> respiration rate </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> respiratory exchange ratio </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> response amplitude </td> <td style="text-align:right;"> 10 </td> </tr> <tr> <td style="text-align:left;"> right anterior chamber depth </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> right corneal thickness </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> right inner nuclear layer </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> right outer nuclear layer </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> right posterior chamber depth </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> right total retinal thickness </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> rmssd </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> rp macrophage (cd19- cd11c-) </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> rr </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> sodium </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> spleen weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> st </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> stroke volume </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> t cells </td> <td style="text-align:right;"> 3 </td> </tr> <tr> <td style="text-align:left;"> tibia length </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> total bilirubin </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> total cholesterol </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> total food intake </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> total protein </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> total water intake </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> triglycerides </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> urea (blood urea nitrogen - bun) </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> uric acid </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> white blood cell count </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> whole arena average speed </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> whole arena resting time </td> <td style="text-align:right;"> 1 </td> </tr> </tbody> </table></div> <!-- rnb-htmlwidget-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>meta1_sub <- meta1 %>% # Add summary of number of parameter names in each parameter group group_by(parameter_group) %>% mutate(par_group_size = length(unique(parameter_name)), sampleSize = as.numeric(sampleSize)) %>% ungroup() %>% # Create subsets with > 1 count (par_group_size > 1) filter(par_group_size > 1) # 90 observations</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <div id="meta-analyses-on-correlated-sub-traits-using-robumeta" class="section level4"> <h4>Meta-analyses on correlated (sub-)traits, using robumeta`</h4> <p>Here we pepare the subset of the data (using nest()), and in this first step the model of the meta-analysis effect sizes are calculated</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code> # Create summary of number of parameter names in each parameter group, and merge back together meta1b <- meta1 %>% group_by(parameter_group) %>% summarize(par_group_size = length(unique(parameter_name, na.rm = TRUE))) meta1$par_group_size <- meta1b$par_group_size[match(meta1$parameter_group, meta1b$parameter_group)] # Create subsets with > 1 count (par_group_size > 1) meta1_sub <- subset(meta1,par_group_size >1) # 90 observations meta1_sub$sampleSize <- as.numeric(meta1_sub$sampleSize) # Nesting and meta-analyses on correlated traits, using robumeta n_count <- meta1_sub %>% group_by(parameter_group) %>% mutate(raw_N = sum(sampleSize)) %>% nest() %>% ungroup() model_count <- n_count %>% mutate( model_lnRR = map(data, ~ robu(.x$lnRR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnRR_se)^2)), model_lnVR = map(data, ~ robu(.x$lnVR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnVR_se)^2)), model_lnCVR = map(data, ~ robu(.x$lnCVR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnCVR_se)^2)) )</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="extracting-and-save-parameter-estimates" class="section level4"> <h4>Extracting and save parameter estimates</h4> <p>Here we apply an additional Function to collect the outcomes of the ‘mini-meta-analysis’ that has ondensed our non-independent traits.</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuY291bnRfZnVuIDwtIGZ1bmN0aW9uKG1vZF9zdWIpIHtcbiAgcmV0dXJuKGMobW9kX3N1YiRyZWdfdGFibGUkYi5yLCBtb2Rfc3ViJHJlZ190YWJsZSRDSS5MLCBtb2Rfc3ViJHJlZ190YWJsZSRDSS5VLCBtb2Rfc3ViJHJlZ190YWJsZSRTRSkpXG59ICMgZXN0aW1hdGUsIGxvd2VyIGNpLCB1cHBlciBjaSwgU0VcbmBgYCJ9 --> <pre class="r"><code>count_fun <- function(mod_sub) { return(c(mod_sub$reg_table$b.r, mod_sub$reg_table$CI.L, mod_sub$reg_table$CI.U, mod_sub$reg_table$SE)) } # estimate, lower ci, upper ci, SE</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Extraction of values created during meta-analyses using robumeta</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>robusub_RR <- model_count %>% transmute(parameter_group, estimatelnRR = map(model_lnRR, count_fun)) %>% mutate(r = map(estimatelnRR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnRR) %>% purrr::set_names(c("parameter_group", "lnRR", "lnRR_lower", "lnRR_upper", "lnRR_se")) robusub_CVR <- model_count %>% transmute(parameter_group, estimatelnCVR = map(model_lnCVR, count_fun)) %>% mutate(r = map(estimatelnCVR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnCVR) %>% purrr::set_names(c("parameter_group", "lnCVR", "lnCVR_lower", "lnCVR_upper", "lnCVR_se")) robusub_VR <- model_count %>% transmute(parameter_group, estimatelnVR = map(model_lnVR, count_fun)) %>% mutate(r = map(estimatelnVR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnVR) %>% purrr::set_names(c("parameter_group", "lnVR", "lnVR_lower", "lnVR_upper", "lnVR_se")) robu_all <- full_join(robusub_CVR, robusub_VR) %>% full_join(., robusub_RR)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiSm9pbmluZywgYnkgPSBcInBhcmFtZXRlcl9ncm91cFwiXG5Kb2luaW5nLCBieSA9IFwicGFyYW1ldGVyX2dyb3VwXCJcbiJ9 --> <pre><code>Joining, by = "parameter_group" Joining, by = "parameter_group"</code></pre> <!-- rnb-output-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="combining-data" class="section level4"> <h4>Combining data</h4> <p>Merge the two data sets (the new [robu_all] and the initial [uncorrelated sub-traits with count = 1])</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>meta_all <- meta1 %>% filter(par_group_size == 1) %>% as_tibble() # glimpse(meta_all) # glimpse(robu_all) # Step 1: Columns are matched by name (in our case, 'parameter_group'), and any missing columns will be filled with NA combinedmeta <- bind_rows(robu_all, meta_all) # glimpse(combinedmeta) # Steps 2&3: Add information about number of traits in a parameter group, procedure, and grouping term metacombo <- combinedmeta metacombo$counts <- meta1$par_group_size[match(metacombo$parameter_group, meta1$parameter_group)] metacombo$procedure2 <- meta1$procedure[match(metacombo$parameter_group, meta1$parameter_group)] metacombo$GroupingTerm2 <- meta1$GroupingTerm[match(metacombo$parameter_group, meta1$parameter_group)] </code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Clean-up, reorder, and rename</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>metacombo <- metacombo[c("parameter_group", "counts","procedure2","GroupingTerm2", "lnCVR","lnCVR_lower","lnCVR_upper","lnCVR_se","lnVR","lnVR_lower","lnVR_upper","lnVR_se","lnRR","lnRR_lower","lnRR_upper","lnRR_se")] names(metacombo)[names(metacombo)=="procedure2"] <- "procedure" names(metacombo)[names(metacombo)=="GroupingTerm2"] <- "GroupingTerm" # Quick pre-check before doing plots metacombo %>% group_by(GroupingTerm) %>% dplyr::summarize(MeanCVR = mean(lnCVR), MeanVR = mean(lnVR), MeanRR = mean(lnRR))</code></pre> <!-- rnb-source-end --> <!-- rnb-frame-begin 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</script> </div> <!-- rnb-frame-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> <div id="table-3-full-corrected-dataset" class="section level3"> <h3>Table 3: Full corrected dataset</h3> <p>We use this corrected (for correlated traits) “results” table, which contains each of the meta-analytic means for all effect sizes of interest, for further analyses. We further use this table as part of the Shiny App, which is able to provide the percentage differences between males and females for mean, variance and coefficient of variance.</p> <p>This is the full result dataset</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxua2FibGUobWV0YWNvbWJvKSAlPiVcbiAga2FibGVfc3R5bGluZygpICU+JVxuICBzY3JvbGxfYm94KHdpZHRoID0gXCIxMDAlXCIsIGhlaWdodCA9IFwiMjAwcHhcIilcbmBgYCJ9 --> <pre class="r"><code>kable(metacombo) %>% kable_styling() %>% scroll_box(width = "100%", height = "200px")</code></pre> <!-- rnb-source-end --> <p> <!-- rnb-htmlwidget-begin 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--> <div style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:200px; overflow-x: scroll; width:100%; "><table class="table" style="margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter_group </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> counts </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> procedure </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> GroupingTerm </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_se </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> pre-pulse inhibition </td> <td style="text-align:right;"> 5 </td> <td style="text-align:left;"> Acoustic Startle and Pre-pulse Inhibition (PPI) </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0232963 </td> <td style="text-align:right;"> -0.0802563 </td> <td style="text-align:right;"> 0.1268488 </td> <td style="text-align:right;"> 0.0370507 </td> <td style="text-align:right;"> 0.0091028 </td> <td style="text-align:right;"> -0.0364640 </td> <td style="text-align:right;"> 0.0546695 </td> <td style="text-align:right;"> 0.0143431 </td> <td style="text-align:right;"> -0.0052156 </td> <td style="text-align:right;"> -0.0427126 </td> <td style="text-align:right;"> 0.0322815 </td> <td style="text-align:right;"> 0.0128092 </td> </tr> <tr> <td style="text-align:left;"> B cells </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0938959 </td> <td style="text-align:right;"> -0.2500020 </td> <td style="text-align:right;"> 0.0622103 </td> <td style="text-align:right;"> 0.0426972 </td> <td style="text-align:right;"> -0.0995337 </td> <td style="text-align:right;"> -0.2068001 </td> <td style="text-align:right;"> 0.0077328 </td> <td style="text-align:right;"> 0.0250132 </td> <td style="text-align:right;"> -0.0026281 </td> <td style="text-align:right;"> -0.1298230 </td> <td style="text-align:right;"> 0.1245668 </td> <td style="text-align:right;"> 0.0393018 </td> </tr> <tr> <td style="text-align:left;"> cd4 nkt </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0287688 </td> <td style="text-align:right;"> -0.0566987 </td> <td style="text-align:right;"> -0.0008389 </td> <td style="text-align:right;"> 0.0101634 </td> <td style="text-align:right;"> -0.2018746 </td> <td style="text-align:right;"> -0.3102294 </td> <td style="text-align:right;"> -0.0935198 </td> <td style="text-align:right;"> 0.0331161 </td> <td style="text-align:right;"> -0.2344450 </td> <td style="text-align:right;"> -0.4005266 </td> <td style="text-align:right;"> -0.0683635 </td> <td style="text-align:right;"> 0.0633501 </td> </tr> <tr> <td style="text-align:left;"> cd4 t </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1507387 </td> <td style="text-align:right;"> -0.2427976 </td> <td style="text-align:right;"> -0.0586798 </td> <td style="text-align:right;"> 0.0360690 </td> <td style="text-align:right;"> -0.1699213 </td> <td style="text-align:right;"> -0.2629450 </td> <td style="text-align:right;"> -0.0768975 </td> <td style="text-align:right;"> 0.0348324 </td> <td style="text-align:right;"> -0.0031242 </td> <td style="text-align:right;"> -0.0411564 </td> <td style="text-align:right;"> 0.0349081 </td> <td style="text-align:right;"> 0.0148989 </td> </tr> <tr> <td style="text-align:left;"> cd8 nkt </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0424402 </td> <td style="text-align:right;"> -0.0782046 </td> <td style="text-align:right;"> -0.0066759 </td> <td style="text-align:right;"> 0.0119223 </td> <td style="text-align:right;"> -0.0300442 </td> <td style="text-align:right;"> -0.1823594 </td> <td style="text-align:right;"> 0.1222710 </td> <td style="text-align:right;"> 0.0533765 </td> <td style="text-align:right;"> 0.0035372 </td> <td style="text-align:right;"> -0.0573749 </td> <td style="text-align:right;"> 0.0644494 </td> <td style="text-align:right;"> 0.0205272 </td> </tr> <tr> <td style="text-align:left;"> cd8 t </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1223681 </td> <td style="text-align:right;"> -0.2179976 </td> <td style="text-align:right;"> -0.0267387 </td> <td style="text-align:right;"> 0.0358727 </td> <td style="text-align:right;"> -0.1581698 </td> <td style="text-align:right;"> -0.2342579 </td> <td style="text-align:right;"> -0.0820816 </td> <td style="text-align:right;"> 0.0270229 </td> <td style="text-align:right;"> -0.0415806 </td> <td style="text-align:right;"> -0.0510391 </td> <td style="text-align:right;"> -0.0321221 </td> <td style="text-align:right;"> 0.0023119 </td> </tr> <tr> <td style="text-align:left;"> cdcs </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0362947 </td> <td style="text-align:right;"> -0.3588637 </td> <td style="text-align:right;"> 0.2862742 </td> <td style="text-align:right;"> 0.0253867 </td> <td style="text-align:right;"> 0.1080248 </td> <td style="text-align:right;"> -0.0565718 </td> <td style="text-align:right;"> 0.2726213 </td> <td style="text-align:right;"> 0.0129540 </td> <td style="text-align:right;"> 0.1642541 </td> <td style="text-align:right;"> -0.1701520 </td> <td style="text-align:right;"> 0.4986601 </td> <td style="text-align:right;"> 0.0263183 </td> </tr> <tr> <td style="text-align:left;"> dn nkt </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0619371 </td> <td style="text-align:right;"> -0.1359380 </td> <td style="text-align:right;"> 0.0120637 </td> <td style="text-align:right;"> 0.0257746 </td> <td style="text-align:right;"> -0.1572129 </td> <td style="text-align:right;"> -0.2814342 </td> <td style="text-align:right;"> -0.0329915 </td> <td style="text-align:right;"> 0.0447163 </td> <td style="text-align:right;"> -0.1727105 </td> <td style="text-align:right;"> -0.2906356 </td> <td style="text-align:right;"> -0.0547854 </td> <td style="text-align:right;"> 0.0441034 </td> </tr> <tr> <td style="text-align:left;"> dn t </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0796127 </td> <td style="text-align:right;"> -0.1844481 </td> <td style="text-align:right;"> 0.0252227 </td> <td style="text-align:right;"> 0.0420063 </td> <td style="text-align:right;"> -0.2421038 </td> <td style="text-align:right;"> -0.3431678 </td> <td style="text-align:right;"> -0.1410397 </td> <td style="text-align:right;"> 0.0406314 </td> <td style="text-align:right;"> -0.2298147 </td> <td style="text-align:right;"> -0.2519708 </td> <td style="text-align:right;"> -0.2076586 </td> <td style="text-align:right;"> 0.0072373 </td> </tr> <tr> <td style="text-align:left;"> eosinophils </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0662225 </td> <td style="text-align:right;"> -0.2806631 </td> <td style="text-align:right;"> 0.1482181 </td> <td style="text-align:right;"> 0.0325859 </td> <td style="text-align:right;"> -0.0154112 </td> <td style="text-align:right;"> -0.4051652 </td> <td style="text-align:right;"> 0.3743427 </td> <td style="text-align:right;"> 0.0865366 </td> <td style="text-align:right;"> -0.0042422 </td> <td style="text-align:right;"> -0.2409206 </td> <td style="text-align:right;"> 0.2324362 </td> <td style="text-align:right;"> 0.0508093 </td> </tr> <tr> <td style="text-align:left;"> follicular b cells </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1160077 </td> <td style="text-align:right;"> -0.7256692 </td> <td style="text-align:right;"> 0.4936538 </td> <td style="text-align:right;"> 0.0479814 </td> <td style="text-align:right;"> -0.1050194 </td> <td style="text-align:right;"> -0.6946364 </td> <td style="text-align:right;"> 0.4845977 </td> <td style="text-align:right;"> 0.0464039 </td> <td style="text-align:right;"> 0.0052427 </td> <td style="text-align:right;"> -0.1872381 </td> <td style="text-align:right;"> 0.1977236 </td> <td style="text-align:right;"> 0.0151486 </td> </tr> <tr> <td style="text-align:left;"> luc </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0180436 </td> <td style="text-align:right;"> -0.2038464 </td> <td style="text-align:right;"> 0.2399336 </td> <td style="text-align:right;"> 0.0174631 </td> <td style="text-align:right;"> 0.2657035 </td> <td style="text-align:right;"> -1.2251358 </td> <td style="text-align:right;"> 1.7565428 </td> <td style="text-align:right;"> 0.1173316 </td> <td style="text-align:right;"> 0.2215497 </td> <td style="text-align:right;"> -1.4136389 </td> <td style="text-align:right;"> 1.8567382 </td> <td style="text-align:right;"> 0.1286921 </td> </tr> <tr> <td style="text-align:left;"> lymphocytes </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0805230 </td> <td style="text-align:right;"> -2.2618128 </td> <td style="text-align:right;"> 2.4228588 </td> <td style="text-align:right;"> 0.1843458 </td> <td style="text-align:right;"> 0.1550159 </td> <td style="text-align:right;"> -1.0892706 </td> <td style="text-align:right;"> 1.3993024 </td> <td style="text-align:right;"> 0.0979275 </td> <td style="text-align:right;"> 0.0602144 </td> <td style="text-align:right;"> -1.0131287 </td> <td style="text-align:right;"> 1.1335576 </td> <td style="text-align:right;"> 0.0844739 </td> </tr> <tr> <td style="text-align:left;"> monocytes </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0214677 </td> <td style="text-align:right;"> -0.2033706 </td> <td style="text-align:right;"> 0.1604352 </td> <td style="text-align:right;"> 0.0420605 </td> <td style="text-align:right;"> 0.0784876 </td> <td style="text-align:right;"> -0.1811005 </td> <td style="text-align:right;"> 0.3380757 </td> <td style="text-align:right;"> 0.0585593 </td> <td style="text-align:right;"> 0.1025193 </td> <td style="text-align:right;"> -0.1483375 </td> <td style="text-align:right;"> 0.3533762 </td> <td style="text-align:right;"> 0.0571438 </td> </tr> <tr> <td style="text-align:left;"> neutrophils </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.2587446 </td> <td style="text-align:right;"> 0.0130803 </td> <td style="text-align:right;"> 0.5044089 </td> <td style="text-align:right;"> 0.0557516 </td> <td style="text-align:right;"> 0.3799805 </td> <td style="text-align:right;"> -0.2060446 </td> <td style="text-align:right;"> 0.9660057 </td> <td style="text-align:right;"> 0.1317980 </td> <td style="text-align:right;"> 0.1319372 </td> <td style="text-align:right;"> -0.2669324 </td> <td style="text-align:right;"> 0.5308068 </td> <td style="text-align:right;"> 0.0924336 </td> </tr> <tr> <td style="text-align:left;"> nk cells </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0414772 </td> <td style="text-align:right;"> -0.0960406 </td> <td style="text-align:right;"> 0.0130862 </td> <td style="text-align:right;"> 0.0200411 </td> <td style="text-align:right;"> 0.0156533 </td> <td style="text-align:right;"> -0.0703789 </td> <td style="text-align:right;"> 0.1016856 </td> <td style="text-align:right;"> 0.0315487 </td> <td style="text-align:right;"> 0.0471757 </td> <td style="text-align:right;"> -0.0162213 </td> <td style="text-align:right;"> 0.1105728 </td> <td style="text-align:right;"> 0.0231831 </td> </tr> <tr> <td style="text-align:left;"> nkt cells </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> 0.0033757 </td> <td style="text-align:right;"> -0.1069890 </td> <td style="text-align:right;"> 0.1137404 </td> <td style="text-align:right;"> 0.0294661 </td> <td style="text-align:right;"> -0.2458705 </td> <td style="text-align:right;"> -0.4452333 </td> <td style="text-align:right;"> -0.0465077 </td> <td style="text-align:right;"> 0.0426738 </td> <td style="text-align:right;"> -0.1823355 </td> <td style="text-align:right;"> -0.3233946 </td> <td style="text-align:right;"> -0.0412763 </td> <td style="text-align:right;"> 0.0314580 </td> </tr> <tr> <td style="text-align:left;"> percentage of live gated events </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0934933 </td> <td style="text-align:right;"> -0.3037340 </td> <td style="text-align:right;"> 0.1167473 </td> <td style="text-align:right;"> 0.0165463 </td> <td style="text-align:right;"> -0.0412606 </td> <td style="text-align:right;"> -0.1414443 </td> <td style="text-align:right;"> 0.0589231 </td> <td style="text-align:right;"> 0.0078846 </td> <td style="text-align:right;"> 0.0500941 </td> <td style="text-align:right;"> 0.0081191 </td> <td style="text-align:right;"> 0.0920690 </td> <td style="text-align:right;"> 0.0033035 </td> </tr> <tr> <td style="text-align:left;"> response amplitude </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> Acoustic Startle and Pre-pulse Inhibition (PPI) </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0333147 </td> <td style="text-align:right;"> -0.0127585 </td> <td style="text-align:right;"> 0.0793879 </td> <td style="text-align:right;"> 0.0202947 </td> <td style="text-align:right;"> 0.2549274 </td> <td style="text-align:right;"> 0.1969787 </td> <td style="text-align:right;"> 0.3128761 </td> <td style="text-align:right;"> 0.0255003 </td> <td style="text-align:right;"> 0.2016062 </td> <td style="text-align:right;"> 0.1108136 </td> <td style="text-align:right;"> 0.2923987 </td> <td style="text-align:right;"> 0.0401164 </td> </tr> <tr> <td style="text-align:left;"> t cells </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1338701 </td> <td style="text-align:right;"> -0.2750284 </td> <td style="text-align:right;"> 0.0072883 </td> <td style="text-align:right;"> 0.0326594 </td> <td style="text-align:right;"> -0.1240786 </td> <td style="text-align:right;"> -0.4120104 </td> <td style="text-align:right;"> 0.1638531 </td> <td style="text-align:right;"> 0.0668611 </td> <td style="text-align:right;"> -0.0005749 </td> <td style="text-align:right;"> -0.1663201 </td> <td style="text-align:right;"> 0.1651702 </td> <td style="text-align:right;"> 0.0374233 </td> </tr> <tr> <td style="text-align:left;"> 12khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> 0.0538655 </td> <td style="text-align:right;"> -0.0056830 </td> <td style="text-align:right;"> 0.1134139 </td> <td style="text-align:right;"> 0.0303824 </td> <td style="text-align:right;"> 0.0869649 </td> <td style="text-align:right;"> 0.0065802 </td> <td style="text-align:right;"> 0.1673497 </td> <td style="text-align:right;"> 0.0410134 </td> <td style="text-align:right;"> 0.0024851 </td> <td style="text-align:right;"> -0.0214504 </td> <td style="text-align:right;"> 0.0264205 </td> <td style="text-align:right;"> 0.0122122 </td> </tr> <tr> <td style="text-align:left;"> 18khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> 0.0238241 </td> <td style="text-align:right;"> -0.0331809 </td> <td style="text-align:right;"> 0.0808292 </td> <td style="text-align:right;"> 0.0290848 </td> <td style="text-align:right;"> 0.0250266 </td> <td style="text-align:right;"> -0.0488450 </td> <td style="text-align:right;"> 0.0988982 </td> <td style="text-align:right;"> 0.0376903 </td> <td style="text-align:right;"> -0.0200763 </td> <td style="text-align:right;"> -0.0431508 </td> <td style="text-align:right;"> 0.0029982 </td> <td style="text-align:right;"> 0.0117729 </td> </tr> <tr> <td style="text-align:left;"> 24khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> 0.0518127 </td> <td style="text-align:right;"> -0.0148242 </td> <td style="text-align:right;"> 0.1184497 </td> <td style="text-align:right;"> 0.0339991 </td> <td style="text-align:right;"> -0.0891510 </td> <td style="text-align:right;"> -0.3321998 </td> <td style="text-align:right;"> 0.1538977 </td> <td style="text-align:right;"> 0.1240067 </td> <td style="text-align:right;"> -0.0224536 </td> <td style="text-align:right;"> -0.0444163 </td> <td style="text-align:right;"> -0.0004910 </td> <td style="text-align:right;"> 0.0112057 </td> </tr> <tr> <td style="text-align:left;"> 30khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> 0.0170933 </td> <td style="text-align:right;"> -0.0533187 </td> <td style="text-align:right;"> 0.0875053 </td> <td style="text-align:right;"> 0.0359252 </td> <td style="text-align:right;"> -0.0344797 </td> <td style="text-align:right;"> -0.1017901 </td> <td style="text-align:right;"> 0.0328306 </td> <td style="text-align:right;"> 0.0343426 </td> <td style="text-align:right;"> -0.0497874 </td> <td style="text-align:right;"> -0.0748197 </td> <td style="text-align:right;"> -0.0247550 </td> <td style="text-align:right;"> 0.0127718 </td> </tr> <tr> <td style="text-align:left;"> 6khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> -0.0077678 </td> <td style="text-align:right;"> -0.0418582 </td> <td style="text-align:right;"> 0.0263226 </td> <td style="text-align:right;"> 0.0173934 </td> <td style="text-align:right;"> 0.0141682 </td> <td style="text-align:right;"> -0.0189973 </td> <td style="text-align:right;"> 0.0473337 </td> <td style="text-align:right;"> 0.0169215 </td> <td style="text-align:right;"> 0.0184043 </td> <td style="text-align:right;"> 0.0056897 </td> <td style="text-align:right;"> 0.0311189 </td> <td style="text-align:right;"> 0.0064872 </td> </tr> <tr> <td style="text-align:left;"> alanine aminotransferase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0684217 </td> <td style="text-align:right;"> -0.1895020 </td> <td style="text-align:right;"> 0.0526586 </td> <td style="text-align:right;"> 0.0617768 </td> <td style="text-align:right;"> 0.0585179 </td> <td style="text-align:right;"> -0.1322507 </td> <td style="text-align:right;"> 0.2492866 </td> <td style="text-align:right;"> 0.0973327 </td> <td style="text-align:right;"> 0.1069442 </td> <td style="text-align:right;"> 0.0319934 </td> <td style="text-align:right;"> 0.1818950 </td> <td style="text-align:right;"> 0.0382409 </td> </tr> <tr> <td style="text-align:left;"> albumin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.1133080 </td> <td style="text-align:right;"> 0.0451475 </td> <td style="text-align:right;"> 0.1814685 </td> <td style="text-align:right;"> 0.0347764 </td> <td style="text-align:right;"> 0.0559995 </td> <td style="text-align:right;"> -0.0080678 </td> <td style="text-align:right;"> 0.1200668 </td> <td style="text-align:right;"> 0.0326880 </td> <td style="text-align:right;"> -0.0567840 </td> <td style="text-align:right;"> -0.0732083 </td> <td style="text-align:right;"> -0.0403597 </td> <td style="text-align:right;"> 0.0083799 </td> </tr> <tr> <td style="text-align:left;"> alkaline phosphatase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.1043649 </td> <td style="text-align:right;"> 0.0451585 </td> <td style="text-align:right;"> 0.1635713 </td> <td style="text-align:right;"> 0.0302079 </td> <td style="text-align:right;"> -0.3112471 </td> <td style="text-align:right;"> -0.3980164 </td> <td style="text-align:right;"> -0.2244778 </td> <td style="text-align:right;"> 0.0442709 </td> <td style="text-align:right;"> -0.4216032 </td> <td style="text-align:right;"> -0.4694832 </td> <td style="text-align:right;"> -0.3737231 </td> <td style="text-align:right;"> 0.0244290 </td> </tr> <tr> <td style="text-align:left;"> alpha-amylase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0383407 </td> <td style="text-align:right;"> -0.0423419 </td> <td style="text-align:right;"> 0.1190232 </td> <td style="text-align:right;"> 0.0411653 </td> <td style="text-align:right;"> 0.2795566 </td> <td style="text-align:right;"> 0.1615777 </td> <td style="text-align:right;"> 0.3975355 </td> <td style="text-align:right;"> 0.0601944 </td> <td style="text-align:right;"> 0.2246987 </td> <td style="text-align:right;"> 0.1793151 </td> <td style="text-align:right;"> 0.2700822 </td> <td style="text-align:right;"> 0.0231553 </td> </tr> <tr> <td style="text-align:left;"> area under glucose response curve </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.1531723 </td> <td style="text-align:right;"> -0.2210551 </td> <td style="text-align:right;"> -0.0852895 </td> <td style="text-align:right;"> 0.0346347 </td> <td style="text-align:right;"> 0.2748396 </td> <td style="text-align:right;"> 0.1950895 </td> <td style="text-align:right;"> 0.3545898 </td> <td style="text-align:right;"> 0.0406896 </td> <td style="text-align:right;"> 0.4357738 </td> <td style="text-align:right;"> 0.3655882 </td> <td style="text-align:right;"> 0.5059595 </td> <td style="text-align:right;"> 0.0358097 </td> </tr> <tr> <td style="text-align:left;"> aspartate aminotransferase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0119165 </td> <td style="text-align:right;"> -0.1228287 </td> <td style="text-align:right;"> 0.1466617 </td> <td style="text-align:right;"> 0.0687488 </td> <td style="text-align:right;"> -0.0566968 </td> <td style="text-align:right;"> -0.2457779 </td> <td style="text-align:right;"> 0.1323843 </td> <td style="text-align:right;"> 0.0964717 </td> <td style="text-align:right;"> -0.0585577 </td> <td style="text-align:right;"> -0.1331777 </td> <td style="text-align:right;"> 0.0160624 </td> <td style="text-align:right;"> 0.0380722 </td> </tr> <tr> <td style="text-align:left;"> basophil cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0917931 </td> <td style="text-align:right;"> -0.2022487 </td> <td style="text-align:right;"> 0.0186624 </td> <td style="text-align:right;"> 0.0563559 </td> <td style="text-align:right;"> 0.2031265 </td> <td style="text-align:right;"> -0.0131549 </td> <td style="text-align:right;"> 0.4194079 </td> <td style="text-align:right;"> 0.1103497 </td> <td style="text-align:right;"> 0.2675772 </td> <td style="text-align:right;"> 0.0643028 </td> <td style="text-align:right;"> 0.4708516 </td> <td style="text-align:right;"> 0.1037133 </td> </tr> <tr> <td style="text-align:left;"> basophil differential count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0934739 </td> <td style="text-align:right;"> -0.1787512 </td> <td style="text-align:right;"> -0.0081966 </td> <td style="text-align:right;"> 0.0435096 </td> <td style="text-align:right;"> -0.0639511 </td> <td style="text-align:right;"> -0.2828066 </td> <td style="text-align:right;"> 0.1549044 </td> <td style="text-align:right;"> 0.1116630 </td> <td style="text-align:right;"> -0.0156339 </td> <td style="text-align:right;"> -0.1102310 </td> <td style="text-align:right;"> 0.0789633 </td> <td style="text-align:right;"> 0.0482647 </td> </tr> <tr> <td style="text-align:left;"> bmc/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1314998 </td> <td style="text-align:right;"> 0.0329846 </td> <td style="text-align:right;"> 0.2300151 </td> <td style="text-align:right;"> 0.0502638 </td> <td style="text-align:right;"> -0.0448684 </td> <td style="text-align:right;"> -0.1340146 </td> <td style="text-align:right;"> 0.0442777 </td> <td style="text-align:right;"> 0.0454836 </td> <td style="text-align:right;"> -0.1722378 </td> <td style="text-align:right;"> -0.2207030 </td> <td style="text-align:right;"> -0.1237726 </td> <td style="text-align:right;"> 0.0247276 </td> </tr> <tr> <td style="text-align:left;"> body length </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> -0.0347988 </td> <td style="text-align:right;"> -0.0824528 </td> <td style="text-align:right;"> 0.0128552 </td> <td style="text-align:right;"> 0.0243137 </td> <td style="text-align:right;"> -0.0059677 </td> <td style="text-align:right;"> -0.0526221 </td> <td style="text-align:right;"> 0.0406866 </td> <td style="text-align:right;"> 0.0238037 </td> <td style="text-align:right;"> 0.0282722 </td> <td style="text-align:right;"> 0.0233254 </td> <td style="text-align:right;"> 0.0332189 </td> <td style="text-align:right;"> 0.0025239 </td> </tr> <tr> <td style="text-align:left;"> body temp </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0325368 </td> <td style="text-align:right;"> -0.1066429 </td> <td style="text-align:right;"> 0.0415693 </td> <td style="text-align:right;"> 0.0378099 </td> <td style="text-align:right;"> -0.0303742 </td> <td style="text-align:right;"> -0.1044537 </td> <td style="text-align:right;"> 0.0437054 </td> <td style="text-align:right;"> 0.0377964 </td> <td style="text-align:right;"> 0.0018532 </td> <td style="text-align:right;"> -0.0005002 </td> <td style="text-align:right;"> 0.0042066 </td> <td style="text-align:right;"> 0.0012008 </td> </tr> <tr> <td style="text-align:left;"> body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0245675 </td> <td style="text-align:right;"> -0.0420402 </td> <td style="text-align:right;"> 0.0911752 </td> <td style="text-align:right;"> 0.0339841 </td> <td style="text-align:right;"> 0.2335793 </td> <td style="text-align:right;"> 0.1694979 </td> <td style="text-align:right;"> 0.2976607 </td> <td style="text-align:right;"> 0.0326952 </td> <td style="text-align:right;"> 0.2096770 </td> <td style="text-align:right;"> 0.1938727 </td> <td style="text-align:right;"> 0.2254813 </td> <td style="text-align:right;"> 0.0080636 </td> </tr> <tr> <td style="text-align:left;"> body weight after experiment </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> 0.0853708 </td> <td style="text-align:right;"> 0.0299665 </td> <td style="text-align:right;"> 0.1407751 </td> <td style="text-align:right;"> 0.0282680 </td> <td style="text-align:right;"> 0.2849370 </td> <td style="text-align:right;"> 0.2328875 </td> <td style="text-align:right;"> 0.3369866 </td> <td style="text-align:right;"> 0.0265564 </td> <td style="text-align:right;"> 0.2030973 </td> <td style="text-align:right;"> 0.1864076 </td> <td style="text-align:right;"> 0.2197871 </td> <td style="text-align:right;"> 0.0085153 </td> </tr> <tr> <td style="text-align:left;"> body weight before experiment </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> 0.1053511 </td> <td style="text-align:right;"> 0.0412461 </td> <td style="text-align:right;"> 0.1694562 </td> <td style="text-align:right;"> 0.0327073 </td> <td style="text-align:right;"> 0.3038998 </td> <td style="text-align:right;"> 0.2435428 </td> <td style="text-align:right;"> 0.3642568 </td> <td style="text-align:right;"> 0.0307949 </td> <td style="text-align:right;"> 0.2008638 </td> <td style="text-align:right;"> 0.1816362 </td> <td style="text-align:right;"> 0.2200914 </td> <td style="text-align:right;"> 0.0098102 </td> </tr> <tr> <td style="text-align:left;"> bone area </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0981587 </td> <td style="text-align:right;"> 0.0272824 </td> <td style="text-align:right;"> 0.1690349 </td> <td style="text-align:right;"> 0.0361620 </td> <td style="text-align:right;"> 0.1286546 </td> <td style="text-align:right;"> 0.0533659 </td> <td style="text-align:right;"> 0.2039432 </td> <td style="text-align:right;"> 0.0384133 </td> <td style="text-align:right;"> 0.0315241 </td> <td style="text-align:right;"> 0.0003806 </td> <td style="text-align:right;"> 0.0626676 </td> <td style="text-align:right;"> 0.0158898 </td> </tr> <tr> <td style="text-align:left;"> bone mineral content (excluding skull) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1709230 </td> <td style="text-align:right;"> 0.0625642 </td> <td style="text-align:right;"> 0.2792818 </td> <td style="text-align:right;"> 0.0552861 </td> <td style="text-align:right;"> 0.2091372 </td> <td style="text-align:right;"> 0.1015600 </td> <td style="text-align:right;"> 0.3167143 </td> <td style="text-align:right;"> 0.0548873 </td> <td style="text-align:right;"> 0.0372537 </td> <td style="text-align:right;"> -0.0130828 </td> <td style="text-align:right;"> 0.0875902 </td> <td style="text-align:right;"> 0.0256824 </td> </tr> <tr> <td style="text-align:left;"> bone mineral density (excluding skull) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0542638 </td> <td style="text-align:right;"> -0.0881612 </td> <td style="text-align:right;"> 0.1966887 </td> <td style="text-align:right;"> 0.0726671 </td> <td style="text-align:right;"> 0.0492830 </td> <td style="text-align:right;"> -0.1087868 </td> <td style="text-align:right;"> 0.2073528 </td> <td style="text-align:right;"> 0.0806494 </td> <td style="text-align:right;"> 0.0012286 </td> <td style="text-align:right;"> -0.0187942 </td> <td style="text-align:right;"> 0.0212514 </td> <td style="text-align:right;"> 0.0102159 </td> </tr> <tr> <td style="text-align:left;"> calcium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0097946 </td> <td style="text-align:right;"> -0.0464600 </td> <td style="text-align:right;"> 0.0660492 </td> <td style="text-align:right;"> 0.0287018 </td> <td style="text-align:right;"> 0.0135683 </td> <td style="text-align:right;"> -0.0424600 </td> <td style="text-align:right;"> 0.0695966 </td> <td style="text-align:right;"> 0.0285864 </td> <td style="text-align:right;"> 0.0036564 </td> <td style="text-align:right;"> -0.0000609 </td> <td style="text-align:right;"> 0.0073737 </td> <td style="text-align:right;"> 0.0018966 </td> </tr> <tr> <td style="text-align:left;"> cardiac output </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0133816 </td> <td style="text-align:right;"> -0.0797535 </td> <td style="text-align:right;"> 0.1065166 </td> <td style="text-align:right;"> 0.0475188 </td> <td style="text-align:right;"> 0.1017991 </td> <td style="text-align:right;"> 0.0206287 </td> <td style="text-align:right;"> 0.1829694 </td> <td style="text-align:right;"> 0.0414142 </td> <td style="text-align:right;"> 0.0934439 </td> <td style="text-align:right;"> 0.0580233 </td> <td style="text-align:right;"> 0.1288645 </td> <td style="text-align:right;"> 0.0180721 </td> </tr> <tr> <td style="text-align:left;"> center average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0167300 </td> <td style="text-align:right;"> -0.0404735 </td> <td style="text-align:right;"> 0.0739335 </td> <td style="text-align:right;"> 0.0291860 </td> <td style="text-align:right;"> -0.0588515 </td> <td style="text-align:right;"> -0.1004209 </td> <td style="text-align:right;"> -0.0172820 </td> <td style="text-align:right;"> 0.0212093 </td> <td style="text-align:right;"> -0.0724619 </td> <td style="text-align:right;"> -0.1149622 </td> <td style="text-align:right;"> -0.0299616 </td> <td style="text-align:right;"> 0.0216842 </td> </tr> <tr> <td style="text-align:left;"> center distance travelled </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0162603 </td> <td style="text-align:right;"> -0.0733243 </td> <td style="text-align:right;"> 0.0408038 </td> <td style="text-align:right;"> 0.0291149 </td> <td style="text-align:right;"> -0.1060637 </td> <td style="text-align:right;"> -0.2023343 </td> <td style="text-align:right;"> -0.0097930 </td> <td style="text-align:right;"> 0.0491186 </td> <td style="text-align:right;"> -0.0940204 </td> <td style="text-align:right;"> -0.1945774 </td> <td style="text-align:right;"> 0.0065366 </td> <td style="text-align:right;"> 0.0513055 </td> </tr> <tr> <td style="text-align:left;"> center permanence time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0253715 </td> <td style="text-align:right;"> -0.0826435 </td> <td style="text-align:right;"> 0.0319004 </td> <td style="text-align:right;"> 0.0292209 </td> <td style="text-align:right;"> -0.0255734 </td> <td style="text-align:right;"> -0.1014389 </td> <td style="text-align:right;"> 0.0502922 </td> <td style="text-align:right;"> 0.0387076 </td> <td style="text-align:right;"> -0.0035151 </td> <td style="text-align:right;"> -0.0902886 </td> <td style="text-align:right;"> 0.0832585 </td> <td style="text-align:right;"> 0.0442730 </td> </tr> <tr> <td style="text-align:left;"> center resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0244492 </td> <td style="text-align:right;"> -0.0737922 </td> <td style="text-align:right;"> 0.1226906 </td> <td style="text-align:right;"> 0.0501241 </td> <td style="text-align:right;"> -0.0228690 </td> <td style="text-align:right;"> -0.1548339 </td> <td style="text-align:right;"> 0.1090960 </td> <td style="text-align:right;"> 0.0673303 </td> <td style="text-align:right;"> -0.0630751 </td> <td style="text-align:right;"> -0.2215457 </td> <td style="text-align:right;"> 0.0953955 </td> <td style="text-align:right;"> 0.0808538 </td> </tr> <tr> <td style="text-align:left;"> chloride </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0321555 </td> <td style="text-align:right;"> -0.1270972 </td> <td style="text-align:right;"> 0.1914083 </td> <td style="text-align:right;"> 0.0812529 </td> <td style="text-align:right;"> 0.0241491 </td> <td style="text-align:right;"> -0.1438502 </td> <td style="text-align:right;"> 0.1921485 </td> <td style="text-align:right;"> 0.0857155 </td> <td style="text-align:right;"> -0.0127047 </td> <td style="text-align:right;"> -0.0177349 </td> <td style="text-align:right;"> -0.0076745 </td> <td style="text-align:right;"> 0.0025665 </td> </tr> <tr> <td style="text-align:left;"> click-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> -0.0529450 </td> <td style="text-align:right;"> -0.1534816 </td> <td style="text-align:right;"> 0.0475915 </td> <td style="text-align:right;"> 0.0512951 </td> <td style="text-align:right;"> -0.0561198 </td> <td style="text-align:right;"> -0.1827679 </td> <td style="text-align:right;"> 0.0705282 </td> <td style="text-align:right;"> 0.0646176 </td> <td style="text-align:right;"> -0.0154221 </td> <td style="text-align:right;"> -0.0577200 </td> <td style="text-align:right;"> 0.0268757 </td> <td style="text-align:right;"> 0.0215809 </td> </tr> <tr> <td style="text-align:left;"> creatine kinase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0241232 </td> <td style="text-align:right;"> -0.1071457 </td> <td style="text-align:right;"> 0.1553920 </td> <td style="text-align:right;"> 0.0669751 </td> <td style="text-align:right;"> -0.1318792 </td> <td style="text-align:right;"> -0.3968974 </td> <td style="text-align:right;"> 0.1331390 </td> <td style="text-align:right;"> 0.1352159 </td> <td style="text-align:right;"> -0.1344413 </td> <td style="text-align:right;"> -0.3838303 </td> <td style="text-align:right;"> 0.1149476 </td> <td style="text-align:right;"> 0.1272416 </td> </tr> <tr> <td style="text-align:left;"> creatinine </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0352315 </td> <td style="text-align:right;"> -0.0229205 </td> <td style="text-align:right;"> 0.0933835 </td> <td style="text-align:right;"> 0.0296699 </td> <td style="text-align:right;"> 0.1066373 </td> <td style="text-align:right;"> -0.2200831 </td> <td style="text-align:right;"> 0.4333578 </td> <td style="text-align:right;"> 0.1666972 </td> <td style="text-align:right;"> -0.0844078 </td> <td style="text-align:right;"> -0.1320251 </td> <td style="text-align:right;"> -0.0367905 </td> <td style="text-align:right;"> 0.0242950 </td> </tr> <tr> <td style="text-align:left;"> cv </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.1874544 </td> <td style="text-align:right;"> 0.0716631 </td> <td style="text-align:right;"> 0.3032457 </td> <td style="text-align:right;"> 0.0590783 </td> <td style="text-align:right;"> -0.0895722 </td> <td style="text-align:right;"> -0.2484833 </td> <td style="text-align:right;"> 0.0693388 </td> <td style="text-align:right;"> 0.0810786 </td> <td style="text-align:right;"> -0.2401301 </td> <td style="text-align:right;"> -0.3410322 </td> <td style="text-align:right;"> -0.1392280 </td> <td style="text-align:right;"> 0.0514816 </td> </tr> <tr> <td style="text-align:left;"> distance travelled - total </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0187819 </td> <td style="text-align:right;"> -0.0858957 </td> <td style="text-align:right;"> 0.0483318 </td> <td style="text-align:right;"> 0.0342423 </td> <td style="text-align:right;"> -0.1272582 </td> <td style="text-align:right;"> -0.1997426 </td> <td style="text-align:right;"> -0.0547738 </td> <td style="text-align:right;"> 0.0369825 </td> <td style="text-align:right;"> -0.1121373 </td> <td style="text-align:right;"> -0.1816322 </td> <td style="text-align:right;"> -0.0426424 </td> <td style="text-align:right;"> 0.0354572 </td> </tr> <tr> <td style="text-align:left;"> ejection fraction </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0300111 </td> <td style="text-align:right;"> -0.1345066 </td> <td style="text-align:right;"> 0.0744844 </td> <td style="text-align:right;"> 0.0533150 </td> <td style="text-align:right;"> -0.0525735 </td> <td style="text-align:right;"> -0.1483174 </td> <td style="text-align:right;"> 0.0431705 </td> <td style="text-align:right;"> 0.0488499 </td> <td style="text-align:right;"> -0.0284086 </td> <td style="text-align:right;"> -0.0492579 </td> <td style="text-align:right;"> -0.0075592 </td> <td style="text-align:right;"> 0.0106376 </td> </tr> <tr> <td style="text-align:left;"> end-diastolic diameter </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.1120972 </td> <td style="text-align:right;"> 0.0431489 </td> <td style="text-align:right;"> 0.1810454 </td> <td style="text-align:right;"> 0.0351783 </td> <td style="text-align:right;"> 0.1743929 </td> <td style="text-align:right;"> 0.0875252 </td> <td style="text-align:right;"> 0.2612607 </td> <td style="text-align:right;"> 0.0443211 </td> <td style="text-align:right;"> 0.0600907 </td> <td style="text-align:right;"> 0.0354923 </td> <td style="text-align:right;"> 0.0846891 </td> <td style="text-align:right;"> 0.0125504 </td> </tr> <tr> <td style="text-align:left;"> end-systolic diameter </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0084176 </td> <td style="text-align:right;"> -0.0780811 </td> <td style="text-align:right;"> 0.0612459 </td> <td style="text-align:right;"> 0.0355433 </td> <td style="text-align:right;"> 0.0668966 </td> <td style="text-align:right;"> -0.0016692 </td> <td style="text-align:right;"> 0.1354624 </td> <td style="text-align:right;"> 0.0349832 </td> <td style="text-align:right;"> 0.0763195 </td> <td style="text-align:right;"> 0.0451136 </td> <td style="text-align:right;"> 0.1075254 </td> <td style="text-align:right;"> 0.0159217 </td> </tr> <tr> <td style="text-align:left;"> fasted blood glucose concentration </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.0177245 </td> <td style="text-align:right;"> -0.1256855 </td> <td style="text-align:right;"> 0.0902366 </td> <td style="text-align:right;"> 0.0550832 </td> <td style="text-align:right;"> 0.0702824 </td> <td style="text-align:right;"> -0.0302439 </td> <td style="text-align:right;"> 0.1708087 </td> <td style="text-align:right;"> 0.0512899 </td> <td style="text-align:right;"> 0.0868420 </td> <td style="text-align:right;"> 0.0493007 </td> <td style="text-align:right;"> 0.1243832 </td> <td style="text-align:right;"> 0.0191541 </td> </tr> <tr> <td style="text-align:left;"> fat mass </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0408799 </td> <td style="text-align:right;"> -0.0430149 </td> <td style="text-align:right;"> 0.1247746 </td> <td style="text-align:right;"> 0.0428042 </td> <td style="text-align:right;"> 0.3714313 </td> <td style="text-align:right;"> 0.2698790 </td> <td style="text-align:right;"> 0.4729837 </td> <td style="text-align:right;"> 0.0518134 </td> <td style="text-align:right;"> 0.3282080 </td> <td style="text-align:right;"> 0.2669032 </td> <td style="text-align:right;"> 0.3895129 </td> <td style="text-align:right;"> 0.0312786 </td> </tr> <tr> <td style="text-align:left;"> fat/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0777327 </td> <td style="text-align:right;"> -0.0119735 </td> <td style="text-align:right;"> 0.1674390 </td> <td style="text-align:right;"> 0.0457693 </td> <td style="text-align:right;"> 0.2020776 </td> <td style="text-align:right;"> 0.1083557 </td> <td style="text-align:right;"> 0.2957996 </td> <td style="text-align:right;"> 0.0478182 </td> <td style="text-align:right;"> 0.1235292 </td> <td style="text-align:right;"> 0.0638629 </td> <td style="text-align:right;"> 0.1831955 </td> <td style="text-align:right;"> 0.0304425 </td> </tr> <tr> <td style="text-align:left;"> forelimb and hindlimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Grip Strength </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0578158 </td> <td style="text-align:right;"> 0.0039998 </td> <td style="text-align:right;"> 0.1116318 </td> <td style="text-align:right;"> 0.0274577 </td> <td style="text-align:right;"> 0.1145986 </td> <td style="text-align:right;"> 0.0530521 </td> <td style="text-align:right;"> 0.1761451 </td> <td style="text-align:right;"> 0.0314018 </td> <td style="text-align:right;"> 0.0541888 </td> <td style="text-align:right;"> 0.0294838 </td> <td style="text-align:right;"> 0.0788938 </td> <td style="text-align:right;"> 0.0126048 </td> </tr> <tr> <td style="text-align:left;"> forelimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Grip Strength </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0265051 </td> <td style="text-align:right;"> -0.0187240 </td> <td style="text-align:right;"> 0.0717341 </td> <td style="text-align:right;"> 0.0230765 </td> <td style="text-align:right;"> 0.0995076 </td> <td style="text-align:right;"> 0.0539740 </td> <td style="text-align:right;"> 0.1450413 </td> <td style="text-align:right;"> 0.0232319 </td> <td style="text-align:right;"> 0.0697061 </td> <td style="text-align:right;"> 0.0438625 </td> <td style="text-align:right;"> 0.0955496 </td> <td style="text-align:right;"> 0.0131857 </td> </tr> <tr> <td style="text-align:left;"> fractional shortening </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0148852 </td> <td style="text-align:right;"> -0.1161666 </td> <td style="text-align:right;"> 0.0863961 </td> <td style="text-align:right;"> 0.0516751 </td> <td style="text-align:right;"> -0.0575326 </td> <td style="text-align:right;"> -0.1558559 </td> <td style="text-align:right;"> 0.0407907 </td> <td style="text-align:right;"> 0.0501659 </td> <td style="text-align:right;"> -0.0413498 </td> <td style="text-align:right;"> -0.0567105 </td> <td style="text-align:right;"> -0.0259891 </td> <td style="text-align:right;"> 0.0078372 </td> </tr> <tr> <td style="text-align:left;"> free fatty acids </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0281576 </td> <td style="text-align:right;"> -0.1002531 </td> <td style="text-align:right;"> 0.1565683 </td> <td style="text-align:right;"> 0.0655169 </td> <td style="text-align:right;"> 0.0554109 </td> <td style="text-align:right;"> -0.0736861 </td> <td style="text-align:right;"> 0.1845079 </td> <td style="text-align:right;"> 0.0658670 </td> <td style="text-align:right;"> 0.0193783 </td> <td style="text-align:right;"> -0.0093700 </td> <td style="text-align:right;"> 0.0481266 </td> <td style="text-align:right;"> 0.0146678 </td> </tr> <tr> <td style="text-align:left;"> fructosamine </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0397864 </td> <td style="text-align:right;"> -0.1198801 </td> <td style="text-align:right;"> 0.0403073 </td> <td style="text-align:right;"> 0.0408649 </td> <td style="text-align:right;"> -0.0678231 </td> <td style="text-align:right;"> -0.1513538 </td> <td style="text-align:right;"> 0.0157075 </td> <td style="text-align:right;"> 0.0426184 </td> <td style="text-align:right;"> -0.0283579 </td> <td style="text-align:right;"> -0.0692447 </td> <td style="text-align:right;"> 0.0125289 </td> <td style="text-align:right;"> 0.0208610 </td> </tr> <tr> <td style="text-align:left;"> glucose </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0692601 </td> <td style="text-align:right;"> 0.0184025 </td> <td style="text-align:right;"> 0.1201176 </td> <td style="text-align:right;"> 0.0259482 </td> <td style="text-align:right;"> 0.1279473 </td> <td style="text-align:right;"> 0.0423001 </td> <td style="text-align:right;"> 0.2135946 </td> <td style="text-align:right;"> 0.0436984 </td> <td style="text-align:right;"> 0.0650887 </td> <td style="text-align:right;"> 0.0218496 </td> <td style="text-align:right;"> 0.1083279 </td> <td style="text-align:right;"> 0.0220612 </td> </tr> <tr> <td style="text-align:left;"> hdl-cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0650177 </td> <td style="text-align:right;"> -0.1255786 </td> <td style="text-align:right;"> -0.0044568 </td> <td style="text-align:right;"> 0.0308990 </td> <td style="text-align:right;"> 0.1724354 </td> <td style="text-align:right;"> 0.0701062 </td> <td style="text-align:right;"> 0.2747646 </td> <td style="text-align:right;"> 0.0522097 </td> <td style="text-align:right;"> 0.2606961 </td> <td style="text-align:right;"> 0.2180421 </td> <td style="text-align:right;"> 0.3033501 </td> <td style="text-align:right;"> 0.0217626 </td> </tr> <tr> <td style="text-align:left;"> heart weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1766832 </td> <td style="text-align:right;"> 0.0672843 </td> <td style="text-align:right;"> 0.2860820 </td> <td style="text-align:right;"> 0.0558168 </td> <td style="text-align:right;"> 0.3651806 </td> <td style="text-align:right;"> 0.2169840 </td> <td style="text-align:right;"> 0.5133772 </td> <td style="text-align:right;"> 0.0756119 </td> <td style="text-align:right;"> 0.1737615 </td> <td style="text-align:right;"> 0.1409037 </td> <td style="text-align:right;"> 0.2066193 </td> <td style="text-align:right;"> 0.0167645 </td> </tr> <tr> <td style="text-align:left;"> heart weight normalised against body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0794303 </td> <td style="text-align:right;"> -0.0060591 </td> <td style="text-align:right;"> 0.1649198 </td> <td style="text-align:right;"> 0.0436179 </td> <td style="text-align:right;"> 0.0355574 </td> <td style="text-align:right;"> -0.0973272 </td> <td style="text-align:right;"> 0.1684419 </td> <td style="text-align:right;"> 0.0677995 </td> <td style="text-align:right;"> -0.0495578 </td> <td style="text-align:right;"> -0.0835809 </td> <td style="text-align:right;"> -0.0155346 </td> <td style="text-align:right;"> 0.0173591 </td> </tr> <tr> <td style="text-align:left;"> hematocrit </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0566356 </td> <td style="text-align:right;"> -0.0516862 </td> <td style="text-align:right;"> 0.1649575 </td> <td style="text-align:right;"> 0.0552673 </td> <td style="text-align:right;"> 0.0737071 </td> <td style="text-align:right;"> -0.0328632 </td> <td style="text-align:right;"> 0.1802774 </td> <td style="text-align:right;"> 0.0543736 </td> <td style="text-align:right;"> 0.0173967 </td> <td style="text-align:right;"> 0.0035179 </td> <td style="text-align:right;"> 0.0312754 </td> <td style="text-align:right;"> 0.0070811 </td> </tr> <tr> <td style="text-align:left;"> hemoglobin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0867000 </td> <td style="text-align:right;"> 0.0269936 </td> <td style="text-align:right;"> 0.1464064 </td> <td style="text-align:right;"> 0.0304630 </td> <td style="text-align:right;"> 0.0867345 </td> <td style="text-align:right;"> 0.0194022 </td> <td style="text-align:right;"> 0.1540668 </td> <td style="text-align:right;"> 0.0343538 </td> <td style="text-align:right;"> 0.0051992 </td> <td style="text-align:right;"> -0.0080216 </td> <td style="text-align:right;"> 0.0184199 </td> <td style="text-align:right;"> 0.0067454 </td> </tr> <tr> <td style="text-align:left;"> hr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0634490 </td> <td style="text-align:right;"> -0.1734699 </td> <td style="text-align:right;"> 0.0465718 </td> <td style="text-align:right;"> 0.0561341 </td> <td style="text-align:right;"> -0.0140315 </td> <td style="text-align:right;"> -0.1488474 </td> <td style="text-align:right;"> 0.1207843 </td> <td style="text-align:right;"> 0.0687849 </td> <td style="text-align:right;"> 0.0406617 </td> <td style="text-align:right;"> -0.0139214 </td> <td style="text-align:right;"> 0.0952448 </td> <td style="text-align:right;"> 0.0278490 </td> </tr> <tr> <td style="text-align:left;"> hrv </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.1722593 </td> <td style="text-align:right;"> 0.1094294 </td> <td style="text-align:right;"> 0.2350892 </td> <td style="text-align:right;"> 0.0320567 </td> <td style="text-align:right;"> -0.0813225 </td> <td style="text-align:right;"> -0.2125462 </td> <td style="text-align:right;"> 0.0499011 </td> <td style="text-align:right;"> 0.0669521 </td> <td style="text-align:right;"> -0.2504990 </td> <td style="text-align:right;"> -0.3657436 </td> <td style="text-align:right;"> -0.1352545 </td> <td style="text-align:right;"> 0.0587993 </td> </tr> <tr> <td style="text-align:left;"> initial response to glucose challenge </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.0968821 </td> <td style="text-align:right;"> -0.1503780 </td> <td style="text-align:right;"> -0.0433861 </td> <td style="text-align:right;"> 0.0272943 </td> <td style="text-align:right;"> 0.0429971 </td> <td style="text-align:right;"> 0.0141807 </td> <td style="text-align:right;"> 0.0718136 </td> <td style="text-align:right;"> 0.0147026 </td> <td style="text-align:right;"> 0.1183626 </td> <td style="text-align:right;"> 0.0853242 </td> <td style="text-align:right;"> 0.1514009 </td> <td style="text-align:right;"> 0.0168566 </td> </tr> <tr> <td style="text-align:left;"> insulin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Insulin Blood Level </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.0993292 </td> <td style="text-align:right;"> -0.3721975 </td> <td style="text-align:right;"> 0.1735391 </td> <td style="text-align:right;"> 0.1392211 </td> <td style="text-align:right;"> 0.1774003 </td> <td style="text-align:right;"> -0.1938091 </td> <td style="text-align:right;"> 0.5486096 </td> <td style="text-align:right;"> 0.1893960 </td> <td style="text-align:right;"> 0.4445455 </td> <td style="text-align:right;"> 0.0944498 </td> <td style="text-align:right;"> 0.7946412 </td> <td style="text-align:right;"> 0.1786236 </td> </tr> <tr> <td style="text-align:left;"> iron </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0974214 </td> <td style="text-align:right;"> -0.2141737 </td> <td style="text-align:right;"> 0.0193310 </td> <td style="text-align:right;"> 0.0595686 </td> <td style="text-align:right;"> -0.2534898 </td> <td style="text-align:right;"> -0.3963648 </td> <td style="text-align:right;"> -0.1106147 </td> <td style="text-align:right;"> 0.0728968 </td> <td style="text-align:right;"> -0.1527977 </td> <td style="text-align:right;"> -0.1930307 </td> <td style="text-align:right;"> -0.1125646 </td> <td style="text-align:right;"> 0.0205274 </td> </tr> <tr> <td style="text-align:left;"> lactate dehydrogenase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0941249 </td> <td style="text-align:right;"> -0.0214022 </td> <td style="text-align:right;"> 0.2096519 </td> <td style="text-align:right;"> 0.0589435 </td> <td style="text-align:right;"> 0.1409270 </td> <td style="text-align:right;"> -0.0620594 </td> <td style="text-align:right;"> 0.3439133 </td> <td style="text-align:right;"> 0.1035664 </td> <td style="text-align:right;"> 0.0318801 </td> <td style="text-align:right;"> -0.1412218 </td> <td style="text-align:right;"> 0.2049819 </td> <td style="text-align:right;"> 0.0883189 </td> </tr> <tr> <td style="text-align:left;"> latency to center entry </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.1254239 </td> <td style="text-align:right;"> 0.0330185 </td> <td style="text-align:right;"> 0.2178293 </td> <td style="text-align:right;"> 0.0471465 </td> <td style="text-align:right;"> 0.3641221 </td> <td style="text-align:right;"> 0.2056000 </td> <td style="text-align:right;"> 0.5226441 </td> <td style="text-align:right;"> 0.0808801 </td> <td style="text-align:right;"> 0.2734519 </td> <td style="text-align:right;"> 0.0739366 </td> <td style="text-align:right;"> 0.4729672 </td> <td style="text-align:right;"> 0.1017954 </td> </tr> <tr> <td style="text-align:left;"> ldl-cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.4231644 </td> <td style="text-align:right;"> 0.1551776 </td> <td style="text-align:right;"> 0.6911512 </td> <td style="text-align:right;"> 0.1367305 </td> <td style="text-align:right;"> 0.2669283 </td> <td style="text-align:right;"> -0.0956833 </td> <td style="text-align:right;"> 0.6295400 </td> <td style="text-align:right;"> 0.1850093 </td> <td style="text-align:right;"> -0.1615499 </td> <td style="text-align:right;"> -0.6010478 </td> <td style="text-align:right;"> 0.2779480 </td> <td style="text-align:right;"> 0.2242378 </td> </tr> <tr> <td style="text-align:left;"> lean mass </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1435756 </td> <td style="text-align:right;"> 0.0759342 </td> <td style="text-align:right;"> 0.2112170 </td> <td style="text-align:right;"> 0.0345115 </td> <td style="text-align:right;"> 0.3382447 </td> <td style="text-align:right;"> 0.2664863 </td> <td style="text-align:right;"> 0.4100031 </td> <td style="text-align:right;"> 0.0366121 </td> <td style="text-align:right;"> 0.1928945 </td> <td style="text-align:right;"> 0.1752425 </td> <td style="text-align:right;"> 0.2105465 </td> <td style="text-align:right;"> 0.0090063 </td> </tr> <tr> <td style="text-align:left;"> lean/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1953833 </td> <td style="text-align:right;"> 0.0912480 </td> <td style="text-align:right;"> 0.2995186 </td> <td style="text-align:right;"> 0.0531312 </td> <td style="text-align:right;"> 0.1840786 </td> <td style="text-align:right;"> 0.0863764 </td> <td style="text-align:right;"> 0.2817807 </td> <td style="text-align:right;"> 0.0498490 </td> <td style="text-align:right;"> -0.0122785 </td> <td style="text-align:right;"> -0.0257504 </td> <td style="text-align:right;"> 0.0011934 </td> <td style="text-align:right;"> 0.0068736 </td> </tr> <tr> <td style="text-align:left;"> left anterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.1854856 </td> <td style="text-align:right;"> -0.4305058 </td> <td style="text-align:right;"> 0.0595347 </td> <td style="text-align:right;"> 0.1250126 </td> <td style="text-align:right;"> -0.1534983 </td> <td style="text-align:right;"> -0.4007283 </td> <td style="text-align:right;"> 0.0937316 </td> <td style="text-align:right;"> 0.1261401 </td> <td style="text-align:right;"> 0.0331746 </td> <td style="text-align:right;"> 0.0284172 </td> <td style="text-align:right;"> 0.0379321 </td> <td style="text-align:right;"> 0.0024273 </td> </tr> <tr> <td style="text-align:left;"> left corneal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.1446634 </td> <td style="text-align:right;"> -0.2339950 </td> <td style="text-align:right;"> -0.0553319 </td> <td style="text-align:right;"> 0.0455782 </td> <td style="text-align:right;"> -0.1352252 </td> <td style="text-align:right;"> -0.2234178 </td> <td style="text-align:right;"> -0.0470327 </td> <td style="text-align:right;"> 0.0449970 </td> <td style="text-align:right;"> 0.0075283 </td> <td style="text-align:right;"> -0.0057082 </td> <td style="text-align:right;"> 0.0207648 </td> <td style="text-align:right;"> 0.0067535 </td> </tr> <tr> <td style="text-align:left;"> left inner nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> 0.0480458 </td> <td style="text-align:right;"> -0.0360706 </td> <td style="text-align:right;"> 0.1321622 </td> <td style="text-align:right;"> 0.0429173 </td> <td style="text-align:right;"> 0.0487217 </td> <td style="text-align:right;"> -0.0347622 </td> <td style="text-align:right;"> 0.1322057 </td> <td style="text-align:right;"> 0.0425946 </td> <td style="text-align:right;"> 0.0006956 </td> <td style="text-align:right;"> -0.0095012 </td> <td style="text-align:right;"> 0.0108923 </td> <td style="text-align:right;"> 0.0052025 </td> </tr> <tr> <td style="text-align:left;"> left outer nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.0675012 </td> <td style="text-align:right;"> -0.1511666 </td> <td style="text-align:right;"> 0.0161641 </td> <td style="text-align:right;"> 0.0426872 </td> <td style="text-align:right;"> -0.0618025 </td> <td style="text-align:right;"> -0.1452865 </td> <td style="text-align:right;"> 0.0216814 </td> <td style="text-align:right;"> 0.0425946 </td> <td style="text-align:right;"> 0.0063811 </td> <td style="text-align:right;"> 0.0011702 </td> <td style="text-align:right;"> 0.0115921 </td> <td style="text-align:right;"> 0.0026587 </td> </tr> <tr> <td style="text-align:left;"> left posterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.2631046 </td> <td style="text-align:right;"> -0.4734756 </td> <td style="text-align:right;"> -0.0527336 </td> <td style="text-align:right;"> 0.1073341 </td> <td style="text-align:right;"> -0.2687360 </td> <td style="text-align:right;"> -0.4790035 </td> <td style="text-align:right;"> -0.0584686 </td> <td style="text-align:right;"> 0.1072813 </td> <td style="text-align:right;"> -0.0026027 </td> <td style="text-align:right;"> -0.0146655 </td> <td style="text-align:right;"> 0.0094600 </td> <td style="text-align:right;"> 0.0061546 </td> </tr> <tr> <td style="text-align:left;"> left total retinal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.1975770 </td> <td style="text-align:right;"> -0.4386627 </td> <td style="text-align:right;"> 0.0435087 </td> <td style="text-align:right;"> 0.1230052 </td> <td style="text-align:right;"> -0.1932648 </td> <td style="text-align:right;"> -0.4269751 </td> <td style="text-align:right;"> 0.0404456 </td> <td style="text-align:right;"> 0.1192422 </td> <td style="text-align:right;"> 0.0027995 </td> <td style="text-align:right;"> -0.0034907 </td> <td style="text-align:right;"> 0.0090898 </td> <td style="text-align:right;"> 0.0032094 </td> </tr> <tr> <td style="text-align:left;"> locomotor activity </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Combined SHIRPA and Dysmorphology </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0960106 </td> <td style="text-align:right;"> 0.0224214 </td> <td style="text-align:right;"> 0.1695997 </td> <td style="text-align:right;"> 0.0375462 </td> <td style="text-align:right;"> -0.0159064 </td> <td style="text-align:right;"> -0.0579694 </td> <td style="text-align:right;"> 0.0261566 </td> <td style="text-align:right;"> 0.0214611 </td> <td style="text-align:right;"> -0.1105803 </td> <td style="text-align:right;"> -0.1761043 </td> <td style="text-align:right;"> -0.0450562 </td> <td style="text-align:right;"> 0.0334313 </td> </tr> <tr> <td style="text-align:left;"> lvawd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0228924 </td> <td style="text-align:right;"> -0.0247048 </td> <td style="text-align:right;"> 0.0704896 </td> <td style="text-align:right;"> 0.0242847 </td> <td style="text-align:right;"> 0.0454075 </td> <td style="text-align:right;"> -0.0013249 </td> <td style="text-align:right;"> 0.0921399 </td> <td style="text-align:right;"> 0.0238435 </td> <td style="text-align:right;"> 0.0246614 </td> <td style="text-align:right;"> 0.0114095 </td> <td style="text-align:right;"> 0.0379132 </td> <td style="text-align:right;"> 0.0067613 </td> </tr> <tr> <td style="text-align:left;"> lvaws </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0017749 </td> <td style="text-align:right;"> -0.2517581 </td> <td style="text-align:right;"> 0.2482083 </td> <td style="text-align:right;"> 0.1275448 </td> <td style="text-align:right;"> 0.0232601 </td> <td style="text-align:right;"> -0.1776617 </td> <td style="text-align:right;"> 0.2241819 </td> <td style="text-align:right;"> 0.1025130 </td> <td style="text-align:right;"> 0.0112569 </td> <td style="text-align:right;"> -0.0306073 </td> <td style="text-align:right;"> 0.0531211 </td> <td style="text-align:right;"> 0.0213597 </td> </tr> <tr> <td style="text-align:left;"> lvidd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0453256 </td> <td style="text-align:right;"> -0.0241892 </td> <td style="text-align:right;"> 0.1148405 </td> <td style="text-align:right;"> 0.0354674 </td> <td style="text-align:right;"> 0.0981450 </td> <td style="text-align:right;"> 0.0208146 </td> <td style="text-align:right;"> 0.1754754 </td> <td style="text-align:right;"> 0.0394550 </td> <td style="text-align:right;"> 0.0528053 </td> <td style="text-align:right;"> 0.0378669 </td> <td style="text-align:right;"> 0.0677436 </td> <td style="text-align:right;"> 0.0076218 </td> </tr> <tr> <td style="text-align:left;"> lvids </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0635228 </td> <td style="text-align:right;"> -0.1990947 </td> <td style="text-align:right;"> 0.0720491 </td> <td style="text-align:right;"> 0.0691706 </td> <td style="text-align:right;"> 0.0083352 </td> <td style="text-align:right;"> -0.1335894 </td> <td style="text-align:right;"> 0.1502598 </td> <td style="text-align:right;"> 0.0724118 </td> <td style="text-align:right;"> 0.0756177 </td> <td style="text-align:right;"> 0.0525777 </td> <td style="text-align:right;"> 0.0986576 </td> <td style="text-align:right;"> 0.0117553 </td> </tr> <tr> <td style="text-align:left;"> lvpwd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0317376 </td> <td style="text-align:right;"> -0.1258062 </td> <td style="text-align:right;"> 0.0623311 </td> <td style="text-align:right;"> 0.0479951 </td> <td style="text-align:right;"> -0.0104248 </td> <td style="text-align:right;"> -0.1271922 </td> <td style="text-align:right;"> 0.1063426 </td> <td style="text-align:right;"> 0.0595763 </td> <td style="text-align:right;"> 0.0302674 </td> <td style="text-align:right;"> 0.0131900 </td> <td style="text-align:right;"> 0.0473448 </td> <td style="text-align:right;"> 0.0087131 </td> </tr> <tr> <td style="text-align:left;"> lvpws </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0190522 </td> <td style="text-align:right;"> -0.1014670 </td> <td style="text-align:right;"> 0.0633627 </td> <td style="text-align:right;"> 0.0420492 </td> <td style="text-align:right;"> 0.0089592 </td> <td style="text-align:right;"> -0.0823356 </td> <td style="text-align:right;"> 0.1002540 </td> <td style="text-align:right;"> 0.0465798 </td> <td style="text-align:right;"> 0.0268487 </td> <td style="text-align:right;"> 0.0063146 </td> <td style="text-align:right;"> 0.0473828 </td> <td style="text-align:right;"> 0.0104768 </td> </tr> <tr> <td style="text-align:left;"> magnesium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Urinalysis </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0161699 </td> <td style="text-align:right;"> -0.0231196 </td> <td style="text-align:right;"> 0.0554593 </td> <td style="text-align:right;"> 0.0200460 </td> <td style="text-align:right;"> -0.0513056 </td> <td style="text-align:right;"> -0.1167021 </td> <td style="text-align:right;"> 0.0140909 </td> <td style="text-align:right;"> 0.0333662 </td> <td style="text-align:right;"> -0.0413354 </td> <td style="text-align:right;"> -0.1135580 </td> <td style="text-align:right;"> 0.0308871 </td> <td style="text-align:right;"> 0.0368489 </td> </tr> <tr> <td style="text-align:left;"> mean cell hemoglobin concentration </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0378015 </td> <td style="text-align:right;"> -0.0880637 </td> <td style="text-align:right;"> 0.1636666 </td> <td style="text-align:right;"> 0.0642181 </td> <td style="text-align:right;"> 0.0253063 </td> <td style="text-align:right;"> -0.1086076 </td> <td style="text-align:right;"> 0.1592202 </td> <td style="text-align:right;"> 0.0683247 </td> <td style="text-align:right;"> -0.0113450 </td> <td style="text-align:right;"> -0.0150702 </td> <td style="text-align:right;"> -0.0076199 </td> <td style="text-align:right;"> 0.0019006 </td> </tr> <tr> <td style="text-align:left;"> mean cell volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0039175 </td> <td style="text-align:right;"> -0.0957495 </td> <td style="text-align:right;"> 0.1035845 </td> <td style="text-align:right;"> 0.0508514 </td> <td style="text-align:right;"> -0.0030447 </td> <td style="text-align:right;"> -0.0961742 </td> <td style="text-align:right;"> 0.0900848 </td> <td style="text-align:right;"> 0.0475159 </td> <td style="text-align:right;"> -0.0063502 </td> <td style="text-align:right;"> -0.0099649 </td> <td style="text-align:right;"> -0.0027355 </td> <td style="text-align:right;"> 0.0018443 </td> </tr> <tr> <td style="text-align:left;"> mean corpuscular hemoglobin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0025833 </td> <td style="text-align:right;"> -0.0653065 </td> <td style="text-align:right;"> 0.0601398 </td> <td style="text-align:right;"> 0.0320022 </td> <td style="text-align:right;"> -0.0193465 </td> <td style="text-align:right;"> -0.0824670 </td> <td style="text-align:right;"> 0.0437741 </td> <td style="text-align:right;"> 0.0322049 </td> <td style="text-align:right;"> -0.0169768 </td> <td style="text-align:right;"> -0.0197231 </td> <td style="text-align:right;"> -0.0142305 </td> <td style="text-align:right;"> 0.0014012 </td> </tr> <tr> <td style="text-align:left;"> mean platelet volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0487366 </td> <td style="text-align:right;"> -0.0044688 </td> <td style="text-align:right;"> 0.1019419 </td> <td style="text-align:right;"> 0.0271461 </td> <td style="text-align:right;"> 0.0353913 </td> <td style="text-align:right;"> -0.0210323 </td> <td style="text-align:right;"> 0.0918150 </td> <td style="text-align:right;"> 0.0287881 </td> <td style="text-align:right;"> -0.0174066 </td> <td style="text-align:right;"> -0.0276044 </td> <td style="text-align:right;"> -0.0072089 </td> <td style="text-align:right;"> 0.0052030 </td> </tr> <tr> <td style="text-align:left;"> mean r amplitude </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0084703 </td> <td style="text-align:right;"> -0.0282092 </td> <td style="text-align:right;"> 0.0451499 </td> <td style="text-align:right;"> 0.0187144 </td> <td style="text-align:right;"> -0.0948208 </td> <td style="text-align:right;"> -0.1630495 </td> <td style="text-align:right;"> -0.0265922 </td> <td style="text-align:right;"> 0.0348112 </td> <td style="text-align:right;"> -0.0835612 </td> <td style="text-align:right;"> -0.1503108 </td> <td style="text-align:right;"> -0.0168116 </td> <td style="text-align:right;"> 0.0340565 </td> </tr> <tr> <td style="text-align:left;"> mean sr amplitude </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0284617 </td> <td style="text-align:right;"> -0.0131943 </td> <td style="text-align:right;"> 0.0701178 </td> <td style="text-align:right;"> 0.0212535 </td> <td style="text-align:right;"> -0.0876811 </td> <td style="text-align:right;"> -0.1270777 </td> <td style="text-align:right;"> -0.0482845 </td> <td style="text-align:right;"> 0.0201007 </td> <td style="text-align:right;"> -0.1130259 </td> <td style="text-align:right;"> -0.1558048 </td> <td style="text-align:right;"> -0.0702470 </td> <td style="text-align:right;"> 0.0218264 </td> </tr> <tr> <td style="text-align:left;"> number of center entries </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0150703 </td> <td style="text-align:right;"> -0.0534907 </td> <td style="text-align:right;"> 0.0836313 </td> <td style="text-align:right;"> 0.0349807 </td> <td style="text-align:right;"> -0.0361259 </td> <td style="text-align:right;"> -0.0952472 </td> <td style="text-align:right;"> 0.0229955 </td> <td style="text-align:right;"> 0.0301645 </td> <td style="text-align:right;"> -0.0588092 </td> <td style="text-align:right;"> -0.1679907 </td> <td style="text-align:right;"> 0.0503723 </td> <td style="text-align:right;"> 0.0557059 </td> </tr> <tr> <td style="text-align:left;"> number of rears - total </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0011326 </td> <td style="text-align:right;"> -0.1141113 </td> <td style="text-align:right;"> 0.1118461 </td> <td style="text-align:right;"> 0.0576432 </td> <td style="text-align:right;"> 0.1869490 </td> <td style="text-align:right;"> -0.0392422 </td> <td style="text-align:right;"> 0.4131402 </td> <td style="text-align:right;"> 0.1154058 </td> <td style="text-align:right;"> 0.1794328 </td> <td style="text-align:right;"> 0.0568682 </td> <td style="text-align:right;"> 0.3019974 </td> <td style="text-align:right;"> 0.0625341 </td> </tr> <tr> <td style="text-align:left;"> others </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1684902 </td> <td style="text-align:right;"> -0.2596648 </td> <td style="text-align:right;"> -0.0773156 </td> <td style="text-align:right;"> 0.0465185 </td> <td style="text-align:right;"> -0.1515195 </td> <td style="text-align:right;"> -0.2435956 </td> <td style="text-align:right;"> -0.0594434 </td> <td style="text-align:right;"> 0.0469785 </td> <td style="text-align:right;"> 0.0196158 </td> <td style="text-align:right;"> 0.0049349 </td> <td style="text-align:right;"> 0.0342967 </td> <td style="text-align:right;"> 0.0074904 </td> </tr> <tr> <td style="text-align:left;"> pdcs </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1732553 </td> <td style="text-align:right;"> -0.4003845 </td> <td style="text-align:right;"> 0.0538738 </td> <td style="text-align:right;"> 0.1158844 </td> <td style="text-align:right;"> -0.2572491 </td> <td style="text-align:right;"> -0.7186201 </td> <td style="text-align:right;"> 0.2041219 </td> <td style="text-align:right;"> 0.2353977 </td> <td style="text-align:right;"> -0.0915619 </td> <td style="text-align:right;"> -0.2522236 </td> <td style="text-align:right;"> 0.0690997 </td> <td style="text-align:right;"> 0.0819717 </td> </tr> <tr> <td style="text-align:left;"> percentage center time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0219679 </td> <td style="text-align:right;"> -0.0863184 </td> <td style="text-align:right;"> 0.0423826 </td> <td style="text-align:right;"> 0.0328325 </td> <td style="text-align:right;"> -0.0188907 </td> <td style="text-align:right;"> -0.0912088 </td> <td style="text-align:right;"> 0.0534274 </td> <td style="text-align:right;"> 0.0368977 </td> <td style="text-align:right;"> -0.0061802 </td> <td style="text-align:right;"> -0.0972542 </td> <td style="text-align:right;"> 0.0848938 </td> <td style="text-align:right;"> 0.0464672 </td> </tr> <tr> <td style="text-align:left;"> periphery average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0444272 </td> <td style="text-align:right;"> -0.1082870 </td> <td style="text-align:right;"> 0.0194327 </td> <td style="text-align:right;"> 0.0325822 </td> <td style="text-align:right;"> -0.1401304 </td> <td style="text-align:right;"> -0.2117709 </td> <td style="text-align:right;"> -0.0684898 </td> <td style="text-align:right;"> 0.0365520 </td> <td style="text-align:right;"> -0.0963838 </td> <td style="text-align:right;"> -0.1446043 </td> <td style="text-align:right;"> -0.0481633 </td> <td style="text-align:right;"> 0.0246028 </td> </tr> <tr> <td style="text-align:left;"> periphery distance travelled </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0313217 </td> <td style="text-align:right;"> -0.0918314 </td> <td style="text-align:right;"> 0.0291879 </td> <td style="text-align:right;"> 0.0308728 </td> <td style="text-align:right;"> -0.1342236 </td> <td style="text-align:right;"> -0.1874097 </td> <td style="text-align:right;"> -0.0810376 </td> <td style="text-align:right;"> 0.0271362 </td> <td style="text-align:right;"> -0.1037239 </td> <td style="text-align:right;"> -0.1714836 </td> <td style="text-align:right;"> -0.0359643 </td> <td style="text-align:right;"> 0.0345719 </td> </tr> <tr> <td style="text-align:left;"> periphery permanence time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0369177 </td> <td style="text-align:right;"> -0.1277076 </td> <td style="text-align:right;"> 0.0538721 </td> <td style="text-align:right;"> 0.0463222 </td> <td style="text-align:right;"> -0.0294978 </td> <td style="text-align:right;"> -0.1006346 </td> <td style="text-align:right;"> 0.0416390 </td> <td style="text-align:right;"> 0.0362950 </td> <td style="text-align:right;"> 0.0077038 </td> <td style="text-align:right;"> -0.0137850 </td> <td style="text-align:right;"> 0.0291927 </td> <td style="text-align:right;"> 0.0109639 </td> </tr> <tr> <td style="text-align:left;"> periphery resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0536346 </td> <td style="text-align:right;"> -0.1266045 </td> <td style="text-align:right;"> 0.0193353 </td> <td style="text-align:right;"> 0.0372302 </td> <td style="text-align:right;"> -0.0572459 </td> <td style="text-align:right;"> -0.1071515 </td> <td style="text-align:right;"> -0.0073404 </td> <td style="text-align:right;"> 0.0254625 </td> <td style="text-align:right;"> 0.0026007 </td> <td style="text-align:right;"> -0.0558538 </td> <td style="text-align:right;"> 0.0610552 </td> <td style="text-align:right;"> 0.0298243 </td> </tr> <tr> <td style="text-align:left;"> phosphorus </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0485897 </td> <td style="text-align:right;"> -0.0839101 </td> <td style="text-align:right;"> -0.0132693 </td> <td style="text-align:right;"> 0.0180209 </td> <td style="text-align:right;"> -0.0826120 </td> <td style="text-align:right;"> -0.1576473 </td> <td style="text-align:right;"> -0.0075767 </td> <td style="text-align:right;"> 0.0382840 </td> <td style="text-align:right;"> -0.0420616 </td> <td style="text-align:right;"> -0.0813582 </td> <td style="text-align:right;"> -0.0027650 </td> <td style="text-align:right;"> 0.0200497 </td> </tr> <tr> <td style="text-align:left;"> platelet count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0737198 </td> <td style="text-align:right;"> 0.0205862 </td> <td style="text-align:right;"> 0.1268534 </td> <td style="text-align:right;"> 0.0271095 </td> <td style="text-align:right;"> 0.2415135 </td> <td style="text-align:right;"> 0.1865330 </td> <td style="text-align:right;"> 0.2964940 </td> <td style="text-align:right;"> 0.0280518 </td> <td style="text-align:right;"> 0.1642192 </td> <td style="text-align:right;"> 0.1369820 </td> <td style="text-align:right;"> 0.1914563 </td> <td style="text-align:right;"> 0.0138968 </td> </tr> <tr> <td style="text-align:left;"> pnn5(6>ms) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.2906905 </td> <td style="text-align:right;"> 0.1716202 </td> <td style="text-align:right;"> 0.4097607 </td> <td style="text-align:right;"> 0.0607512 </td> <td style="text-align:right;"> -0.2926013 </td> <td style="text-align:right;"> -0.5272121 </td> <td style="text-align:right;"> -0.0579905 </td> <td style="text-align:right;"> 0.1197016 </td> <td style="text-align:right;"> -0.6004767 </td> <td style="text-align:right;"> -0.9244113 </td> <td style="text-align:right;"> -0.2765420 </td> <td style="text-align:right;"> 0.1652758 </td> </tr> <tr> <td style="text-align:left;"> potassium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0705522 </td> <td style="text-align:right;"> -0.2214989 </td> <td style="text-align:right;"> 0.0803945 </td> <td style="text-align:right;"> 0.0770150 </td> <td style="text-align:right;"> -0.0074675 </td> <td style="text-align:right;"> -0.1729366 </td> <td style="text-align:right;"> 0.1580015 </td> <td style="text-align:right;"> 0.0844245 </td> <td style="text-align:right;"> 0.0704162 </td> <td style="text-align:right;"> 0.0476647 </td> <td style="text-align:right;"> 0.0931676 </td> <td style="text-align:right;"> 0.0116081 </td> </tr> <tr> <td style="text-align:left;"> pq </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0650960 </td> <td style="text-align:right;"> -0.1538776 </td> <td style="text-align:right;"> 0.0236857 </td> <td style="text-align:right;"> 0.0452976 </td> <td style="text-align:right;"> -0.0648322 </td> <td style="text-align:right;"> -0.1270688 </td> <td style="text-align:right;"> -0.0025955 </td> <td style="text-align:right;"> 0.0317540 </td> <td style="text-align:right;"> 0.0015656 </td> <td style="text-align:right;"> -0.0259865 </td> <td style="text-align:right;"> 0.0291178 </td> <td style="text-align:right;"> 0.0140575 </td> </tr> <tr> <td style="text-align:left;"> pr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0564860 </td> <td style="text-align:right;"> -0.1048371 </td> <td style="text-align:right;"> -0.0081349 </td> <td style="text-align:right;"> 0.0246694 </td> <td style="text-align:right;"> -0.0754718 </td> <td style="text-align:right;"> -0.1235224 </td> <td style="text-align:right;"> -0.0274213 </td> <td style="text-align:right;"> 0.0245160 </td> <td style="text-align:right;"> -0.0183785 </td> <td style="text-align:right;"> -0.0319887 </td> <td style="text-align:right;"> -0.0047684 </td> <td style="text-align:right;"> 0.0069441 </td> </tr> <tr> <td style="text-align:left;"> qrs </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0725454 </td> <td style="text-align:right;"> 0.0354722 </td> <td style="text-align:right;"> 0.1096185 </td> <td style="text-align:right;"> 0.0189152 </td> <td style="text-align:right;"> 0.0681074 </td> <td style="text-align:right;"> 0.0300869 </td> <td style="text-align:right;"> 0.1061278 </td> <td style="text-align:right;"> 0.0193986 </td> <td style="text-align:right;"> -0.0054233 </td> <td style="text-align:right;"> -0.0154885 </td> <td style="text-align:right;"> 0.0046418 </td> <td style="text-align:right;"> 0.0051354 </td> </tr> <tr> <td style="text-align:left;"> qtc </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0328106 </td> <td style="text-align:right;"> -0.0101032 </td> <td style="text-align:right;"> 0.0757244 </td> <td style="text-align:right;"> 0.0218952 </td> <td style="text-align:right;"> 0.0310473 </td> <td style="text-align:right;"> -0.0207365 </td> <td style="text-align:right;"> 0.0828310 </td> <td style="text-align:right;"> 0.0264208 </td> <td style="text-align:right;"> -0.0005046 </td> <td style="text-align:right;"> -0.0085696 </td> <td style="text-align:right;"> 0.0075604 </td> <td style="text-align:right;"> 0.0041149 </td> </tr> <tr> <td style="text-align:left;"> qtc dispersion </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0031258 </td> <td style="text-align:right;"> -0.0523919 </td> <td style="text-align:right;"> 0.0586435 </td> <td style="text-align:right;"> 0.0283259 </td> <td style="text-align:right;"> -0.0046501 </td> <td style="text-align:right;"> -0.1060530 </td> <td style="text-align:right;"> 0.0967528 </td> <td style="text-align:right;"> 0.0517371 </td> <td style="text-align:right;"> -0.0077373 </td> <td style="text-align:right;"> -0.0510162 </td> <td style="text-align:right;"> 0.0355416 </td> <td style="text-align:right;"> 0.0220815 </td> </tr> <tr> <td style="text-align:left;"> red blood cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0773455 </td> <td style="text-align:right;"> 0.0071933 </td> <td style="text-align:right;"> 0.1474977 </td> <td style="text-align:right;"> 0.0357926 </td> <td style="text-align:right;"> 0.0997278 </td> <td style="text-align:right;"> 0.0316996 </td> <td style="text-align:right;"> 0.1677560 </td> <td style="text-align:right;"> 0.0347089 </td> <td style="text-align:right;"> 0.0228493 </td> <td style="text-align:right;"> 0.0088583 </td> <td style="text-align:right;"> 0.0368404 </td> <td style="text-align:right;"> 0.0071384 </td> </tr> <tr> <td style="text-align:left;"> red blood cell distribution width </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.1248464 </td> <td style="text-align:right;"> -0.0035148 </td> <td style="text-align:right;"> 0.2532076 </td> <td style="text-align:right;"> 0.0654916 </td> <td style="text-align:right;"> 0.1353460 </td> <td style="text-align:right;"> -0.0035862 </td> <td style="text-align:right;"> 0.2742782 </td> <td style="text-align:right;"> 0.0708851 </td> <td style="text-align:right;"> 0.0104789 </td> <td style="text-align:right;"> -0.0032056 </td> <td style="text-align:right;"> 0.0241635 </td> <td style="text-align:right;"> 0.0069821 </td> </tr> <tr> <td style="text-align:left;"> respiration rate </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.1384843 </td> <td style="text-align:right;"> -0.2178736 </td> <td style="text-align:right;"> -0.0590950 </td> <td style="text-align:right;"> 0.0405055 </td> <td style="text-align:right;"> -0.0703570 </td> <td style="text-align:right;"> -0.1795875 </td> <td style="text-align:right;"> 0.0388735 </td> <td style="text-align:right;"> 0.0557309 </td> <td style="text-align:right;"> 0.0611034 </td> <td style="text-align:right;"> 0.0227141 </td> <td style="text-align:right;"> 0.0994926 </td> <td style="text-align:right;"> 0.0195867 </td> </tr> <tr> <td style="text-align:left;"> respiratory exchange ratio </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.0116565 </td> <td style="text-align:right;"> -0.0896490 </td> <td style="text-align:right;"> 0.0663361 </td> <td style="text-align:right;"> 0.0397928 </td> <td style="text-align:right;"> -0.0106530 </td> <td style="text-align:right;"> -0.0878483 </td> <td style="text-align:right;"> 0.0665424 </td> <td style="text-align:right;"> 0.0393861 </td> <td style="text-align:right;"> 0.0017027 </td> <td style="text-align:right;"> -0.0057348 </td> <td style="text-align:right;"> 0.0091402 </td> <td style="text-align:right;"> 0.0037947 </td> </tr> <tr> <td style="text-align:left;"> right anterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.4491432 </td> <td style="text-align:right;"> -1.3293546 </td> <td style="text-align:right;"> 0.4310682 </td> <td style="text-align:right;"> 0.4490957 </td> <td style="text-align:right;"> -0.4157377 </td> <td style="text-align:right;"> -1.2918620 </td> <td style="text-align:right;"> 0.4603867 </td> <td style="text-align:right;"> 0.4470104 </td> <td style="text-align:right;"> 0.0316098 </td> <td style="text-align:right;"> 0.0264512 </td> <td style="text-align:right;"> 0.0367685 </td> <td style="text-align:right;"> 0.0026320 </td> </tr> <tr> <td style="text-align:left;"> right corneal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.0355898 </td> <td style="text-align:right;"> -0.2280522 </td> <td style="text-align:right;"> 0.1568726 </td> <td style="text-align:right;"> 0.0981969 </td> <td style="text-align:right;"> -0.0306550 </td> <td style="text-align:right;"> -0.1963692 </td> <td style="text-align:right;"> 0.1350592 </td> <td style="text-align:right;"> 0.0845496 </td> <td style="text-align:right;"> -0.0013855 </td> <td style="text-align:right;"> -0.0237830 </td> <td style="text-align:right;"> 0.0210121 </td> <td style="text-align:right;"> 0.0114275 </td> </tr> <tr> <td style="text-align:left;"> right inner nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.2545083 </td> <td style="text-align:right;"> -0.7633116 </td> <td style="text-align:right;"> 0.2542949 </td> <td style="text-align:right;"> 0.2595983 </td> <td style="text-align:right;"> -0.2785114 </td> <td style="text-align:right;"> -0.8373133 </td> <td style="text-align:right;"> 0.2802906 </td> <td style="text-align:right;"> 0.2851083 </td> <td style="text-align:right;"> -0.0175090 </td> <td style="text-align:right;"> -0.0664158 </td> <td style="text-align:right;"> 0.0313978 </td> <td style="text-align:right;"> 0.0249529 </td> </tr> <tr> <td style="text-align:left;"> right outer nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> 0.0061253 </td> <td style="text-align:right;"> -0.0781241 </td> <td style="text-align:right;"> 0.0903746 </td> <td style="text-align:right;"> 0.0429851 </td> <td style="text-align:right;"> 0.0109098 </td> <td style="text-align:right;"> -0.0731427 </td> <td style="text-align:right;"> 0.0949622 </td> <td style="text-align:right;"> 0.0428847 </td> <td style="text-align:right;"> 0.0055513 </td> <td style="text-align:right;"> 0.0000519 </td> <td style="text-align:right;"> 0.0110508 </td> <td style="text-align:right;"> 0.0028059 </td> </tr> <tr> <td style="text-align:left;"> right posterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.0775673 </td> <td style="text-align:right;"> -0.2905688 </td> <td style="text-align:right;"> 0.1354341 </td> <td style="text-align:right;"> 0.1086762 </td> <td style="text-align:right;"> -0.0764571 </td> <td style="text-align:right;"> -0.2893152 </td> <td style="text-align:right;"> 0.1364010 </td> <td style="text-align:right;"> 0.1086031 </td> <td style="text-align:right;"> 0.0071990 </td> <td style="text-align:right;"> -0.0178434 </td> <td style="text-align:right;"> 0.0322413 </td> <td style="text-align:right;"> 0.0127769 </td> </tr> <tr> <td style="text-align:left;"> right total retinal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.1987993 </td> <td style="text-align:right;"> -0.6457320 </td> <td style="text-align:right;"> 0.2481333 </td> <td style="text-align:right;"> 0.2280310 </td> <td style="text-align:right;"> -0.1925482 </td> <td style="text-align:right;"> -0.6285715 </td> <td style="text-align:right;"> 0.2434750 </td> <td style="text-align:right;"> 0.2224649 </td> <td style="text-align:right;"> 0.0052882 </td> <td style="text-align:right;"> -0.0045957 </td> <td style="text-align:right;"> 0.0151720 </td> <td style="text-align:right;"> 0.0050429 </td> </tr> <tr> <td style="text-align:left;"> rmssd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.1800273 </td> <td style="text-align:right;"> -0.0882317 </td> <td style="text-align:right;"> 0.4482864 </td> <td style="text-align:right;"> 0.1368694 </td> <td style="text-align:right;"> -0.0161048 </td> <td style="text-align:right;"> -0.4112809 </td> <td style="text-align:right;"> 0.3790712 </td> <td style="text-align:right;"> 0.2016241 </td> <td style="text-align:right;"> -0.1178703 </td> <td style="text-align:right;"> -0.2449843 </td> <td style="text-align:right;"> 0.0092436 </td> <td style="text-align:right;"> 0.0648552 </td> </tr> <tr> <td style="text-align:left;"> rp macrophage (cd19- cd11c-) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0765771 </td> <td style="text-align:right;"> -0.3398075 </td> <td style="text-align:right;"> 0.1866533 </td> <td style="text-align:right;"> 0.1343037 </td> <td style="text-align:right;"> -0.0747691 </td> <td style="text-align:right;"> -0.3351316 </td> <td style="text-align:right;"> 0.1855933 </td> <td style="text-align:right;"> 0.1328404 </td> <td style="text-align:right;"> -0.0746396 </td> <td style="text-align:right;"> -0.2072980 </td> <td style="text-align:right;"> 0.0580188 </td> <td style="text-align:right;"> 0.0676841 </td> </tr> <tr> <td style="text-align:left;"> rr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0761505 </td> <td style="text-align:right;"> -0.1876687 </td> <td style="text-align:right;"> 0.0353678 </td> <td style="text-align:right;"> 0.0568981 </td> <td style="text-align:right;"> -0.0896869 </td> <td style="text-align:right;"> -0.2063458 </td> <td style="text-align:right;"> 0.0269721 </td> <td style="text-align:right;"> 0.0595210 </td> <td style="text-align:right;"> -0.0125023 </td> <td style="text-align:right;"> -0.0214082 </td> <td style="text-align:right;"> -0.0035963 </td> <td style="text-align:right;"> 0.0045440 </td> </tr> <tr> <td style="text-align:left;"> sodium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0262100 </td> <td style="text-align:right;"> -0.1171674 </td> <td style="text-align:right;"> 0.1695873 </td> <td style="text-align:right;"> 0.0731531 </td> <td style="text-align:right;"> 0.0338228 </td> <td style="text-align:right;"> -0.1337162 </td> <td style="text-align:right;"> 0.2013618 </td> <td style="text-align:right;"> 0.0854806 </td> <td style="text-align:right;"> 0.0099680 </td> <td style="text-align:right;"> 0.0065815 </td> <td style="text-align:right;"> 0.0133545 </td> <td style="text-align:right;"> 0.0017278 </td> </tr> <tr> <td style="text-align:left;"> spleen weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> 0.1874259 </td> <td style="text-align:right;"> -0.0500875 </td> <td style="text-align:right;"> 0.4249393 </td> <td style="text-align:right;"> 0.1211825 </td> <td style="text-align:right;"> 0.1133706 </td> <td style="text-align:right;"> -0.1604807 </td> <td style="text-align:right;"> 0.3872220 </td> <td style="text-align:right;"> 0.1397227 </td> <td style="text-align:right;"> -0.1542349 </td> <td style="text-align:right;"> -0.2104415 </td> <td style="text-align:right;"> -0.0980283 </td> <td style="text-align:right;"> 0.0286774 </td> </tr> <tr> <td style="text-align:left;"> st </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0032888 </td> <td style="text-align:right;"> -0.0544512 </td> <td style="text-align:right;"> 0.0610288 </td> <td style="text-align:right;"> 0.0294597 </td> <td style="text-align:right;"> -0.0054976 </td> <td style="text-align:right;"> -0.0811810 </td> <td style="text-align:right;"> 0.0701858 </td> <td style="text-align:right;"> 0.0386147 </td> <td style="text-align:right;"> -0.0034902 </td> <td style="text-align:right;"> -0.0175917 </td> <td style="text-align:right;"> 0.0106113 </td> <td style="text-align:right;"> 0.0071948 </td> </tr> <tr> <td style="text-align:left;"> stroke volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0594276 </td> <td style="text-align:right;"> -0.0782445 </td> <td style="text-align:right;"> 0.1970997 </td> <td style="text-align:right;"> 0.0702422 </td> <td style="text-align:right;"> 0.1574330 </td> <td style="text-align:right;"> 0.0091891 </td> <td style="text-align:right;"> 0.3056769 </td> <td style="text-align:right;"> 0.0756360 </td> <td style="text-align:right;"> 0.0937375 </td> <td style="text-align:right;"> 0.0775587 </td> <td style="text-align:right;"> 0.1099162 </td> <td style="text-align:right;"> 0.0082546 </td> </tr> <tr> <td style="text-align:left;"> tibia length </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> -0.1475403 </td> <td style="text-align:right;"> -0.4396127 </td> <td style="text-align:right;"> 0.1445320 </td> <td style="text-align:right;"> 0.1490192 </td> <td style="text-align:right;"> -0.1374401 </td> <td style="text-align:right;"> -0.4261352 </td> <td style="text-align:right;"> 0.1512551 </td> <td style="text-align:right;"> 0.1472961 </td> <td style="text-align:right;"> 0.0095199 </td> <td style="text-align:right;"> 0.0059199 </td> <td style="text-align:right;"> 0.0131200 </td> <td style="text-align:right;"> 0.0018368 </td> </tr> <tr> <td style="text-align:left;"> total bilirubin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0605449 </td> <td style="text-align:right;"> -0.0097669 </td> <td style="text-align:right;"> 0.1308567 </td> <td style="text-align:right;"> 0.0358740 </td> <td style="text-align:right;"> 0.0022671 </td> <td style="text-align:right;"> -0.0859910 </td> <td style="text-align:right;"> 0.0905252 </td> <td style="text-align:right;"> 0.0450305 </td> <td style="text-align:right;"> -0.0550333 </td> <td style="text-align:right;"> -0.0979518 </td> <td style="text-align:right;"> -0.0121148 </td> <td style="text-align:right;"> 0.0218976 </td> </tr> <tr> <td style="text-align:left;"> total cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0942595 </td> <td style="text-align:right;"> -0.0751596 </td> <td style="text-align:right;"> 0.2636786 </td> <td style="text-align:right;"> 0.0864399 </td> <td style="text-align:right;"> 0.3142208 </td> <td style="text-align:right;"> 0.1125613 </td> <td style="text-align:right;"> 0.5158803 </td> <td style="text-align:right;"> 0.1028894 </td> <td style="text-align:right;"> 0.2027583 </td> <td style="text-align:right;"> 0.1750477 </td> <td style="text-align:right;"> 0.2304688 </td> <td style="text-align:right;"> 0.0141383 </td> </tr> <tr> <td style="text-align:left;"> total food intake </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.1192293 </td> <td style="text-align:right;"> -0.2542902 </td> <td style="text-align:right;"> 0.0158316 </td> <td style="text-align:right;"> 0.0689099 </td> <td style="text-align:right;"> -0.0964842 </td> <td style="text-align:right;"> -0.2564912 </td> <td style="text-align:right;"> 0.0635228 </td> <td style="text-align:right;"> 0.0816377 </td> <td style="text-align:right;"> 0.0267691 </td> <td style="text-align:right;"> -0.0233285 </td> <td style="text-align:right;"> 0.0768667 </td> <td style="text-align:right;"> 0.0255605 </td> </tr> <tr> <td style="text-align:left;"> total protein </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0422347 </td> <td style="text-align:right;"> -0.0623878 </td> <td style="text-align:right;"> -0.0220816 </td> <td style="text-align:right;"> 0.0102824 </td> <td style="text-align:right;"> -0.0355909 </td> <td style="text-align:right;"> -0.0619127 </td> <td style="text-align:right;"> -0.0092692 </td> <td style="text-align:right;"> 0.0134297 </td> <td style="text-align:right;"> 0.0092660 </td> <td style="text-align:right;"> -0.0008158 </td> <td style="text-align:right;"> 0.0193478 </td> <td style="text-align:right;"> 0.0051439 </td> </tr> <tr> <td style="text-align:left;"> total water intake </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.1457383 </td> <td style="text-align:right;"> -0.2373165 </td> <td style="text-align:right;"> -0.0541601 </td> <td style="text-align:right;"> 0.0467244 </td> <td style="text-align:right;"> -0.2097443 </td> <td style="text-align:right;"> -0.2681948 </td> <td style="text-align:right;"> -0.1512937 </td> <td style="text-align:right;"> 0.0298223 </td> <td style="text-align:right;"> -0.0654284 </td> <td style="text-align:right;"> -0.1374220 </td> <td style="text-align:right;"> 0.0065653 </td> <td style="text-align:right;"> 0.0367321 </td> </tr> <tr> <td style="text-align:left;"> triglycerides </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0320020 </td> <td style="text-align:right;"> -0.1233659 </td> <td style="text-align:right;"> 0.0593619 </td> <td style="text-align:right;"> 0.0466151 </td> <td style="text-align:right;"> 0.3268957 </td> <td style="text-align:right;"> 0.2087111 </td> <td style="text-align:right;"> 0.4450803 </td> <td style="text-align:right;"> 0.0602994 </td> <td style="text-align:right;"> 0.3473552 </td> <td style="text-align:right;"> 0.2592006 </td> <td style="text-align:right;"> 0.4355098 </td> <td style="text-align:right;"> 0.0449777 </td> </tr> <tr> <td style="text-align:left;"> urea (blood urea nitrogen - bun) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.1405306 </td> <td style="text-align:right;"> -0.2664120 </td> <td style="text-align:right;"> -0.0146491 </td> <td style="text-align:right;"> 0.0642264 </td> <td style="text-align:right;"> -0.0950040 </td> <td style="text-align:right;"> -0.2507897 </td> <td style="text-align:right;"> 0.0607817 </td> <td style="text-align:right;"> 0.0794840 </td> <td style="text-align:right;"> 0.0403162 </td> <td style="text-align:right;"> 0.0051883 </td> <td style="text-align:right;"> 0.0754441 </td> <td style="text-align:right;"> 0.0179227 </td> </tr> <tr> <td style="text-align:left;"> uric acid </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0367062 </td> <td style="text-align:right;"> -0.0660619 </td> <td style="text-align:right;"> 0.1394744 </td> <td style="text-align:right;"> 0.0524337 </td> <td style="text-align:right;"> 0.3626957 </td> <td style="text-align:right;"> 0.0914512 </td> <td style="text-align:right;"> 0.6339402 </td> <td style="text-align:right;"> 0.1383926 </td> <td style="text-align:right;"> 0.4472349 </td> <td style="text-align:right;"> -0.0801891 </td> <td style="text-align:right;"> 0.9746588 </td> <td style="text-align:right;"> 0.2690988 </td> </tr> <tr> <td style="text-align:left;"> white blood cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0907957 </td> <td style="text-align:right;"> -0.1703063 </td> <td style="text-align:right;"> -0.0112852 </td> <td style="text-align:right;"> 0.0405673 </td> <td style="text-align:right;"> 0.1168446 </td> <td style="text-align:right;"> -0.0023934 </td> <td style="text-align:right;"> 0.2360826 </td> <td style="text-align:right;"> 0.0608368 </td> <td style="text-align:right;"> 0.1978876 </td> <td style="text-align:right;"> 0.1368305 </td> <td style="text-align:right;"> 0.2589447 </td> <td style="text-align:right;"> 0.0311521 </td> </tr> <tr> <td style="text-align:left;"> whole arena average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0156634 </td> <td style="text-align:right;"> -0.0857564 </td> <td style="text-align:right;"> 0.0544296 </td> <td style="text-align:right;"> 0.0357624 </td> <td style="text-align:right;"> -0.1140149 </td> <td style="text-align:right;"> -0.1840029 </td> <td style="text-align:right;"> -0.0440269 </td> <td style="text-align:right;"> 0.0357088 </td> <td style="text-align:right;"> -0.0997437 </td> <td style="text-align:right;"> -0.1519566 </td> <td style="text-align:right;"> -0.0475307 </td> <td style="text-align:right;"> 0.0266397 </td> </tr> <tr> <td style="text-align:left;"> whole arena resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0531307 </td> <td style="text-align:right;"> -0.1011672 </td> <td style="text-align:right;"> -0.0050941 </td> <td style="text-align:right;"> 0.0245089 </td> <td style="text-align:right;"> -0.0593672 </td> <td style="text-align:right;"> -0.1076067 </td> <td style="text-align:right;"> -0.0111276 </td> <td style="text-align:right;"> 0.0246125 </td> <td style="text-align:right;"> 0.0045878 </td> <td style="text-align:right;"> -0.0513396 </td> <td style="text-align:right;"> 0.0605152 </td> <td style="text-align:right;"> 0.0285349 </td> </tr> </tbody> </table></div> <!-- rnb-htmlwidget-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuXG4jIHRyYWl0X21ldGFfcmVzdWx0cyA8LSB3cml0ZS5jc3YobWV0YWNvbWJvLCBmaWxlID0gXCJleHBvcnQvdHJhaXRfbWV0YV9yZXN1bHRzLmNzdlwiKSAgI0ZlbGl4IDcvMi8yMDIwOiBJIHRoaW5rIHRoaXMgY2FuIGJlIGRlbGV0ZWQgZm9yIHB1YmxpY2F0aW9uIVxuYGBgIn0= --></p> <pre class="r"><code> # trait_meta_results <- write.csv(metacombo, file = "export/trait_meta_results.csv") #Felix 7/2/2020: I think this can be deleted for publication!</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> <div id="second-order-meta-analysis-for-functional-groups" class="section level2"> <h2>3. Second-order meta-analysis for functional groups</h2> <p>(Section H in Figure 3 in main article)</p> <div id="performing-meta-analyses-3-for-each-of-the-9-grouping-terms-lncvr-lnvr-lnrr" class="section level3"> <h3>Performing meta-analyses (3 for each of the 9 grouping terms: lnCVR, lnVR, lnRR)</h3> <div id="preparation-of-data" class="section level4"> <h4>Preparation of data</h4> <p>Nesting, calculating the number of parameters within each grouping term, and running the meta-analyses</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>metacombo_final <- metacombo %>% group_by(GroupingTerm) %>% nest() # **calculate number of parameters per grouping term metacombo_final <- metacombo_final %>% mutate(para_per_GroupingTerm = map_dbl(data, nrow)) # For all grouping terms metacombo_final_all <- metacombo %>% nest(data = everything()) # **Final fixed effects meta-analyses within grouping terms, with SE of the estimate overall1 <- metacombo_final %>% mutate( model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )), model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )), model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )) ) # **Final fixed effects meta-analyses ACROSS grouping terms, with SE of the estimate overall_all1 <- metacombo_final_all %>% mutate( model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )), model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )), model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )) )</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> <div id="re-structuring-the-data-for-each-grouping-term" class="section level3"> <h3>Re-structuring the data for each grouping term</h3> <p>We here delete unused variables, and select the respective effect sizes. Please note - the referencing of the cells does NOT depend on previous ordering of the data. This would only be affected if the output structure from metafor::rma.uni changes.</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>Behaviour <- overall1 %>% filter(., GroupingTerm == "Behaviour") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ) %>% select(., GroupingTerm, lnCVR:lnRR_se) Immunology <- overall1 %>% filter(., GroupingTerm == "Immunology") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ) %>% select(., GroupingTerm, lnCVR:lnRR_se) Hematology <- overall1 %>% filter(., GroupingTerm == "Hematology") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ) %>% select(., GroupingTerm, lnCVR:lnRR_se) Hearing <- overall1 %>% filter(., GroupingTerm == "Hearing") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ) %>% select(., GroupingTerm, lnCVR:lnRR_se) Physiology <- overall1 %>% filter(., GroupingTerm == "Physiology") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ) %>% select(., GroupingTerm, lnCVR:lnRR_se) Metabolism <- overall1 %>% filter(., GroupingTerm == "Metabolism") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ) %>% select(., GroupingTerm, lnCVR:lnRR_se) Morphology <- overall1 %>% filter(., GroupingTerm == "Morphology") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ) %>% select(., GroupingTerm, lnCVR:lnRR_se) Heart <- overall1 %>% filter(., GroupingTerm == "Heart") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ) %>% select(., GroupingTerm, lnCVR:lnRR_se) Eye <- overall1 %>% filter(., GroupingTerm == "Eye") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ) %>% select(., GroupingTerm, lnCVR:lnRR_se) All <- overall_all1 %>% mutate( lnCVR = .[[2]][[1]]$b, lnCVR_lower = .[[2]][[1]]$ci.lb, lnCVR_upper = .[[2]][[1]]$ci.ub, lnCVR_se = .[[2]][[1]]$se, lnVR = .[[3]][[1]]$b, lnVR_lower = .[[3]][[1]]$ci.lb, lnVR_upper = .[[3]][[1]]$ci.ub, lnVR_se = .[[3]][[1]]$se, lnRR = .[[4]][[1]]$b, lnRR_lower = .[[4]][[1]]$ci.lb, lnRR_upper = .[[4]][[1]]$ci.ub, lnRR_se = .[[4]][[1]]$se ) %>% select(., lnCVR:lnRR_se) All <- All %>% mutate(GroupingTerm = "All") overall2 <- bind_rows(Behaviour, Morphology, Metabolism, Physiology, Immunology, Hematology, Heart, Hearing, Eye, All) </code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> </div> <div id="visualisation" class="section level1"> <h1>Visualisation</h1> <div id="figure-4" class="section level2"> <h2>Figure 4</h2> <div id="preparation-for-plots-count-data-based-on-first-order-metamanalysis-results" class="section level4"> <h4>Preparation for plots: Count data, based on First-order metamanalysis results</h4> <p>This includes all separate eligible traits. Re-ordering of grouping terms</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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--> <pre class="r"><code> meta_clean$GroupingTerm <- factor(meta_clean$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye")) meta_clean$GroupingTerm <- factor(meta_clean$GroupingTerm, rev(levels(meta_clean$GroupingTerm))) # *Preparing data for all traits meta.plot2.all <- meta_clean %>% select(lnCVR, lnVR, lnRR, GroupingTerm) %>% arrange(GroupingTerm) meta.plot2.all.b <- gather(meta.plot2.all, trait, value, c(lnCVR, lnRR)) # lnVR has been removed here and in the steps below, as this is only included in the supplemental figure meta.plot2.all.b$trait <- factor(meta.plot2.all.b$trait, levels = c("lnCVR", "lnRR")) meta.plot2.all.c <- meta.plot2.all.b %>% group_by_at(vars(trait, GroupingTerm)) %>% summarise( malebias = sum(value > 0), femalebias = sum(value <= 0), total = malebias + femalebias, malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total ) meta.plot2.all.c$label <- "All traits" # Re-structure to create stacked bar plots meta.plot2.all.d <- as.data.frame(meta.plot2.all.c) meta.plot2.all.e <- gather(meta.plot2.all.d, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE) # Create new sample size variable meta.plot2.all.e$samplesize <- with(meta.plot2.all.e, ifelse(sex == "malepercent", malebias, femalebias)) # Add summary row ('All') and re-arrange rows into correct order for plotting (warnings about coercing 'id' into character vector are ok) meta.plot2.all.f <- meta.plot2.all.e %>% group_by(trait, sex) %>% summarise(GroupingTerm = "All", malebias = sum(malebias), femalebias = sum(femalebias), total = malebias + femalebias, label = "All traits", samplesize = sum(samplesize)) %>% mutate(percent = ifelse(sex == "femalepercent", femalebias*100/(malebias+femalebias), malebias*100/(malebias+femalebias))) %>% bind_rows(meta.plot2.all.e, .) %>% mutate(rownumber = row_number()) %>% .[c(37, 1:9, 39, 10:18, 38, 19:27, 40, 28:36), ] </code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYmluZGluZyBmYWN0b3IgYW5kIGNoYXJhY3RlciB2ZWN0b3IsIGNvZXJjaW5nIGludG8gY2hhcmFjdGVyIHZlY3RvcmJpbmRpbmcgY2hhcmFjdGVyIGFuZCBmYWN0b3IgdmVjdG9yLCBjb2VyY2luZyBpbnRvIGNoYXJhY3RlciB2ZWN0b3JcbiJ9 --> <pre><code>binding factor and character vector, coercing into character vectorbinding character and factor vector, coercing into character vector</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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--> <pre class="r"><code> #line references in previous code line corresponding to: #'lnCVR(male(All)), lnCVR(male('single grouping terms'), lnRR(male(All)), lnRR(male('single grouping terms')), #lnCVR(female(All)), lnCVR(female('single grouping terms'), lnRR(female(All)), lnRR(female('single grouping terms'))' meta.plot2.all.f$GroupingTerm <- factor(meta.plot2.all.f$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) meta.plot2.all.f$GroupingTerm <- factor(meta.plot2.all.f$GroupingTerm, rev(levels(meta.plot2.all.f$GroupingTerm))) malebias_Fig2_alltraits <- ggplot(meta.plot2.all.f) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text( data = subset(meta.plot2.all.f, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5), color = "white", size = 3.5 ) + facet_grid( cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18), scales = "free", space = "free" ) + scale_fill_brewer(palette = "Set2") + theme_bw(base_size = 18) + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip() # malebias_Fig2_alltraits #(panel A in Figure 4 in ms)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="overall-results-of-second-order-meta-analysis-figure-4-panel-b" class="section level3"> <h3>Overall results of second order meta-analysis (Figure 4, Panel B)</h3> <div id="re-structure-data-for-plotting" class="section level4"> <h4>Re-structure data for plotting</h4> <p>Data are re-structured, and grouping terms are being re-ordered</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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--> <pre class="r"><code>overall3 <- gather(overall2, parameter, value, c(lnCVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3 %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3 %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3 %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4 <- bind_rows(lnCVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # Re-order grouping terms overall4$GroupingTerm <- factor(overall4$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall4$GroupingTerm <- factor(overall4$GroupingTerm, rev(levels(overall4$GroupingTerm))) overall4$label <- "All traits" kable(cbind(overall4, overall4)) %>% kable_styling() %>% scroll_box(width = "100%", height = "200px")</code></pre> <!-- rnb-source-end --> 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--> <div style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:200px; overflow-x: scroll; width:100%; "><table class="table" style="margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> GroupingTerm </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> value </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> ci.low </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> ci.high </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> label </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> GroupingTerm1 </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter1 </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> value1 </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> ci.low1 </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> ci.high1 </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> label1 </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0035049 </td> <td style="text-align:right;"> -0.0240688 </td> <td style="text-align:right;"> 0.0170591 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0035049 </td> <td style="text-align:right;"> -0.0240688 </td> <td style="text-align:right;"> 0.0170591 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0774453 </td> <td style="text-align:right;"> 0.0414171 </td> <td style="text-align:right;"> 0.1134734 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0774453 </td> <td style="text-align:right;"> 0.0414171 </td> <td style="text-align:right;"> 0.1134734 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0430831 </td> <td style="text-align:right;"> -0.1125945 </td> <td style="text-align:right;"> 0.0264283 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0430831 </td> <td style="text-align:right;"> -0.1125945 </td> <td style="text-align:right;"> 0.0264283 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0126792 </td> <td style="text-align:right;"> -0.0140094 </td> <td style="text-align:right;"> 0.0393678 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0126792 </td> <td style="text-align:right;"> -0.0140094 </td> <td style="text-align:right;"> 0.0393678 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0681817 </td> <td style="text-align:right;"> -0.0980135 </td> <td style="text-align:right;"> -0.0383499 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0681817 </td> <td style="text-align:right;"> -0.0980135 </td> <td style="text-align:right;"> -0.0383499 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0217865 </td> <td style="text-align:right;"> -0.0165045 </td> <td style="text-align:right;"> 0.0600776 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0217865 </td> <td style="text-align:right;"> -0.0165045 </td> <td style="text-align:right;"> 0.0600776 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0183839 </td> <td style="text-align:right;"> -0.0128375 </td> <td style="text-align:right;"> 0.0496053 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0183839 </td> <td style="text-align:right;"> -0.0128375 </td> <td style="text-align:right;"> 0.0496053 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0157302 </td> <td style="text-align:right;"> -0.0111999 </td> <td style="text-align:right;"> 0.0426603 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0157302 </td> <td style="text-align:right;"> -0.0111999 </td> <td style="text-align:right;"> 0.0426603 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0817932 </td> <td style="text-align:right;"> -0.1476821 </td> <td style="text-align:right;"> -0.0159043 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0817932 </td> <td style="text-align:right;"> -0.1476821 </td> <td style="text-align:right;"> -0.0159043 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> All </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0046553 </td> <td style="text-align:right;"> -0.0086242 </td> <td style="text-align:right;"> 0.0179348 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> All </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0046553 </td> <td style="text-align:right;"> -0.0086242 </td> <td style="text-align:right;"> 0.0179348 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0199206 </td> <td style="text-align:right;"> -0.0634388 </td> <td style="text-align:right;"> 0.0235976 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0199206 </td> <td style="text-align:right;"> -0.0634388 </td> <td style="text-align:right;"> 0.0235976 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0678160 </td> <td style="text-align:right;"> 0.0072225 </td> <td style="text-align:right;"> 0.1284095 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0678160 </td> <td style="text-align:right;"> 0.0072225 </td> <td style="text-align:right;"> 0.1284095 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.1422577 </td> <td style="text-align:right;"> 0.0364352 </td> <td style="text-align:right;"> 0.2480801 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.1422577 </td> <td style="text-align:right;"> 0.0364352 </td> <td style="text-align:right;"> 0.2480801 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0163695 </td> <td style="text-align:right;"> -0.0443364 </td> <td style="text-align:right;"> 0.0770753 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0163695 </td> <td style="text-align:right;"> -0.0443364 </td> <td style="text-align:right;"> 0.0770753 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0574840 </td> <td style="text-align:right;"> -0.1074213 </td> <td style="text-align:right;"> -0.0075466 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0574840 </td> <td style="text-align:right;"> -0.1074213 </td> <td style="text-align:right;"> -0.0075466 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0388537 </td> <td style="text-align:right;"> -0.0024274 </td> <td style="text-align:right;"> 0.0801348 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0388537 </td> <td style="text-align:right;"> -0.0024274 </td> <td style="text-align:right;"> 0.0801348 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0048933 </td> <td style="text-align:right;"> -0.0324240 </td> <td style="text-align:right;"> 0.0226374 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0048933 </td> <td style="text-align:right;"> -0.0324240 </td> <td style="text-align:right;"> 0.0226374 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0132366 </td> <td style="text-align:right;"> -0.0335982 </td> <td style="text-align:right;"> 0.0071251 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0132366 </td> <td style="text-align:right;"> -0.0335982 </td> <td style="text-align:right;"> 0.0071251 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0091186 </td> <td style="text-align:right;"> 0.0012071 </td> <td style="text-align:right;"> 0.0170302 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0091186 </td> <td style="text-align:right;"> 0.0012071 </td> <td style="text-align:right;"> 0.0170302 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> All </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0124332 </td> <td style="text-align:right;"> -0.0061474 </td> <td style="text-align:right;"> 0.0310138 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> All </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0124332 </td> <td style="text-align:right;"> -0.0061474 </td> <td style="text-align:right;"> 0.0310138 </td> <td style="text-align:left;"> All traits </td> </tr> </tbody> </table></div> <!-- rnb-htmlwidget-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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--> <pre class="r"><code>Metameta_Fig3_alltraits <- overall4 %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "black", color = "black", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(-0.24, 0.25), breaks = c(-0.2, -0.1, 0, 0.1, 0.2), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = 0.5, size = 14), axis.title.y = element_blank() ) # Metameta_Fig3_alltraits</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> <div id="fig-4-sz-still-to-do" class="section level3"> <h3>Fig 4 # SZ STILL TO DO</h3> <p>Join the different parts and #TO DO!! add M / F symbols in Metameta_Fig3_alltraits</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI1Rlc3RcbiNtYWxlIDwtIHJlYWRQTkcoc3lzdGVtLmZpbGUoXCJpbWdcIiwgXCJtYWxlXCIpKVxuI3Rlc3QgPC0gTWV0YW1ldGFfRmlnM19hbGx0cmFpdHMgXG5cbiNsaWJyYXJ5KHBuZylcbmBgYCJ9 --> <pre class="r"><code>#Test #male <- readPNG(system.file("img", "male")) #test <- Metameta_Fig3_alltraits #library(png)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="figure-4-1" class="section level3"> <h3>Figure 4</h3> <p>Panel A shows the numbers of traits across functional groups that are either male-biased (blue-green) or female-biased (orange-red), as calculated in Step D (figure 3). Panel B shows effect sizes and 95% CI from separate meta-analysis for each functional group (step H in Figure 3). Both panels represent results evaluated across all traits (Phase 3, Figure 3). Traits that are male biased is shown in blue, whereas female bias data is represented in orange.</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuRmlnNCA8LSBnZ2FycmFuZ2UobWFsZWJpYXNfRmlnMl9hbGx0cmFpdHMsIE1ldGFtZXRhX0ZpZzNfYWxsdHJhaXRzLCAgbnJvdyA9IDIsIGFsaWduID0gXCJ2XCIsIGhlaWdodHMgPSBjKDEsIDEpLCBsYWJlbHMgPSBjKFwiQVwiLCBcIkJcIikpXG5GaWc0XG5gYGAifQ== --> <pre class="r"><code>Fig4 <- ggarrange(malebias_Fig2_alltraits, Metameta_Fig3_alltraits, nrow = 2, align = "v", heights = c(1, 1), labels = c("A", "B")) Fig4</code></pre> <!-- rnb-source-end --> <!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbXSwiaGVpZ2h0Ijo0MzIuNjMyOSwic2l6ZV9iZWhhdmlvciI6MCwid2lkdGgiOjcwMH0= --> <p><img 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" /></p> <!-- rnb-plot-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> <div id="figure-5" class="section level2"> <h2>Figure 5</h2> <div id="preparing-data-for-traits-with-ci-not-overlapping-0" class="section level4"> <h4>Preparing data for traits with CI not overlapping 0</h4> <p>To further investigate sex bias in this dataset, and in particular if the extent of sex bias differs between traits, we investigate the magnitude of male- and female bias in significantly different traits on (both for means and variability)</p> <p>To do this, we select only traits that have CIs that do not overlap with zero. The code below creates Figure 5A.</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbWzEsIlJlbW92ZWQgMiByb3dzIGNvbnRhaW5pbmcgbWlzc2luZyB2YWx1ZXMgKHBvc2l0aW9uX3N0YWNrKS4iXV0sImhlaWdodCI6NDMyLjYzMjksInNpemVfYmVoYXZpb3IiOjAsIndpZHRoIjo3MDB9 --> <p><img 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" /></p> <!-- rnb-plot-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="preparation-for-plots-on-significant-sex-bias-second-order-meta-analysis-results" class="section level3"> <h3>Preparation for Plots on significant sex-bias (Second-order meta analysis results)</h3> <div id="figure-5-b---traits-with-ci-not-overlapping-0" class="section level4"> <h4>Figure 5 B - traits with CI not overlapping 0</h4> <p>Prepare data create column with 1= different from zero, 0= zero included in CI #### Male-biased (significant) traits</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>meta.male.plot3.sig <- metacombo %>% mutate( sigCVR = ifelse(lnCVR_lower > 0, 1, 0), sigVR = ifelse(lnVR_lower > 0, 1, 0), sigRR = ifelse(lnRR_lower > 0, 1, 0) ) # Significant subset for lnCVR metacombo_male.plot3.CVR <- meta.male.plot3.sig %>% filter(sigCVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.CVR.all <- meta.male.plot3.sig %>% filter(sigCVR == 1) %>% nest(data = everything()) #Felix added 'data = everything()' on 4/2/2020 # Significant subset for lnVR metacombo_male.plot3.VR <- meta.male.plot3.sig %>% filter(sigVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.VR.all <- meta.male.plot3.sig %>% filter(sigVR == 1) %>% nest(data = everything()) # Significant subset for lnRR metacombo_male.plot3.RR <- meta.male.plot3.sig %>% filter(sigRR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.RR.all <- meta.male.plot3.sig %>% filter(sigRR == 1) %>% nest(data = everything()) # **Final fixed effects meta-analyses within grouping terms, with SE of the estimate plot3.male.meta.CVR <- metacombo_male.plot3.CVR %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.VR <- metacombo_male.plot3.VR %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.RR <- metacombo_male.plot3.RR %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) # Across all grouping terms # plot3.male.meta.CVR.all <- metacombo_male.plot3.CVR.all %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.CVR.all <- plot3.male.meta.CVR.all %>% mutate(GroupingTerm = "All") plot3.male.meta.VR.all <- metacombo_male.plot3.VR.all %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.VR.all <- plot3.male.meta.VR.all %>% mutate(GroupingTerm = "All") plot3.male.meta.RR.all <- metacombo_male.plot3.RR.all %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.RR.all <- plot3.male.meta.RR.all %>% mutate(GroupingTerm = "All") # Combine with separate grouping term results plot3.male.meta.CVR <- bind_rows(plot3.male.meta.CVR, plot3.male.meta.CVR.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdDMubWFsZS5tZXRhLlZSIDwtIGJpbmRfcm93cyhwbG90My5tYWxlLm1ldGEuVlIsIHBsb3QzLm1hbGUubWV0YS5WUi5hbGwpXG5gYGAifQ== --> <pre class="r"><code>plot3.male.meta.VR <- bind_rows(plot3.male.meta.VR, plot3.male.meta.VR.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdDMubWFsZS5tZXRhLlJSIDwtIGJpbmRfcm93cyhwbG90My5tYWxlLm1ldGEuUlIsIHBsb3QzLm1hbGUubWV0YS5SUi5hbGwpXG5gYGAifQ== --> <pre class="r"><code>plot3.male.meta.RR <- bind_rows(plot3.male.meta.RR, plot3.male.meta.RR.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code># **Re-structure data for each grouping term; delete un-used variables plot3.male.meta.CVR.b <- as.data.frame(plot3.male.meta.CVR %>% group_by(GroupingTerm) %>% mutate( lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)), lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.CVR.b)) plot3.male.meta.CVR.b <- bind_rows(plot3.male.meta.CVR.b, add.row.hearing)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYmluZGluZyBjaGFyYWN0ZXIgYW5kIGZhY3RvciB2ZWN0b3IsIGNvZXJjaW5nIGludG8gY2hhcmFjdGVyIHZlY3RvclxuIn0= --> <pre><code>binding character and factor vector, coercing into character vector</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code>plot3.male.meta.CVR.b <- plot3.male.meta.CVR.b[order(plot3.male.meta.CVR.b$GroupingTerm), ] plot3.male.meta.VR.b <- as.data.frame(plot3.male.meta.VR %>% group_by(GroupingTerm) %>% mutate( lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)), lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3)) ))[, c(1, 4:7)] plot3.male.meta.VR.b <- plot3.male.meta.VR.b[order(plot3.male.meta.VR.b$GroupingTerm), ] plot3.male.meta.RR.b <- as.data.frame(plot3.male.meta.RR %>% group_by(GroupingTerm) %>% mutate( lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)), lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3)) ))[, c(1, 4:7)] plot3.male.meta.RR.b <- plot3.male.meta.RR.b[order(plot3.male.meta.RR.b$GroupingTerm), ] overall.male.plot3 <- full_join(plot3.male.meta.CVR.b, plot3.male.meta.VR.b)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiSm9pbmluZywgYnkgPSBcIkdyb3VwaW5nVGVybVwiXG4ifQ== --> <pre><code>Joining, by = "GroupingTerm"</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxub3ZlcmFsbC5tYWxlLnBsb3QzIDwtIGZ1bGxfam9pbihvdmVyYWxsLm1hbGUucGxvdDMsIHBsb3QzLm1hbGUubWV0YS5SUi5iKVxuYGBgIn0= --> <pre class="r"><code>overall.male.plot3 <- full_join(overall.male.plot3, plot3.male.meta.RR.b)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiSm9pbmluZywgYnkgPSBcIkdyb3VwaW5nVGVybVwiXG4ifQ== --> <pre><code>Joining, by = "GroupingTerm"</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code>overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, rev(levels(overall.male.plot3$GroupingTerm))) overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, rev(levels(overall.male.plot3$GroupingTerm))) # str(overall.male.plot3)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Restructure MALE data for plotting</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>overall3.male.sig <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3.male.sig %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) # lnVR.ci <- overall3.male.sig %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.male.sig %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.male.sig <- bind_rows(lnCVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.male.sig$label <- "CI not overlapping zero"</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Plot Fig 5B all significant results (CI not overlapping zero) for males. This is the right panel in Figure 5B.</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbWzEsIlJlbW92ZWQgMyByb3dzIGNvbnRhaW5pbmcgbWlzc2luZyB2YWx1ZXMgKGdlb21fZXJyb3JiYXJoKS4iXSxbMSwiUmVtb3ZlZCAzIHJvd3MgY29udGFpbmluZyBtaXNzaW5nIHZhbHVlcyAoZ2VvbV9wb2ludCkuIl1dLCJoZWlnaHQiOjQzMi42MzI5LCJzaXplX2JlaGF2aW9yIjowLCJ3aWR0aCI6NzAwfQ== --> <p><img src="data:image/png;base64,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" /></p> <!-- rnb-plot-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="female-part-significant-traits" class="section level4"> <h4>Female part, significant traits</h4> <p>Female Fig5B sig</p> <p>Prepare data for traits with CI not overlapping 0 create column with 1= different from zero, 0= zero included in CI</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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--> <pre class="r"><code> # Female-biased traits meta.female.plot3.sig <- metacombo %>% mutate( sigCVR = ifelse(lnCVR_upper < 0, 1, 0), sigVR = ifelse(lnVR_upper < 0, 1, 0), sigRR = ifelse(lnRR_upper < 0, 1, 0) ) # Significant subset for lnCVR metacombo_female.plot3.CVR <- meta.female.plot3.sig %>% filter(sigCVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_female.plot3.CVR.all <- meta.female.plot3.sig %>% filter(sigCVR == 1) %>% nest(data = everything()) # Significant subset for lnVR metacombo_female.plot3.VR <- meta.female.plot3.sig %>% filter(sigVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_female.plot3.VR.all <- meta.female.plot3.sig %>% filter(sigVR == 1) %>% nest(data = everything()) # Significant subset for lnRR metacombo_female.plot3.RR <- meta.female.plot3.sig %>% filter(sigRR == 1) %>% group_by(GroupingTerm) %>% nest() #Felix added 7/2/2020: metacombo_female.plot3.RR[4,2][[1]] [[1]]$lnRR_upper; #only two data points: -0.12377263 -0.01553462; should probably be excluded?! metacombo_female.plot3.RR.all <- meta.female.plot3.sig %>% filter(sigRR == 1) %>% nest(data = everything()) # **Final fixed effects meta-analyses within grouping terms, with SE of the estimate plot3.female.meta.CVR <- metacombo_female.plot3.CVR %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.female.meta.VR <- metacombo_female.plot3.VR %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.female.meta.RR <- metacombo_female.plot3.RR %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) # Across all grouping terms # plot3.female.meta.CVR.all <- metacombo_female.plot3.CVR.all %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.female.meta.CVR.all <- plot3.female.meta.CVR.all %>% mutate(GroupingTerm = "All") plot3.female.meta.VR.all <- metacombo_female.plot3.VR.all %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.female.meta.VR.all <- plot3.female.meta.VR.all %>% mutate(GroupingTerm = "All") plot3.female.meta.RR.all <- metacombo_female.plot3.RR.all %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.female.meta.RR.all <- plot3.female.meta.RR.all %>% mutate(GroupingTerm = "All") # Combine with separate grouping term results plot3.female.meta.CVR <- bind_rows(plot3.female.meta.CVR, plot3.female.meta.CVR.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdDMuZmVtYWxlLm1ldGEuVlIgPC0gYmluZF9yb3dzKHBsb3QzLmZlbWFsZS5tZXRhLlZSLCBwbG90My5mZW1hbGUubWV0YS5WUi5hbGwpXG5gYGAifQ== --> <pre class="r"><code>plot3.female.meta.VR <- bind_rows(plot3.female.meta.VR, plot3.female.meta.VR.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdDMuZmVtYWxlLm1ldGEuUlIgPC0gYmluZF9yb3dzKHBsb3QzLmZlbWFsZS5tZXRhLlJSLCBwbG90My5mZW1hbGUubWV0YS5SUi5hbGwpXG5gYGAifQ== --> <pre class="r"><code>plot3.female.meta.RR <- bind_rows(plot3.female.meta.RR, plot3.female.meta.RR.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code># **Re-structure data for each grouping term; delete un-used variables plot3.female.meta.CVR.b <- as.data.frame(plot3.female.meta.CVR %>% group_by(GroupingTerm) %>% mutate( lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)), lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.female.meta.CVR.b)) plot3.female.meta.CVR.b <- bind_rows(plot3.female.meta.CVR.b, add.row.hearing)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYmluZGluZyBjaGFyYWN0ZXIgYW5kIGZhY3RvciB2ZWN0b3IsIGNvZXJjaW5nIGludG8gY2hhcmFjdGVyIHZlY3RvclxuIn0= --> <pre><code>binding character and factor vector, coercing into character vector</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code>plot3.female.meta.CVR.b <- plot3.female.meta.CVR.b[order(plot3.female.meta.CVR.b$GroupingTerm), ] plot3.female.meta.VR.b <- as.data.frame(plot3.female.meta.VR %>% group_by(GroupingTerm) %>% mutate( lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)), lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3)) ))[, c(1, 4:7)] plot3.female.meta.VR.b <- plot3.female.meta.VR.b[order(plot3.female.meta.VR.b$GroupingTerm), ] plot3.female.meta.RR.b <- as.data.frame(plot3.female.meta.RR %>% group_by(GroupingTerm) %>% mutate( lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)), lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3)) ))[, c(1, 4:7)] plot3.female.meta.RR.b <- plot3.female.meta.RR.b[order(plot3.female.meta.RR.b$GroupingTerm), ] overall.female.plot3 <- full_join(plot3.female.meta.CVR.b, plot3.female.meta.VR.b)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiSm9pbmluZywgYnkgPSBcIkdyb3VwaW5nVGVybVwiXG4ifQ== --> <pre><code>Joining, by = "GroupingTerm"</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxub3ZlcmFsbC5mZW1hbGUucGxvdDMgPC0gZnVsbF9qb2luKG92ZXJhbGwuZmVtYWxlLnBsb3QzLCBwbG90My5mZW1hbGUubWV0YS5SUi5iKVxuYGBgIn0= --> <pre class="r"><code>overall.female.plot3 <- full_join(overall.female.plot3, plot3.female.meta.RR.b)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiSm9pbmluZywgYnkgPSBcIkdyb3VwaW5nVGVybVwiXG4ifQ== --> <pre><code>Joining, by = "GroupingTerm"</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code>overall.female.plot3$GroupingTerm <- factor(overall.female.plot3$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall.female.plot3$GroupingTerm <- factor(overall.female.plot3$GroupingTerm, rev(levels(overall.female.plot3$GroupingTerm))) </code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Re-structure data for plotting</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>overall3.female.sig <- gather(overall.female.plot3, parameter, value, c(lnCVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3.female.sig %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) # lnVR.ci <- overall3.female.sig %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.female.sig %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.female.sig <- bind_rows(lnCVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # lnVR.ci, overall4.female.sig$label <- "CI not overlapping zero" </code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Plotting Fig5B all significant results (CI not overlapping zero, female )</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code> Metameta_Fig3_female.sig <- overall4.female.sig %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "salmon1", color = "salmon1", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(-0.4, 0), breaks = c(-0.3, 0), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), # rows = vars(label), # labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank() ) # Metameta_Fig3_female.sig #(Figure 5B left panel)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> </div> <div id="join-code-missing" class="section level2"> <h2>JOIN!!! CODE MISSING??</h2> <p>malebias_Fig2_sigtraits Metameta_Fig3_female.sig #(Figure 5B left panel) Metameta_Fig3_male.sig</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuRmlnNSA8LSBnZ2FycmFuZ2UobWFsZWJpYXNfRmlnMl9zaWd0cmFpdHMsIEZpZzVCLFxuICBuY29sID0gMSwgbnJvdyA9IDIsIHdpZHRocyA9IGMoMSwgMS4xMCksIGhlaWdodHMgPSBjKDEuMTAsIDEpLCAgbGFiZWxzID0gYyhcIkFcIiwgXCJCXCIpXG4pXG5cbmBgYCJ9 --> <pre class="r"><code>Fig5 <- ggarrange(malebias_Fig2_sigtraits, Fig5B, ncol = 1, nrow = 2, widths = c(1, 1.10), heights = c(1.10, 1), labels = c("A", "B") ) </code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiUmVtb3ZlZCAyIHJvd3MgY29udGFpbmluZyBtaXNzaW5nIHZhbHVlcyAocG9zaXRpb25fc3RhY2spLlxuIn0= --> <pre><code>Removed 2 rows containing missing values (position_stack).</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuRmlnNVxuYGBgIn0= --> <pre class="r"><code>Fig5</code></pre> <!-- rnb-source-end --> <!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbXSwiaGVpZ2h0Ijo0MzIuNjMyOSwic2l6ZV9iZWhhdmlvciI6MCwid2lkdGgiOjcwMH0= --> <p><img src="data:image/png;base64,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" /></p> <!-- rnb-plot-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> <div id="supplemental-plots" class="section level1"> <h1>Supplemental Plots</h1> <div id="figure-s1" class="section level2"> <h2>Figure S1</h2> <div id="including-lnvr" class="section level3"> <h3>Including lnVR</h3> </div> <div id="count-data-including-lnvr-fig-s1-panel-a" class="section level3"> <h3>Count data, including lnVR (Fig S1 panel A)</h3> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbXSwiaGVpZ2h0Ijo0MzIuNjMyOSwic2l6ZV9iZWhhdmlvciI6MCwid2lkdGgiOjcwMH0= --> <p><img 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" /></p> <!-- rnb-plot-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="overall-results-of-second-order-meta-analysis-including-vr" class="section level3"> <h3>Overall results of second order meta analysis, INCLUDING VR</h3> <div id="re-structure-data-for-plotting-1" class="section level4"> <h4>Re-structure data for plotting</h4> <p>Restructure MALE data for plotting</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>overall3.male.sigS <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3.male.sigS %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3.male.sigS %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.male.sigS %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.male.sigS <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.male.sigS$label <- "CI not overlapping zero" # Data are re-structured, and grouping terms are being re-ordered overall3S <- gather(overall2, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3S %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3S %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3S %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4S <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # Re-order grouping terms overall4S$GroupingTerm <- factor(overall4S$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall4S$GroupingTerm <- factor(overall4S$GroupingTerm, rev(levels(overall4S$GroupingTerm))) overall4S$label <- "All traits"</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="preparation-for-plot-including-lnvr" class="section level4"> <h4>Preparation for plot, including lnVR</h4> <p>Preparation: Sub-Plot for Figure S1: all traits (S1 B)</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>Metameta_FigS1_alltraits <- overall4S %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "black", color = "black", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(-0.24, 0.25), breaks = c(-0.2, -0.1, 0, 0.1, 0.2), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = 0.5, size = 14), axis.title.y = element_blank() ) # Metameta_FigS1_alltraits</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> <div id="heterogeneity" class="section level3"> <h3>Heterogeneity</h3> <p>The analysis for heterogeneity follows the workflow of the above steps for the different meta-analyses. However, in the initial meta-analysis we extract sigma^2 and errors for mouse strains and centers (Institutions).</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBDcmVhdGUgZGF0YWZyYW1lIHRvIHN0b3JlIHJlc3VsdHNcbnJlc3VsdHMuYWxsaGV0ZXJvLmdyb3VwaW5nIDwtIGFzLmRhdGEuZnJhbWUoY2JpbmQoYygxOm4pLCBtYXRyaXgocmVwKDAsIG4gKiAzMCksIG5jb2wgPSAzMCkpKVxubmFtZXMocmVzdWx0cy5hbGxoZXRlcm8uZ3JvdXBpbmcpIDwtIGMoXG4gIFwiaWRcIiwgXCJzaWdtYTJfc3RyYWluLkNWUlwiLCBcInNpZ21hMl9jZW50ZXIuQ1ZSXCIsIFwic2lnbWEyX2Vycm9yLkNWUlwiLCBcInMubmxldmVscy5zdHJhaW4uQ1ZSXCIsXG4gIFwicy5ubGV2ZWxzLmNlbnRlci5DVlJcIiwgXCJzLm5sZXZlbHMuZXJyb3IuQ1ZSXCIsIFwic2lnbWEyX3N0cmFpbi5WUlwiLCBcInNpZ21hMl9jZW50ZXIuVlJcIiwgXCJzaWdtYTJfZXJyb3IuVlJcIiwgXCJzLm5sZXZlbHMuc3RyYWluLlZSXCIsXG4gIFwicy5ubGV2ZWxzLmNlbnRlci5WUlwiLCBcInMubmxldmVscy5lcnJvci5WUlwiLCBcInNpZ21hMl9zdHJhaW4uUlJcIiwgXCJzaWdtYTJfY2VudGVyLlJSXCIsIFwic2lnbWEyX2Vycm9yLlJSXCIsIFwicy5ubGV2ZWxzLnN0cmFpbi5SUlwiLFxuICBcInMubmxldmVscy5jZW50ZXIuUlJcIiwgXCJzLm5sZXZlbHMuZXJyb3IuUlJcIiwgXCJsbkNWUlwiLCBcImxuQ1ZSX2xvd2VyXCIsIFwibG5DVlJfdXBwZXJcIiwgXCJsbkNWUl9zZVwiLCBcImxuVlJcIiwgXCJsblZSX2xvd2VyXCIsIFwibG5WUl91cHBlclwiLFxuICBcImxuVlJfc2VcIiwgXCJsblJSXCIsIFwibG5SUl9sb3dlclwiLCBcImxuUlJfdXBwZXJcIiwgXCJsblJSX3NlXCJcbilcblxuYGBgIn0= --> <pre class="r"><code># Create dataframe to store results results.allhetero.grouping <- as.data.frame(cbind(c(1:n), matrix(rep(0, n * 30), ncol = 30))) names(results.allhetero.grouping) <- c( "id", "sigma2_strain.CVR", "sigma2_center.CVR", "sigma2_error.CVR", "s.nlevels.strain.CVR", "s.nlevels.center.CVR", "s.nlevels.error.CVR", "sigma2_strain.VR", "sigma2_center.VR", "sigma2_error.VR", "s.nlevels.strain.VR", "s.nlevels.center.VR", "s.nlevels.error.VR", "sigma2_strain.RR", "sigma2_center.RR", "sigma2_error.RR", "s.nlevels.strain.RR", "s.nlevels.center.RR", "s.nlevels.error.RR", "lnCVR", "lnCVR_lower", "lnCVR_upper", "lnCVR_se", "lnVR", "lnVR_lower", "lnVR_upper", "lnVR_se", "lnRR", "lnRR_lower", "lnRR_upper", "lnRR_se" ) </code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>LOOP Parameters to extract from metafor (sigma2’s, s.nlevels)</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code> for (t in 1:n) { tryCatch( { data_par_age <- data_subset_parameterid_individual_by_age(data, t, age_min = 0, age_center = 100) population_stats <- calculate_population_stats(data_par_age) results <- create_meta_analysis_effect_sizes(population_stats) # lnCVR, logaritm of the ratio of male and female coefficients of variance cvr. <- metafor::rma.mv(yi = effect_size_CVR, V = sample_variance_CVR, random = list( ~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err ), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), data = results) results.allhetero.grouping[t, 2] <- cvr.$sigma2[1] results.allhetero.grouping[t, 3] <- cvr.$sigma2[2] results.allhetero.grouping[t, 4] <- cvr.$sigma2[3] results.allhetero.grouping[t, 5] <- cvr.$s.nlevels[1] results.allhetero.grouping[t, 6] <- cvr.$s.nlevels[2] results.allhetero.grouping[t, 7] <- cvr.$s.nlevels[3] results.allhetero.grouping[t, 20] <- cvr.$b results.allhetero.grouping[t, 21] <- cvr.$ci.lb results.allhetero.grouping[t, 22] <- cvr.$ci.ub results.allhetero.grouping[t, 23] <- cvr.$se # lnVR, male to female variability ratio (logarithm of male and female standard deviations) vr. <- metafor::rma.mv(yi = effect_size_VR, V = sample_variance_VR, random = list( ~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err ), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), data = results) results.allhetero.grouping[t, 8] <- vr.$sigma2[1] results.allhetero.grouping[t, 9] <- vr.$sigma2[2] results.allhetero.grouping[t, 10] <- vr.$sigma2[3] results.allhetero.grouping[t, 11] <- vr.$s.nlevels[1] results.allhetero.grouping[t, 12] <- vr.$s.nlevels[2] results.allhetero.grouping[t, 13] <- vr.$s.nlevels[3] results.allhetero.grouping[t, 24] <- vr.$b results.allhetero.grouping[t, 25] <- vr.$ci.lb results.allhetero.grouping[t, 26] <- vr.$ci.ub results.allhetero.grouping[t, 27] <- vr.$se # lnRR, response ratio (logarithm of male and female means) rr. <- metafor::rma.mv(yi = effect_size_RR, V = sample_variance_RR, random = list( ~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err ), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), data = results) results.allhetero.grouping[t, 14] <- rr.$sigma2[1] results.allhetero.grouping[t, 15] <- rr.$sigma2[2] results.allhetero.grouping[t, 16] <- rr.$sigma2[3] results.allhetero.grouping[t, 17] <- rr.$s.nlevels[1] results.allhetero.grouping[t, 18] <- rr.$s.nlevels[2] results.allhetero.grouping[t, 19] <- rr.$s.nlevels[3] results.allhetero.grouping[t, 28] <- rr.$b results.allhetero.grouping[t, 29] <- rr.$ci.lb results.allhetero.grouping[t, 30] <- rr.$ci.ub results.allhetero.grouping[t, 31] <- rr.$se }, error = function(e) { cat("ERROR :", conditionMessage(e), "\n") } ) }</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <div id="exclude-traits-without-variation-between-mouse-strains-merge-datasets-felix-added-622020" class="section level4"> <h4>Exclude traits without variation between mouse strains; merge datasets #Felix added 6/2/2020</h4> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucmVzdWx0cy5hbGxoZXRlcm8uZ3JvdXBpbmcyIDwtIHJlc3VsdHMuYWxsaGV0ZXJvLmdyb3VwaW5nW3Jlc3VsdHMuYWxsaGV0ZXJvLmdyb3VwaW5nJHMubmxldmVscy5zdHJhaW4uVlIgIT0gMCwgXVxuIyBucm93KHJlc3VsdHMuYWxsaGV0ZXJvLmdyb3VwaW5nKSAjMjIzICBGZWxpeCA3LzIvMjAyMCBhZGRlZDogbm90IHN1cmUuIFJ1biBhZ2FpbiBhbmQgaXQgd2FzIDIzMj8hXG5gYGAifQ== --> <pre class="r"><code>results.allhetero.grouping2 <- results.allhetero.grouping[results.allhetero.grouping$s.nlevels.strain.VR != 0, ] # nrow(results.allhetero.grouping) #223 Felix 7/2/2020 added: not sure. Run again and it was 232?!</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Merge data sets containing metafor results with procedure etc. names</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBwcm9jZWR1cmVzIDwtIHJlYWQuY3N2KGhlcmUoXCJleHBvcnRcIiwgXCJwcm9jZWR1cmVzLmNzdlwiKSlcblxucmVzdWx0cy5hbGxoZXRlcm8uZ3JvdXBpbmcyJHBhcmFtZXRlcl9ncm91cCA8LSBkYXRhJHBhcmFtZXRlcl9ncm91cFttYXRjaChyZXN1bHRzLmFsbGhldGVyby5ncm91cGluZzIkaWQsIGRhdGEkaWQpXVxucmVzdWx0cy5hbGxoZXRlcm8uZ3JvdXBpbmcyJHByb2NlZHVyZSA8LSBkYXRhJHByb2NlZHVyZV9uYW1lW21hdGNoKHJlc3VsdHMuYWxsaGV0ZXJvLmdyb3VwaW5nMiRpZCwgZGF0YSRpZCldXG5cbnJlc3VsdHMuYWxsaGV0ZXJvLmdyb3VwaW5nMiRHcm91cGluZ1Rlcm0gPC0gcHJvY2VkdXJlcyRHcm91cGluZ1Rlcm1bbWF0Y2gocmVzdWx0cy5hbGxoZXRlcm8uZ3JvdXBpbmcyJHByb2NlZHVyZSwgcHJvY2VkdXJlcyRwcm9jZWR1cmUpXVxucmVzdWx0cy5hbGxoZXRlcm8uZ3JvdXBpbmcyJHBhcmFtZXRlcl9uYW1lIDwtIGRhdGEkcGFyYW1ldGVyX25hbWVbbWF0Y2gocmVzdWx0cy5hbGxoZXRlcm8uZ3JvdXBpbmcyJGlkLCBkYXRhJGlkKV1cbmBgYCJ9 --> <pre class="r"><code># procedures <- read.csv(here("export", "procedures.csv")) results.allhetero.grouping2$parameter_group <- data$parameter_group[match(results.allhetero.grouping2$id, data$id)] results.allhetero.grouping2$procedure <- data$procedure_name[match(results.allhetero.grouping2$id, data$id)] results.allhetero.grouping2$GroupingTerm <- procedures$GroupingTerm[match(results.allhetero.grouping2$procedure, procedures$procedure)] results.allhetero.grouping2$parameter_name <- data$parameter_name[match(results.allhetero.grouping2$id, data$id)]</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="correlated-parameters" class="section level4"> <h4>Correlated parameters</h4> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>metahetero1 <- results.allhetero.grouping2 # length(unique(metahetero1$procedure)) #19 # length(unique(metahetero1$GroupingTerm)) #9 # length(unique(metahetero1$parameter_group)) #152 # length(unique(metahetero1$parameter_name)) #223 # Count of number of parameter names (correlated sub-traits) in each parameter group (par_group_size) metahetero1b <- metahetero1 %>% group_by(parameter_group) %>% mutate(par_group_size = n_distinct(parameter_name)) metahetero1$par_group_size <- metahetero1b$par_group_size[match(metahetero1$parameter_group, metahetero1b$parameter_group)] # Create subsets with > 1 count (par_group_size > 1) metahetero1_sub <- subset(metahetero1, par_group_size > 1) # 92 observations # str(metahetero1_sub) # metahetero1_sub$sampleSize <- as.numeric(metahetero1_sub$sampleSize) #from previous analysis? don't think is used: : delete in final version # Nest data n_count. <- metahetero1_sub %>% group_by(parameter_group) %>% # mutate(raw_N = sum(sampleSize)) %>% #Felix added: don't think is necessary: delete in final version nest() # meta-analysis preparation model_count. <- n_count. %>% mutate( model_lnRR = map(data, ~ robu(.x$lnRR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnRR_se)^2 )), model_lnVR = map(data, ~ robu(.x$lnVR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnVR_se)^2 )), model_lnCVR = map(data, ~ robu(.x$lnCVR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnCVR_se)^2 )) ) # Robumeta object details: # str(model_count.$model_lnCVR[[1]]) ## *Perform meta-analyses on correlated sub-traits, using robumeta # Susi / FELIX: what's this below? # Shinichi: We think we want to use these for further analyses: # residual variance: as.numeric(robu_fit$mod_info$term1) (same as 'mod_info$tau.sq') # sample size: robu_fit$N ## **Extract and save parameter estimates # Felix: doesn't work , error message: #!!!!!!!!!!! ERROR!!!!!!!!!!!!!!!!!!!! #Error: Column `parameter_group` can't be modified because it's a grouping variable count_fun. <- function(mod_sub) { return(c(as.numeric(mod_sub$mod_info$term1), mod_sub$N)) } robusub_RR. <- model_count. %>% transmute(estimatelnRR = map(model_lnRR, count_fun.)) %>% #Felix 4/2/2020: deleted: 'parameter_group' (in brackets, after 'transmute') mutate(r = map(estimatelnRR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnRR) %>% purrr::set_names(c("parameter_group", "var.RR", "N.RR")) robusub_CVR. <- model_count. %>% transmute(estimatelnCVR = map(model_lnCVR, count_fun.)) %>% mutate(r = map(estimatelnCVR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnCVR) %>% purrr::set_names(c("parameter_group", "var.CVR", "N.CVR")) robusub_VR. <- model_count. %>% transmute(estimatelnVR = map(model_lnVR, count_fun.)) %>% mutate(r = map(estimatelnVR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnVR) %>% purrr::set_names(c("parameter_group", "var.VR", "N.VR")) robu_all. <- full_join(robusub_CVR., robusub_VR.) %>% full_join(., robusub_RR.)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Merge the two data sets (the new [robu_all.] and the initial [uncorrelated sub-traits with count = 1])</p> <p>In this step, we<br /> 1) merge the N from robumeta and the N from metafor (s.nlevels.error) together into the same columns (N.RR, N.VR, N.CVR) 2) calculate the total variance for metafor models as the sum of random effect variances and the residual error, then add in the same columns together with the residual variances from robumeta</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>metahetero_all <- metahetero1 %>% filter(par_group_size == 1) %>% as_tibble() metahetero_all$N.RR <- metahetero_all$s.nlevels.error.RR metahetero_all$N.CVR <- metahetero_all$s.nlevels.error.CVR metahetero_all$N.VR <- metahetero_all$s.nlevels.error.VR metahetero_all$var.RR <- log(sqrt(metahetero_all$sigma2_strain.RR + metahetero_all$sigma2_center.RR + metahetero_all$sigma2_error.RR)) metahetero_all$var.VR <- log(sqrt(metahetero_all$sigma2_strain.VR + metahetero_all$sigma2_center.VR + metahetero_all$sigma2_error.VR)) metahetero_all$var.CVR <- log(sqrt(metahetero_all$sigma2_strain.CVR + metahetero_all$sigma2_center.CVR + metahetero_all$sigma2_error.CVR)) # str(metahetero_all) # str(robu_all.) metahetero_all <- metahetero_all %>% mutate( var.RR = if_else(var.RR == -Inf, -7, var.RR), #Felix commented 6/2/2020: can't remmeber, why -7, -6, -5 in this section! var.VR = if_else(var.VR == -Inf, -5, var.VR), var.CVR = if_else(var.CVR == -Inf, -6, var.CVR) ) # **Combine data ## Step1 combinedmetahetero <- bind_rows(robu_all., metahetero_all) # glimpse(combinedmetahetero) # Steps 2&3 metacombohetero <- combinedmetahetero metacombohetero$counts <- metahetero1$par_group_size[match(metacombohetero$parameter_group, metahetero1$parameter_group)] metacombohetero$procedure2 <- metahetero1$procedure[match(metacombohetero$parameter_group, metahetero1$parameter_group)] metacombohetero$GroupingTerm2 <- metahetero1$GroupingTerm[match(metacombohetero$parameter_group, metahetero1$parameter_group)] # **Clean-up and rename metacombohetero <- metacombohetero %>% select(parameter_group, var.CVR, N.CVR, var.VR, N.VR, var.RR, N.RR, counts, procedure = procedure2, GroupingTerm = GroupingTerm2) #Felix changed 6/2/2020: was: c(1:7, 43:45, and 2 renaming lines) </code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="meta-analysis-of-heterogeneity" class="section level4"> <h4>Meta-analysis of heterogeneity</h4> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin {"data":"```r\n## Perform meta-meta-analysis (3 for each of the 9 grouping terms: var.CVR, var.VR, var.RR)\n\nmetacombohetero_final <- metacombohetero %>%\n  group_by(GroupingTerm) %>%\n  nest()\n\n# Final fixed effects meta-analyses within grouping terms, with SE of the estimate\n\nheterog1 <- metacombohetero_final %>%\n\n  mutate(\n    model_heteroCVR = map(data, ~ metafor::rma.uni(\n      yi = .x$var.CVR, sei = sqrt(1 / 2 * (.x$N.CVR - 1)),\n      control = list(optimizer = \"optim\", optmethod = \"Nelder-Mead\", maxit = 10000, stepadj = 0.5), verbose = F\n    )),\n    model_heteroVR = map(data, ~ metafor::rma.uni(\n      yi = .x$var.VR, sei = sqrt(1 / 2 * (.x$N.VR - 1)),\n      control = list(optimizer = \"optim\", optmethod = \"Nelder-Mead\", maxit = 10000, stepadj = 0.5), verbose = F\n    )),\n    model_heteroRR = map(data, ~ metafor::rma.uni(\n      yi = .x$var.RR, sei = sqrt(1 / 2 * (.x$N.RR - 1)),\n      control = list(optimizer = \"optim\", optmethod = \"Nelder-Mead\", maxit = 10000, stepadj = 0.5), verbose = F\n    ))\n  )\n\n# Across all grouping terms   \n\nmetacombohetero_all_final <- metacombohetero %>%\n  nest(data = everything()) \n\n# Final fixed effects meta-analyses ACROSS grouping terms, with SE of the estimate\n\nheterog1_all <- metacombohetero_all_final %>%\n  \n  mutate(\n    model_heteroCVR = map(data, ~ metafor::rma.uni(\n      yi = .x$var.CVR, sei = sqrt(1 / 2 * (.x$N.CVR - 1)),\n      control = list(optimizer = \"optim\", optmethod = \"Nelder-Mead\", maxit = 10000, stepadj = 0.5), verbose = F\n    )),\n    model_heteroVR = map(data, ~ metafor::rma.uni(\n      yi = .x$var.VR, sei = sqrt(1 / 2 * (.x$N.VR - 1)),\n      control = list(optimizer = \"optim\", optmethod = \"Nelder-Mead\", maxit = 10000, stepadj = 0.5), verbose = F\n    )),\n    model_heteroRR = map(data, ~ metafor::rma.uni(\n      yi = .x$var.RR, sei = sqrt(1 / 2 * (.x$N.RR - 1)),\n      control = list(optimizer = \"optim\", optmethod = \"Nelder-Mead\", maxit = 10000, stepadj = 0.5), verbose = F\n    ))\n  )\n\n\n# Re-structure data for each grouping term; extract heterogenenity/variance terms; delete un-used variables\n\nBehaviour. <- heterog1 %>%\n  filter(., GroupingTerm == \"Behaviour\") %>%\n  select(., -data) %>%\n  mutate(\n    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,\n    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,\n    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se\n  ) %>%\n  select(., GroupingTerm, heteroCVR:heteroRR_se)\n\nImmunology. <- heterog1 %>%\n  filter(., GroupingTerm == \"Immunology\") %>%\n  select(., -data) %>%\n  mutate(\n    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,\n    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,\n    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se\n  ) %>%\n  select(., GroupingTerm, heteroCVR:heteroRR_se)\n\nHematology. <- heterog1 %>%\n  filter(., GroupingTerm == \"Hematology\") %>%\n  select(., -data) %>%\n  mutate(\n    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,\n    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,\n    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se\n  ) %>%\n  select(., GroupingTerm, heteroCVR:heteroRR_se)\n\n\nHearing. <- heterog1 %>%\n  filter(., GroupingTerm == \"Hearing\") %>%\n  select(., -data) %>%\n  mutate(\n    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,\n    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,\n    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se\n  ) %>%\n  select(., GroupingTerm, heteroCVR:heteroRR_se)\n\nPhysiology. <- heterog1 %>%\n  filter(., GroupingTerm == \"Physiology\") %>%\n  select(., -data) %>%\n  mutate(\n    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,\n    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,\n    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se\n  ) %>%\n  select(., GroupingTerm, heteroCVR:heteroRR_se)\n\nMetabolism. <- heterog1 %>%\n  filter(., GroupingTerm == \"Metabolism\") %>%\n  select(., -data) %>%\n  mutate(\n    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,\n    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,\n    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se\n  ) %>%\n  select(., GroupingTerm, heteroCVR:heteroRR_se)\n\nMorphology. <- heterog1 %>%\n  filter(., GroupingTerm == \"Morphology\") %>%\n  select(., -data) %>%\n  mutate(\n    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,\n    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,\n    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se\n  ) %>%\n  select(., GroupingTerm, heteroCVR:heteroRR_se)\n\nHeart. <- heterog1 %>%\n  filter(., GroupingTerm == \"Heart\") %>%\n  select(., -data) %>%\n  mutate(\n    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,\n    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,\n    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se\n  ) %>%\n  select(., GroupingTerm, heteroCVR:heteroRR_se)\n\nEye. <- heterog1 %>%\n  filter(., GroupingTerm == \"Eye\") %>%\n  select(., -data) %>%\n  mutate(\n    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,\n    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,\n    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se\n  ) %>%\n  select(., GroupingTerm, heteroCVR:heteroRR_se)\n\n#Reorder to be able to keep cell referencing \nheterog1_all <- heterog1_all %>% mutate(GroupingTerm = \"All\") %>% select(GroupingTerm, everything())\n\nAll. <- heterog1_all %>% \n  select(., -data) %>%\n  mutate(\n    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,\n    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,\n    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se\n  ) %>%\n  select(., GroupingTerm, heteroCVR:heteroRR_se)\n\nheterog2 <- bind_rows(Behaviour., Morphology., Metabolism., Physiology., Immunology., Hematology., Heart., Hearing., Eye., All.)\n# str(heterog2)\n```"} --> <pre class="r"><code>## Perform meta-meta-analysis (3 for each of the 9 grouping terms: var.CVR, var.VR, var.RR) metacombohetero_final <- metacombohetero %>% group_by(GroupingTerm) %>% nest() # Final fixed effects meta-analyses within grouping terms, with SE of the estimate heterog1 <- metacombohetero_final %>% mutate( model_heteroCVR = map(data, ~ metafor::rma.uni( yi = .x$var.CVR, sei = sqrt(1 / 2 * (.x$N.CVR - 1)), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F )), model_heteroVR = map(data, ~ metafor::rma.uni( yi = .x$var.VR, sei = sqrt(1 / 2 * (.x$N.VR - 1)), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F )), model_heteroRR = map(data, ~ metafor::rma.uni( yi = .x$var.RR, sei = sqrt(1 / 2 * (.x$N.RR - 1)), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F )) ) # Across all grouping terms metacombohetero_all_final <- metacombohetero %>% nest(data = everything()) # Final fixed effects meta-analyses ACROSS grouping terms, with SE of the estimate heterog1_all <- metacombohetero_all_final %>% mutate( model_heteroCVR = map(data, ~ metafor::rma.uni( yi = .x$var.CVR, sei = sqrt(1 / 2 * (.x$N.CVR - 1)), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F )), model_heteroVR = map(data, ~ metafor::rma.uni( yi = .x$var.VR, sei = sqrt(1 / 2 * (.x$N.VR - 1)), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F )), model_heteroRR = map(data, ~ metafor::rma.uni( yi = .x$var.RR, sei = sqrt(1 / 2 * (.x$N.RR - 1)), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F )) ) # Re-structure data for each grouping term; extract heterogenenity/variance terms; delete un-used variables Behaviour. <- heterog1 %>% filter(., GroupingTerm == "Behaviour") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Immunology. <- heterog1 %>% filter(., GroupingTerm == "Immunology") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Hematology. <- heterog1 %>% filter(., GroupingTerm == "Hematology") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Hearing. <- heterog1 %>% filter(., GroupingTerm == "Hearing") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Physiology. <- heterog1 %>% filter(., GroupingTerm == "Physiology") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Metabolism. <- heterog1 %>% filter(., GroupingTerm == "Metabolism") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Morphology. <- heterog1 %>% filter(., GroupingTerm == "Morphology") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Heart. <- heterog1 %>% filter(., GroupingTerm == "Heart") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Eye. <- heterog1 %>% filter(., GroupingTerm == "Eye") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) #Reorder to be able to keep cell referencing heterog1_all <- heterog1_all %>% mutate(GroupingTerm = "All") %>% select(GroupingTerm, everything()) All. <- heterog1_all %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) heterog2 <- bind_rows(Behaviour., Morphology., Metabolism., Physiology., Immunology., Hematology., Heart., Hearing., Eye., All.) # str(heterog2)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="heterogeneity-plot" class="section level4"> <h4>Heterogeneity PLOT</h4> <p>Restructure data for plotting</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>heterog3 <- gather(heterog2, parameter, value, c(heteroCVR, heteroVR, heteroRR), factor_key = TRUE) heteroCVR.ci <- heterog3 %>% filter(parameter == "heteroCVR") %>% mutate(ci.low = heteroCVR_lower, ci.high = heteroCVR_upper) heteroVR.ci <- heterog3 %>% filter(parameter == "heteroVR") %>% mutate(ci.low = heteroVR_lower, ci.high = heteroVR_upper) heteroRR.ci <- heterog3 %>% filter(parameter == "heteroRR") %>% mutate(ci.low = heteroRR_lower, ci.high = heteroRR_upper) heterog4 <- bind_rows(heteroCVR.ci, heteroVR.ci, heteroRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # **Re-order grouping terms heterog4$GroupingTerm <- factor(heterog4$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) heterog4$GroupingTerm <- factor(heterog4$GroupingTerm, rev(levels(heterog4$GroupingTerm))) heterog4$label <- "All traits" # write.csv(heterog4, "heterog4.csv")</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="plot-s1-c-second-order-meta-analysis-on-heterogeneity" class="section level4"> <h4>Plot S1 C (Second-order meta analysis on heterogeneity)</h4> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>heterog5 <- heterog4 heterog5$mean <- as.numeric(exp(heterog5$value)) heterog5$ci.l <- as.numeric(exp(heterog5$ci.low)) heterog5$ci.h <- as.numeric(exp(heterog5$ci.high)) heterog6 <- heterog5 HeteroS1 <- heterog6 %>% ggplot(aes(y = GroupingTerm, x = mean)) + geom_errorbarh(aes( xmin = ci.l, xmax = ci.h ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "black", color = "black", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(-0.1, 1.4), # breaks = c(0, 0.1, 0.2), name = "sigma^2" ) + # geom_vline(xintercept=0, # color='black', # linetype='dashed')+ facet_grid( cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = 0.5, size = 14), axis.title.y = element_blank() ) #HeteroS1 </code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="combined-figure-s1-overall-count-data-meta-anlysis-results-heterogeneity" class="section level4"> <h4>Combined Figure S1: overall Count data, Meta anlysis results, Heterogeneity)</h4> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbXSwiaGVpZ2h0Ijo0MzIuNjMyOSwic2l6ZV9iZWhhdmlvciI6MCwid2lkdGgiOjcwMH0= --> <p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArwAAAGwCAYAAABLkLalAAAEGWlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9oU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvuuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTvYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8eUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiYMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpkhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thKbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpXzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477hLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549HQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgYhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjzhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VQNcC+8AAEAASURBVHgB7F0HnBTF0q8NR875OMIdOeckOSkqIEZEEUURxezTZ+LzKZhzeD59oqiIWdSniCio5Jxzzhw553C3u1/9e6+X2bmZzbO7d3T9frs7oadDTW1PTXXVv2weJlKkOKA4oDigOKA4oDigOKA4oDiQTzlgz6fjUsNSHFAcUBxQHFAcUBxQHFAcUBwQHFAKrxIExQHFAcUBxQHFAcUBxQHFgXzNAaXw5uvbqwanOKA4oDigOKA4oDigOKA4oBReJQOKA4oDigOKA4oDigOKA4oD+ZoDSuHN17dXDU5xQHFAcUBxQHFAcUBxQHHAGQ4Ltm7dSrNnzxaX1KhRg9q3bx/O5aqs4oDigOKA4oDigOKA4oDigOJA3DlgCweW7LbbbqOxY8eKTlapUoW2bdtGDocj7p1WDSoOKA4oDigOKA4oDigOKA4oDoTKgZBdGo4dO0bjxo3z1ZuZmUkTJ0707asNxQHFAcUBxQHFAcUBxQHFAcWBZORAyArv119/TWfOnBFjSElJEb+jRo1KxjGpPikOKA4oDigOKA4oDigOKA4oDvg4ELLC+8knn4iLGjVqRAMHDhTbv//+O+3cudNXmdpQHFAcUBxQHFAcUBxQHFAcUBxINg6EFLS2bNkyWrx4sej7oEGDqE2bNjRmzBhyu900evRoGjlyZLKNK8/35+zZs3T+/Pk8Pw41gPzPAfjxFy1aNOhAsUKUlZUVtJwqoDiQaA44nU4qUqRI0G4omQ7KIlUgSTgQqkwnSXct6UZIQWv3338/vf/++2S322nHjh1UuXJlysjIoO3bt1NaWpr4VcFrsb0/hw8fpuPHj8e2UlWb4oAFHChQoICYE4JVffDgQTp58mSwYuq84kDCOVCoUCGqVKlS0H7s37+fTp8+HbScKqA4kGgO4AWuQoUKie5GQtsP6tIAS+NXX30lOtmtWzeh4NpsNrr11lvFsV27dtFvv/2W0EGoxhUHFAcUBxQHFAcUBxQHFAcUB8w4EFTh/fHHH+no0aPi+jp16hD8dvHRvil89NFHZvWr44oDigOKA4oDigOKA4oDigOKAwnlQFCXBlh1p02bFrCTcHUAJm/VqlUDllMnQ+eAcmkInVeqZGI5oFwaEst/1XrsOaBcGmLPU1VjYjmgXBqIAlp4N23aRNOnTxd3CQ7PmAS0n4IFC4pzMngtsbdTta44oDigOKA4oDigOKA4oDigOJCbAwEV3k8//ZQ4E5u4CkkmEJGq/cAKWbx4cXEesGUulyt3C+qI4oDigOKA4oDigOKA4oDigOJAAjlgqvBmZ2cL6DH0DagMPXr0yNVNmMgHDBggjqvgtVzsUQcUBxQHFAcUBxQHFAcUBxQHkoADpgovLLp79uwRXbz55psFJJlRf++44w7fYZV5zccKtaE4oDigOKA4oDigOKA4oDiQJBwwTTxx1VVX+dwZAvW1Xbt2IZULVIc6pzigOKA4oDigOKA4oDigOKA4YBUHTC28VjWo6lUcUBxQHFAcUBxQHFAcUBxQHIgnB5TCG09uq7biygG45CDIUpHigOKA4oDigOKA4sDFzQFTl4aLmy1q9InmwCWXXEIPP/ww3XjjjWF1ZfHixfTtt9/S0qVL6dixY+LaJk2a0COPPEINGzYU+8OGDSMEWY4fP56QNVBPgOO76aab6LnnniP049JLL9UXoWLFiomsg506daK77rrLsJ5cF6kDigM6DgD28Z///KdI5lOuXDnd2dy7SnZz80QdSS4OhDt3I7FVOHPsPffcQ4sWLfIbdEpKigiur1mzJj3++ONUtmxZv/NqR3EAHFAKr5KDfMOBZcuW0UMPPURNmzale++9lxo3bkwrVqwgZAuEUvH9998LGL0+ffrQyJEjaeXKlQRlWE/IJAgEEiRdQWpt0LXXXiv2ZVlYj2fPnk2jR4+mU6dOCYVanlO/igNWcUDJrlWcVfUmmgPhzLEZGRl+cy7m4IULF9LkyZMJgfSY85E7QJHigJYDSiK03FDbeZYDUEBhEYYV97333vOhitSuXVtYafv370/vvPMO/etf/6Lu3bvTq6++Sn/++WcuhRe405MmTaKePXuKJCtS4a1evTohQFNL11xzDQ0ZMkTUAwuyIsUBqzmgZNdqDqv6E8WBcOZY4P/r52NAp6KOt956i1avXi0MH4kai2o3OTmgfHiT876oXmk4ACvqBx98IJZ9Bw4cSF27dqUHH3yQdu/e7Su1YMECYWkdMWKET9mVJ4Ej/cILL1CHDh3EIVhvoTj89ddfhCyBWoIrxL59+wiWtFCoTp06dO7cOQJutSLFgWg5APeYuXPn0muvvUa9e/cWH7yoSflSshsth9X18eQAVthuu+022rFjB91///1i7oa72JQpU0LuRjhzLMqCYPFVpDig54BSePUcUftJxwFYb+FvC6UXyi6UArgjPPnkk76+rlmzhuADmZqa6jum3cB1UHIlQZk4ePCg8PWVx/D7xx9/CN/c5s2baw8bbq9bt04ozbAGq+UzQxapg2FyYMOGDfT888/Txo0bxepBx44d6auvvqKvv/7aV5OSXR8r1EaScwCKJ+ZmGCjKly8v3BAKFSpETzzxBGVmZgbtfThz7OnTp+mzzz6jUqVKUZs2bYLWrQpcfBxQLg0X3z3PkyM+cuSI8MuqUqWKr/9vv/02HT9+nEqUKEFr166latWq+c4F22jVqhVVqFBB+Hy1bNlSFM/KyhIKLBKt6AmBcLAIg2AVPnDgAO3fv18o2Ebl9derfcWBUDkAxeDjjz/2FYcSPH/+fLr11lvFMSW7PtaojTzCAfjnSvlt27atWEHDqpx2Pg9njkVgsTbpFZ4DCEQGwRCiDBB5RDDi3E1l4Y0zw1VzkXEAyql2cpTbeKsHFS5cOCwIMrvdTldeeaVYWpPLxXPmzKETJ06IZWR9L0uXLi38w9LS0ujQoUNC4YXfLgLh0tPT9cXVvuJAxByQL2CyArzInTx5Uu4Klx0luz52qI08wAGtTFesWJEKFCiQy+0gnDkW18Nft2rVqgIhZ/v27dS+fXv6+eefqV+/fnmAI6qLieCAsvAmguuqzbA5gMlQS5jwQC6XS/zCd+uXX34R20ZfsAA4HA4qWrSo7zSWhseMGSMgbhAAAXQGTMxGbhG9evUiaclFINt9991HH330EWHJGZOuIsWBWHEAS7JaAuQSgim1pGRXyw21newcCEWmw5lj8RL47LPP+oaNefz9998XAcrXX3+977jaUBzQckBZeLXcUNt5lgNAY0CSic2bNxuO4Y033hDIC/DblQTLbIMGDQTKAnzNZs6cSX379pWnTX/hg/bSSy8JJQSYjwhaU6Q4EE8OKNmNJ7dVW9FywAjvPFCd4c6xgwcPFqgNb775pkBoCFS3OnfxckApvBfvvc9XI+/cubMIisBbv14B3bJliwhGA+auHtwfaAwzZsygWbNmCQuwNrAtEIOwLIeoY/iSff7554GKqnOKA5ZwQMmuJWxVlSYJB8KdY4cPHy58d4HII1f+kmQoqhtJwgGl8CbJjVDdiI4DWDJ7+eWXhQL6wAMPCF8uIDkAkxfuB/DxhTVWT5dddpnwj4R7AnAcUS5UQiAGLMRQeEOJOA61XlVOcSAUDijZDYVLqkxe5kA4cyxc0e68807xDPjuu+/y8rBV3y3igFJ4LWKsqjb+HECGtRdffFFkU/v3v/8tonjHjh0rYMYAaYa0k3oqWbIkIT0wcCJDxd6VdSDwDRHBQHd4/fXX5WH1qzgQFw4o2Y0Lm1UjCeRAuHMscNrh7jNq1CgRWJzArqumk5ADNg6G8I+GSMJOXoxdOnz4sIDcuhjHHosxAzoMVle4MACsX5F1HEAAIZJ7BCP4T2vRBoKVV+cVBxLFAfiQVqpUKWjzgCaUSDFBC6sCigMJ5ACeg0A7uphJoTRczHc/H48dloFwcHnzMSvU0BQHFAcUBxQHFAcueg4ol4aLXgQUAxQHFAcUBxQHFAcUBxQH8jcHlMKbv++vGp3igOKA4oDigOKA4oDiwEXPAaXwXvQioBigOKA4oDigOKA4oDigOJC/OaAU3vx9f9XoFAcUBxQHFAcUBxQHFAcueg4ohfeiFwHFAMUBxQHFAcUBxQHFAcWB/M0BpfDm7/urRqc4oDigOKA4oDigOKA4cNFzQCm8F70IKAYoDigOKA4oDigOKA4oDuRvDsQFh3f8+PE0d+5cHydTUlKoatWq1LZtW2rSpInveLCN5cuX048//kjPPfdcsKIRnV+xYgWNGzeOnn/++YiuVxcpDigOKA4oDigOKA4oDigOJB8H4qLwbtmyhaCsIvc76MyZM7R06VL68ssvafjw4dSrV6+QOIOsNrNnzw6pbCSF0K89e/ZEcmlMr/nkk09o7dq11KpVK0IucT1lZ2eLQw6Hg2w2m/50QvfRNyR9QL+SqW9IKLhr1y4he7Vq1Uq6vrlcrqTlG/oGMpO3UO9zqVKlqESJEobymawyDbkJNn7DAcXhIPqGlNhIbY202aHehzh0jZKdb8HuKeawUKhMmTIEudZTXhh/ss7T27dvJ2TKzMjISDqZxn0FJePzLRSZPnXqlF5U8/R+0aJFw+p/XBRe9Kh06dL08MMP+3Vu5MiR9M0334Ss8PpdbMEOLM74JJpgZZ40aRLdeeedNGDAgFzdgWKe8utHRE4HK0mOXOcTcsCZQvbmXSkltTr3y07ZsyaQbf9Ow67Ym3UmW+kK5Jr6g+F5Kw5imvp9wXracOgEvd6rhVAujdoZVb+h0WHLjqXw/eucWpeqFS9LDt7+ffty2nnqiF97lYuWom5pDahUwSI0dt0sOpF11u+8lTuY4LNZoRpcsz0VL15cKL2Rtud0mk83SM9aYMLHZOMyoSobkfYj2HW2Gg3J3qQTUdY58uzbSa45E8Ul6JuzdU8ivk/uhX96j2U0IFv5KuReMDlYtTE/7yEPjZ+zlg6cOkfP9WxGdn7JjLf8agfVoHRlap9ah865sijz1GGavHW5OO3kFb0rqjehfaeP09KD27WXJGTbw8oUXrAg03gBi0bezGQa/5tjx44lRKb18ute9Jcfnz0sxyk9+LnicJJ70wqiDUv8zsdjx0xOwbcVX08kN79kNxvUN64Kr5/8njxMU3et9WOFlJvKxUrT1bVa0/eb5tOx82f8yiRqR/YtmExnZmYmqouWtJu0Cq/RaNPS0mj9+vV+pxYsWCCUYNyYKlWq0K233krNmzfPVWbs2LF06NAhaty4MT344INUrFgxUeb48eM0evRoWrNmjZhw0MbNN99Mbdq0oU8//VRYQ+6++25ffWjntddeo6effpqw/dVXX9Gbb74pzkOx/OKLL2jOnDl0/vx5atiwIeHacuXKifMffviheLvXKqX/+c9/qHLlysIyu3DhQpoyZQrVqVOHfvjhB2Hhvu2223xtYwNt4KMlvKnhDRJ/frzp6gnHnavniMO5z+pLx2ffllaTPNXqUPbXr1PKfa8TbVlJ7o3LcjVuS2cFoWodyvr3P4iOHsh13soD9sNsDT/Hn+UzyIxvM0uaK2VW9C2jRHmqXbIivb18Er3Y7npadXgXrTh04UWhsLMAvXxJf3p72R80su21tHDfFjpw9oQVXQlYJyZSyKKRFTFUawfkFh8zShaZdtZvRdljnid7/dZEhYr6/mvot+3qu8U5z84NRMXYsrd0mtlw4nLcdoxXUrJYppdNFzIdb/nVDrJl+XR6ZfGv1LJCBhVhuZ13cKvv9PW12tDfmXPp6LnT/D7soB0nDrK6nliSMm3Ui2hlWsp5ImRaL79unYw6rr2PPKwE29JqEe3eQvrzRvyI9bFAcuo6f5rI5aZZe/g/FkfSy+/M3f66CbpSwO6kAfU70OfrZrI8r4lj70JrCjIN2TPSG4zm7tBqzT+l4vZ0h8Ioldtz584Rli3g26tVPmfNmiUUz2uuuYYGDhxIkydPpkceeYTefvttatasmeA66nn33Xdp8ODBdPbsWcLy/xtvvEEjRowQ52FFLlSoEN1+++1i/+eff6annnpK+P6mp6fTiy++SLfccgvJN4M//viDoCRXqFCBVq5cKVwtxIX8BV/hdevW0Y033iiUXFijYXWFKwYU7E2bNlHFihVlcfG7YcMG3xIoFHIovKtWraKuXbtSamqqX1nsvP/++/Txxx/nOl6yZEmhZKNveYE8uzaT68f3vV3l568ZOfrdRa4Z/4u7smvWn0Qf33r8AI1aPVV0w2hCGlK/C03LXEsol2gyWw6DT778PwXqI5beYclNdsr++g1vF6HwamW5YGGyFSpCtmr1yDn4aV7JKECeg7sp+8tXiQ4l3hUq0Xx9h1/aQFB4tWwr7EihioVL0JAGXYX1tzqvZmw8uo9eWTKBzmSfT2i3T548adh+wYIFqXDhwobntAfxPNIbLLTnE7FtKr/cGVuDNmSrVJ2yf/4vOQcNT0T3krZNM/nVdrhvRjM6yzKbjMqu7OeJE8YGEehFFzvFTeHdu3evUBa1DIcvZbt27XyHRo0aRe3bt6eHHnpIHIMPq1Rq33vvPV+5Z555hurWrSv24Zc5dapXYcCNbtq0qbCuIigOhF8ozzt37qSOHTsSJrJp06ZR7969xZsQlOrrrrtOlNV+QfmFAg4FuXPnzuJUy5YtqV+/fkJ51ltqtddqt/GAh/VY9ld77mLbtlWrS7ZylXk52H+J7WLjQ6jj7ZBam8oXLk7vr/yL4PqgKMEcKFpSdMDeoDVlvcPuWezy4LzufnJe/wBlj1LKg9ndKcTuTn9lrqYJ25axW8MxKs2uOc+1vY5uqNmGxq6fZXaZOh5rDvCKhLPvUMr65FliE2Csa8/39eElDq5lP25emO/Hml8HGLHC+9tvvwmFsEGDBjRo0KBc/Fm0aJFQDKVlFoonFFoQLD0IQIOLAa797rvvhCIKqy/8BF9++WVfffv27RPKqjwAfysEaEiCDxWUaRCuhXvDsmXLRHAblFy4NoDQZoECBejSSy+lP//8Uyi8QGVAP+B6AGVWSwi0Q91QuiXBDxluDbDshkrob40aNUyLw3osFWpZ6LHHHiO0D8uZdNWQ5/BrZpXQliEbB45lNCRb5Qxyr1tExJaoRJO9zWXkXsmuGKeOJaQr/cp4qJfbZCGVrXX2zv34QXDEr28lChSmthVrksvjpsX7t7HPVnwslMVSChKsu79uW0rtKtX0KbwtKqTTsoM7hOLg19E47BQpUsTQ39HIMm3UHfyfjOQZZUOSaaNK+ZgtNZ1sdVqQ58Au8qzjh5FVD3P2QQW5Zk8gOnbQuz1rPDmHPk/E9wsKcLypf1n2sQ7gJqLvD16c+qY3p5+28JygIbggtCyfwffXRgvYbSaWltcj7MbwyZrpvtawP3vPRmpctorvWKI2opVpzNEI5tQTlpXNVkT0ZY32rZBpJ7vjePZsJXtlfh7hU6KMeD7YajQiz5ZVRt1IyDH7JbWJrVEhtx0vmW5arjrBxQyym8yE1TajOTkaX/VkHm84fYtY4YVCBgso0BagtEGZ1NIvv/wizmNZCIEBuAFQSCUhuvXJJ58UFlMgL3To0EFYXCtVqkTVqlWTxXzbPp8ofmhqAwWw5C/9VWANhgvEtm3bqEWLFsK/F1bdxx9/3FfflVdeSUOHDqUDBw6IwDAEqcFHV+uHi8JYpoI1WD8u/QQp25YNQLHWEurApGhGeBGQ1mhZBjyTaAfascrz4OW53kO5bynMC4O6+bydl67o/FnynDpOjh43kufkUfLssN4nChZtJ/s9Uoe+5GjWRXZZ/NqbdiT3ltXkuMFrwfc7afmOh4qfPkNCAvme+D+k2A8yoz7Z+AFw774LLzNQdhuWqUL72SoFv8Pb6nWk5axsnuWgnFiSh5VwyFtRRwHqXb0pdaxchwo6nNxONl1atbFoim+poGtrtKLWvGR8+Fx8om3d3LezOT7m4Jk/38LjAibcQJMuZLogzyMO/o+HSvAdt5UsQ54jB8T94wg7cm9eGerlAcuhbgT2oF8g/Jc9XL+93RVE9fn/BYIfLyvYjqvv4Z3QH9Li2ii/PPwSVvKMN4DRVqgg89ZB9/L8aUYQoXocWFa6YFGqlGOtRtlSBYpQ/TJpdIh9w6Fn3MkvWuuP7mUZM17uN6tfHq9RogI5+IV7cI1LxKEyxUtSRW5vO/vtSqpd0tvPexv3kIfi9utm/1A8K0CYXwPJZLBOmcm0fF4Jmc413wSuNVYyLeXXN99WrEa2AoXInlaDg8LYwlusJNnLVGQXh3TysC9vPMlMTrXzTSF+DuIFLBBZIdOQXyfPVXrZrMMye/j0Cbo5vbVwd7EF6Vugfsf6nCvbRXAVBWGOjkamY923ZKov9CeLQa+hmEJxhIUUCqskKD3A3dUrcvK8/JXKoVSGoRhDOYQLgqTFixfT0aNHDd9YZBn5CwUc/rJAOZC+tatXrxanpWIK1wJYXP/++2+axq4N//znP4XCK+uQv+g73tBRn7RSQ6BgPb7jjjtEMSjDep/E3bt3U7169WQ1lv06tvG4GKXBww85PcGyCyuq66/vvKcKFyPHlbeRG9HmZ80VJbPIWX39gfaz2O1tCM9CU5weykzJ9hUtnlKIbmXlYUzWUTqrOe4rYPUGP8jRN5DTyUGBdq9yUrZQMVYqGwkLKjycVh7KFGXwdXVGCxEkJiPLYRnDg1xbxlc4mg3WMvBf6OCpL5QCidKwiC3Kkpzc7t2NutMvW5ckBKWhXXnzVQrZx2h/vTLtZJm2h1ZV4aKMptCRXJO+9K4a8MPc0fdO4VcbbkCkkex3sLs5SMVNkzVy0/7ILkqtVJV+OXNAoCL0qJhKtpOH6PeU2L4EhcQAyE1OQZ9Ma+RXW4dWzt18nVaGIdcIhpRy3qRsVcJDf/ruddoqQt4ukVKYCvB/fd1x78pbobNHaHC5TrSBlWh8yhUqTm0q1KRJO1dSJkfDx5tkRHt+l+mVVTOE/E6V8+2eNV5W58w3/Wq1ot2njtKyQzt4hSLOd8FETvGiAFQYkPNMSsDnvlUyLeVX+x9Bf+qVqkwbj+0Vcp3CfePO4XBSUDxlOikGHGEnolJ4YfGAoovALK3CC8UT/rnaQBa8UcOSCncCKKFQHmHFRHAWEBRAGYy7h/Pb2EKLYAD4y/773/8WvrfApcU5XDNz5kzq1IlhgzQEdAcgLOAP8+qrr4qAM1jNEPAGgvvE66+/LqzNUIY///xzoVzLtjVVCZ/g77//XrwpAScYgXUYH67B2xOCz1544QWh/ELhBQID+tSoUSM6fPgwLVniD/Ny5MgRevbZZwmuCvqXAIxH4ufJPkjlHGORlgJ5Tv4GjP7N3ERuXmr1SFgwDrRxXD6IPHu3kWer9wVA1qP9DRQ5qy0XbHuQuzOtZsVAizbQnJeDEKH9587kWTrDOPCwn8BuA5uP7aenWvYlbWTunJylK7gzYGq7OqOlCLbRlgnGi3DOg296lAZ5fQG7Uyi8iURpCCSPRktosu/y10yW5fmAMi0L6X7di/4WKxk4DEsVmzbIvWJW2EFkRrJfvXg5KspLmFq0gYUHttFdDbvSrXU7EBTH7azsvrl0Ip1OcPCVji25dgPJ+eGzJ2nb8YN0KttrIYKhumm5an7/hVwVBjhgxLeTXHc/foGE2wTcdX7esjiXW0WAKi05JSPazeQyGpmWdSZSpjPbdBHyazZfdanWgLax1d3svCVMj2GlVsm0lF89X+BiNmPvRlp3JPHugWZshEyDpPyZlYvlcayOX3XVVXTFFVcI+NlY1h3ruqJSeNGZ7t27C+UPCqpc/of1tGfPnn7Z1eAr+8QTT4j+YyKBMoybgiUlueQvo2Kh3KIMlFRAgEGBhotC3759BULCiBEjBDKDFq5MIjcA+QH+w7gB8BcEIsPGjRuFry78e+GKAWUW1tv+/fsLBVbLVCibQGeA6wNu4EcffSTaeuutt6h69eoiUQa269evT//4xz8IMGRoGwQ3DfQTCi/GK+mvv/4i+CIDZk1P77zzjiFKA8DMwVNgOYZNBzL9Flft7XsTnThCnp0bw64qkgvum/55rstgPRo27bNcxxN9AD6yoFoMDaYnKLqgHlUaUE+2Ah9hN4JJOxi30iIy4pts6rw7mwZMel/uJuTXzM8W/1/ty61Z52DF1q+ImJUN+Ti77QAX13H1MLLXa0WuP8aGreyatfXl+tm5TgFj9r0VfwqXE6fNcUFJzFUyuQ4EkvPVDIUnCSsYlzNmrnzZk8fD+TXi26L9WwkfuAmdZBxpvCwkA5lFtMOYI59HgfppCUpDjGTa6D5ox/Lqkt+0u3lu2yqZNuPb4L8/yhM8MkN2sgqlATogdCoYJ7t06SJ0L4ARROP+ZhWjQ1w7NG9eJmqAhRWECQQKZ7du3XwXASrsgw8+EPtAPZgxYwb9/vvvAm8XNwfpgkHwOwE8GBREMBB+wLAUg3EIZAMkGNwQkI74s8+8yhNweEFAbkBAGtAc8MYB5RPKLwLaoIh//fXXIjhs8ODBwmILZRtKsZZ69Ogh3lBw02CNxfkJEyaI9uC+AexfBMFBKUZ/0B76iMA29BFWXCjDZcuWFdboyy+/XECrIYkEtkOxGGj7E4tte+tLydGpH2V/+xZRkluhYjHeQHV4zmWR5/T5QEUMz53KOk9rD++mSkVKUquKNQzLqIMJ5AD7sSMYx7NzPdnhN85IIFbTOfav9llErW4sQP2esyzTZ8KXaaMqEfzzcLNefMpD32yca1Qk6mPHGag/WZTdqAdjZQUJkGkrhxNO3UKmz+YdmQ5nbPmx7F133UWAfwU87PTp04UuBuMkIFcBHJBMFLXCiyQNsKT+9NNPYlwYMJRQKH0gWFSBg4vANFhzg6EeAIEByiNcHqSCCKuptAKjTrghaJESUA6+tZJq164t/H6xj/aBwCCtzzh2ySWX4MevDuD5oo6DBxkQnZVhKNySoADjxsH9ADi7UPK1TuE4L/uKvkO5hfsFaOvWrQJ/GMeMCKmDYUXWfuByAWuatJzBeqb9GNVjdMzRcwA5et1C2aOfZWVgg1GRpD5WkRXMLmn1cn1alK8eUb/dC7aQa6LXohtOBfM4kA3wSViGvbmOV3bCuT5RZVOZfwiCa8SBdxyWF3U3YPHSyqHcDtVygP+/vEb/G1Xn2CLmnjuRsr94hYiRFByXXGlcHQdO2dteTngJFMFmxqXy1FH3HMb9nhx9kF5RZ0F6ulU/KsyBk88t/IXxci/438MFoVtafZEtDX6T+YnMZFr7vAg0XjOZRnBzSMQuM/buN+QuGqpM574yzx9xT19L7ik5/sYGo0FGtCt5XoMveCAKJNOBrsvr5yB7+vkV+1odyooxIsEWdEHkNnj00Udp8+bNdNNNNxEUYsRiJQNF7dIAyy6URCRZwPKOdGfA4OCbCoUPA0dAGpaJ9BOJHvUADPvXv/4l0BmGDRsmeCSVZ8kwuDlA+ZR+KlA20YYRwY8XVmMtYZLTR9ciOA0BeCAsvSJQThKERSq4cIXQwqKhjF6Q4A4Bf2L0CYovgt6Mkk7gWgTQ6WHLwBOMD23q68Y1GG9AlAaUqVKLo9fLitSR9vZ9cFlQMoucDXqhpsDpU6fFXgG+104OqouGgNdZpZj3xUnWg+A3pHNsdzhTHgrtl1dQJy8/SnsPn6eB9bty8L1/31AvLFzayNyaHLizi9P8SlSGkhzNjoj2+5v0jKmVSqI0YCBQHu2OqN9DCX0vw8rJgTMn2GeyEBVNKUAL93NWpTCXkrVR03i444UuUtL/5/T1hI3SwGggtrKVCElPfFSiLNnLpYkMab5j2CjKKWRrNibP4X0cDcNQUn2GeDMBnjsjipnKPsuNdMPA/KWXG7824rwDuZm4YD8HMR6l/g26hSQ3RnIOuW/MgWqAIlt3ZA8niOjsG0lBThoBf97jzKcsdqsZWKc9rWUfRrj3BCKt3IQSbR+orlif06I0YH6Vc3sk7eD/YPSfkM+mwCgNF9BhbGVzViXCkekgHTaTaSvmmyBdCfk05Gb8TE7nzbrDNQ27873xf1FvxrEgmMOwUnBj7ba0k4MeT/C2fu4OJNMhd0ZXUCs30CGSFaUB83Q0Mq0bdti7+E9htRw5FZDnAKv7cEvVQ7+GXXEMLoha4UUfoNnD8gk3BSRsgA8sCEojHuB4YEBpDYZ6IC7iLwR+aaHJ4CKhJSiSaFNaVbXn9NsIEps/f75QjmV5oErALQF1aAl+s1DIy5cvL95K5Ll58+aJgDoIEpRd+BhrCfVrCX0HXi/cL/AJNUmFto5g2wFRGtIZYqtUec5o9rM/KgMe7hrLjb6N9puW6w+FvY+XHpAlfzpeqoZ7RskpP1D7k+H5NrPeQiv376ITZ05RhzmTc08IZSqRo0tlaj9rkug/vuytelIqR5u7F7K1npUke5teAuu13cw/fGViscGhiZR1PktU5cQDmF9oQEbIAeJEkC9ktmpfqTZ9vWGuD9EBcGYufpisCvNFAQ9uRE0nZUQ7Kxv23neQh9NYC/90KLuNLmGs59nk0UGT2bteR+7V88iz3htQau98NT+wOMAtR+bNZB9ykxWhTEd6/4Lc3gun8eDPOsMvZOfFfcV4ghEyngFqTxuB3rt6MxFQOkWXKvUE+9p2qVxPZPmT5+qzdS2taGm/643alHKDc8Gi7Y2ut/JYPCPaTZFHSpYjR1t2H+F5BbB2Hjn3Gsh0dtMONG/vZlol0RZCZY4JEgI/DIVRB9U4TzvF/yDUKq0uB7lBgKObFV7MVfKZjXZTi5QSiu0YTusLpRfBZZ0r16VlB7aHJdORjkHKDa5XKA3mXIQ199dffxXGT6xWI+stEn0lA8VE4YUmDyUUPhtwWZB4u7D6AlcXbgVwM4DyiUA0BJKBIVA8YU0FioFEJoAyiQAvWFthOQbt2bOH7rnnHuHmgGAwLPfrcXNhEUX7cJ0AyfqQphg+tPAvgSKNVMG4HlZfKLa4ThL+XMi69r///U8EiwEnGBZrvJ3AQg28YdxAoEy88sorIohuxIgRwrqNN1L0CUo0sIDhe/zFF18IRVtiPsp2YvEbKPo3peu1jLfIFtaeN/o1BT9e9/KZfsdivaMVKG/IV4xaYAtlykPvkGvCJ+ReMDmiSu2HWZE8x5/lM0jfN1tVfvnpdJVfXnk3g7EDzs1x6c1ERYoJHOPs798hOn44ovYDXWTENyPkgEB1yHNYgs5ky/QaTTBSO06egYArfeSxvCbYr4z+DVYumvOBZNqsXg+/+Di6Xc+4020FrijQSVx/fJGruEBu4IckP0mxREIOVoBdO9b73e9cF+UcMLo3ZmW1xyO9f9o6gm27GCvXwwkvZnH0eCiE4Mze6c18clClWBlKL1FOXHor40xr6ebJH9Dt9TvTK4t/pU3H9hFWOCKVH229ybKdSJnGC5dr1i/kYUQd5+3P+MmhXqZ/277CMp/qZLkX2n64kNyHcYJn7dmgPUx1S6VSH5ZdINUAFcWd6qGOnI0SiXjCkelwV7n8OpHkO/GQaSMWIFYKsVgIsr/vvvtEDBcC8pPBqqvtr3Yu1x4PaxtLfQgkQxIKoDOAELyGwSMTGxReWPwQ6PUCw3nBvwME5RHlYfaW0FyVK3PqWX7jxTI/gsa+/fZbYSUG0gKUYywlpKWlCaxdrdKLQDcw9/777xf+t/C5lQktoIACngxvj+gH3AvQN8CUAWlBS0OGDBGO14Bag3VWLGPyWzcQHuBKAV9lWIHRH1wrE1TAjI9rP/zwQ9EHWLahQGdkZIhANiTAgIKtJ/hAf/zxx36H0U9Ym+HHrHWt8CsUYCfr7QcDnM2bp+ydribPOfbVjFDZDTZq+DhnjWDFVkucTcv1zZvkYnxXXrfkbCSBl3G1lyZy+yQrQFplt0rRMmJZ+oVF46PqlllEO/4Pofgswvov3QOi6ojmYg9nEcxGJkEkgDjDiRLMVjByjiPbn50ta54Th4Xfr6aqi2ITiuvtf1+Yb4CDa4b+gZUCZGCDovFkiz6Uwqsdexm39c1lv9NeTsaSH8gsoh3zPp41wQhzNJ4B4ZIbq29M4kVbd7Fepr/p1FVX4uLcXX90D41nHPK3Ot5MW44fYKzo8gIxJRyZvhg4ZybTocZahMujBx54QKyiQ5+DYRJwsno31HDrtKp88DUwk5YxSFhrJQHiCw89mSYXSAxNmzYVyAWyDGC9YA3GZAJFFvi1SPwAxRGKJDKiwXIKP14ovlCiYYGFNRaoDCg/ceJEoTjj4QmlFkgJqBM3EwostvFmAb9Z+NGCUA5tfvPNN8LaC8QGOFLDBQOWZCjcklAO1mj4oCDlMYLNoHxDYcV4ME6Y6AFFhutBgChDH9EXfJAiGW85UK4xNtx8YBAbEZRb/ceo3EV9DBY5DjRCYFI0VJ/jSNoXxyJ1mASYoDyi7OpHhuXn4a36ioA7q/AjIb8JJ84kaKrsajvH99HD2f5sZVO9VmHtuTy4bUsrQ/aM3C/SsRgKIMRALSuk02NzvqV7p42hfWeO0z2NesSielVHMA6EKtPB6slj523VypKtunfVQdt1oOS05CyT64/sFag5J3heRtp3fy9f7RVqOx4cgL8wDHdArEJSrmRVdsGLmFh4URGQC2DRBNrArl27RIYyWERHjBiB077EDLCMwuUBEF5w9k9PTxeWTFh1EeCmZxaURiiUcG9Yv369UF4BDQaCxVdS69athYKJfSi6UICXL/f6pEqkBi0OrkRqgIsDsHT1hJuINxZg8cIlAtZmuGugXqnMw20BhH7BFQIEyywUf1iI0R6UdozXDE8X8B2wgmvppZdeErzA2I0sZ7G2kmnb1m7bKlUnW92WRGzpdMP3ERa0BJCtTnPO61qY3TFmRNV6RMpuGC3aOD89gqjcK3Nebnjbnu5/b1Ed0j3DimM1wTL3WIsr6ZctS0hiVkbTJmTfKBjC6JhRO2byjLLxkmn499oO7SFilx8kY3Evnebrqq1CVa+8Hz9E7lUMy2VmLfZdkfgNe91UyzoBH17Q77ykfoiTU8Cfdx67TtzX+FKBQyyRHKoVL0tNy1ajg5yaePF+ThrEwW15hcxk2igQzWhMZjKNl8BILL9GbURzLJ39XKEoztunCezkCoHg0qJ8Omd2PETAYUYcQbKQvX6aYVd6VWssgiXfXu6NoZi6aw39t+vtwvUBGfxiSZD1DWxR3mOwkgGs6qsymjMm+8qkTDgjAupYF9NTqDKtvy7Yvlyxz8zMNC0Kl1DoRommmCm8WNJH9jOgNiC9LpRRWEWhBIOmTp0qYL9gCcXgsRSEpVAZnAarKhRkPeFhijphIQU8GSDPUC+swVrSMhM+wXCRkJanQEgNqANpkM0IaYK//PJL4cuL4DT0A5Pc008/LXyCcR36NXToUFEF2sLDexq7Q8gsb5IHRm0g1TE+WgKAM/gHAQWP9BSPidTeuD05+j9MnrULyMOKbworB1kfPMn+q4f03bF8396Ys1qxosLRQ5a3FXEDpcqT89bhHPnPsGc5Cq+NA+Hszbv4VWmrUpstjKu8y/B+Z2K7g/SwDze7nD5fO5MiTRGr7xH+49FMmvgvG8kz2rFU4eUHlKP37eSa+xsrsS7vfeKsg1S8NCu+7K7CliJ7qx5e5AbO3Ga/5Aqxn/3pSKQs0rPhotmHn+Qpdo/JYr/nDuwrOYxTW3+1YY6ALMMxUIfUOiLr3LRd66he6VSCH/BDM77MM0ov5DHUFzajG4//g9F/IhkUXsB2Pd6iNy1nH1etwju4Xieh7M7hl5e+rLjd07gHPTLra+HjbzTGZDkGf/M1jIcuCa5b+xmGsCoj+cRS4ZWy/jL7rhspvECHuIqzBs7avSEpFV7M09HItORvqL/S2BeoPOK2HnrooUBF4nIuZgovegs3AAR5geHIgobManiQIUDt3nvv9SVlgBsBLJ9wXRg4cKCw3kJBRLIIkFZB7Nq1q7Cojhs3TlhccR5BYyAZmIZtLS4v/HihEMs0voGQGqSVFnUYEQLW4OaAgDV8oEzffPPNwqIL6+/VV18trL9SwJAquXTp0gKIWR4zqjfaY660WkKorWrD0e9uYVF1L/zL21V+8DtveYJcv11wYzEagwwCdDDUUSSwLVMq5367b1ezEe3asYZ2XhLFUirrLdJP3MGKF0MhGHU/omNVOEVnnYYd6CxPwIcqVKbV2n6uvmCVLlUmlVpWr0dzd6+j09oyJn2rU6pSRP0BDNmDTS6jabvWigm5dYUaoh7ASoWdAlf0LT4WO8h0pHITjFGwvOOFxFaSl/+h4KbVJM/urWRLTfdaexmmzPXbZyIdt3vNfHLe9CjZO/Yj9451bOj1jj/cvg077VUKzfpmJOtmZQ2Pa+6NULoMrDqG14VxcDlnSJRQfVByO6XWZf/J/SIzISxdg1nB/ZlXEDYe20uLD2ylAbXbicCiVYwQ4MqxkDvY9zep1p15LpNzQRisiKhoMJnGqgJPlLyEX8+0/mByZHRhanpDqtmoIyOsnKN62R6SdaSwm0o7zhw5d9IYKsYrTYewytrlBvq/cnVo91brskga9RHHcv0H/GTaX272sP84sl5uZfnDPAbEkEqMrQ04vUjnSm2/CvAzqzenmq9dqqJ4yYOCLSEpRTmWm6ocDwEEHFAG+xCXZhi5pKA4yrR+vDACBiOr9JRg7erPx1ThxZI/lFVYIGXwmmwQ1lsotPiFSwLcF6Bs/ve//xWoCCgHZQmBaHjrhq8ugryAeoC3Zfjh4jrg2sq0vbCEAuoMKAgIqIEbAXyIgeMLq7BMZQz3BbglAB8XAWtwIQAaBH6BIqEnKOSwUl9//fWiLwgcQ1vw5YV/LpR4WHuRhQ2EuuBfjCxrgFDD+LU3GJMryiL7nFTC9W2Gu+/YtUlcwvND7Kkw45sybql71gTywBLG5ObJwDlkpIgqDrTUy6FdPoqkb59lVPFdLze6FCtN3589QusKRqukxlTcZffo4UrV6KWlE4V/GYJ8PjPoZwG7k15r0ZM+43S1f2cfZxcN/VgM+hbhMt31NdtQIYY7QopYfCQhuhkBR8lKVsp09s+jKGXYy95sg0UYsP7UMcoe+6JA3LBV5QcYK8ECfi6HOe7V8xnaj1ecZq6jSGW6/fZ1AVn9YdeeAc8nw0kkLKnJyA7w53XyHAB6g2UdCgdWEbI9bvpt+zIqzHIPiX5uoTcYSxRUXxRUptmVj5iHngCyEkyOjNjsrNWSXJ89Tyn1W1MFVsrKLpjqLcay7969g9rwKpMkZ/32VIyfWemyjDwRh99w/gMITrPzy8Ejza4Q6akxp36w8i+CpToWBAX2KONLPzrrG3qx3fWEgE6t5Rgyfm8TzubKqcVHtr1WwPUdYDeei532MIpWMMp3Lg0YMJb64aIAayvcG7QE2DLAgsGyi8CukSNHCqUSiiGUWii/gBWDQnrZZZfRa6+9JgLDkLkNVlQosVAkYT0GGgL8ZmFNhSUXwWqITMT1zz//vHh7h3KKgDNAo8GhGoomttE3+BFDmf7888/9rMnoLwLYoMACCQImeGQNgdUaVmPpMwyFHH4yCNwDdBlcHiRBoYcCDqUc7hsgwKDhWq0PsSwPRXrMmDFyV/xCocbYIkVp8Ksskp2zZxjq6DzZ0mr4FF4sxdvYGkBQFk4ciaTWiK9J9hzm7yz3YvcioEKvxspB981oRmdZSfhbh3Uqz8fy94fNCwifWFMyojSEPEb2Q896dahwYYDvLjHMnYSXs5WuSB4ECGmJ94X1TXvsItxGZPxDM78UPrtvdxwoXBrkKgGyriGxyf3s04vAtkKM6jBj93oatXpK2AlOEsVas4h2rOpZidIgx2uIDiNPRvGb/fUb3qtZ4fWblE6fEC5Vsmrht84xEi7OxpnsBDix0Wum0eeMw4sXsMPsVx6JUcVsnFsZ+WHUau+LgXaVWZYfUr8LTctcKxRdeSwZf81kGv7qVtBF5dIAzFotASUB1tj33ntPHIZ1MyMjg5C6V0KNYTJBkBuUTyh7MjBryZIlQtlEhjVYXh9++GGBwQun6BtuuEG4P6C8hOoC3BjqhuI6c+ZMoVAiKxpQGqTjNgLYoGziOiiqwPiFmwLKwD1BQoXB3xYWauD2du/enUaMGCH6365dO1EObUIJhyUX6AxAk4CyjnahdANTGAQlGUFtsETDXQOENjp06JBLucY5oE3ASq0noz+cvoxl+2xxcM//gxyXcf+h4PKSJAD9BWVnWdas1RW7V+0kz5FTnLzCfPnQij5ACe6W1oB+3OyVESvaSGSd0lc+kX0IuW225PI6r1fhlRdBxs+fk3viV7zwcWBbspN72XbynD5Pjva1Le0qAtT0ygWUjtps/YX7wl1TPxUJKZ5q2Zd6VW1Mv++I//K4pQzIj5VXqELOO54l99QfyLPNGwieDMN0LdrCS4oecrSpadgduNYgiDKeBL/e8oWL0/tsUUYWN0UXOJBvXRpgGQUUGaDE9GgKF4aPOI8LEapQ3Nq0aSP8X3v16uVDUkB5WHalsot9KMSwAoOAqQtoMiiOyFQGnF2gNaSnpwslEe4IOI5fuCx0ZV9fwJ6BtO4EEqEByi4IyAuwPkPhhcINSyoIyBLoD9AgJIKDOMFfUFaRHg9jBg+grGvbgJIvFV740sGyLBVeuG7AGg3LsxGhLJR2LcE1Ytu2bYJXRlaGeAStuSZ+Tp5jh8heqyl5Du4i1y8fkY0B0hOF1KDlT6TbniOnybMv/vihTTkdJpbDZu/ZGGnXk+I6vKhq5V52yihoR57T/qKckTyjTDxkWtsX7baHVyxsOl88G7v1QP6TnTyH2cp1gpX4BBBQHKAI/7J1sfB53HbioAiOalyuap5ReKOVaaxqGsk0noFWJByK1W22Va9PztuGk2vqj+SemWRuKIdYmeXEE8lCSOQD6y6QbtpVqulTeFvwqgaSXuwzQHJIZN9hyTUymEFWrSCsuucVCosDsIjCOorl/UAEGC5pIQ1UTm9i198k+NzC9xaKLX4RNAaC8goLMvqCtwtk+IAiDkurPoUdHqTStUD2BRMUHtz4yOAFuB3Ihy78TbQEq7F80MMai/TCWtLfcPQblmW4cUDxBSYwXDWMqFGjRoSPlpCIApZfKAiYkPUUj4nUVqUmuZdMZT/e8aJ5G1t4PYf36rsS931MPgjCgo/qAvZJ1b7pI4imYZk0EUyw7sgewnJsMtAllWoxnNOmpIpcb8XuF97o5j209sjukJag8b8LVbk14juuNbte/veMrrP82NEDjCfI/roIrmIFDmTjNNbunbF7QbHVaOz1CdYMBvLq4tUULbWrWIsOc/Y0re+g9nwybSNCHnSeV/EkYTUDyA6xJiOYqAqcJhmBS1oyg5LSltFumym82jKBts1kOpkVXlvtZuS8+TFy/TpazPH68QEVx8NoPH5+xRwcJlb52LrqXssrVXoXIH0l+Wi/ILvqbGT/4Xosa/jI1O/teF7Hf4BDoE0h3gI9r6xiEeZpqa9Y1UZerTcshTfeg4T1E64QUHYRSAbfXxCspsuWLfO5HwCtATBhUHz1Cm8ghAZYdLUExRjCMm/ePD8rL/ZhhcUbEpRd+PhqCQFsWoIfc8OGDWnatGniA4U91mQ5SkPnaziWgic3KLy8tOvsdQu5ls0IGE2MMUaL0iCjiY34VYiXnptxMMGxQwx7d/4MDWw/gNYs+IMO798uijdp34fheovT0YOZ1L/VVbR55UyOPF4pzmE5dmqWi/a4PHTTqWzfpGXUTqTHama5Gdj6QkS0rKdFkTK0Z/tqX6S0PC5/gYHpyvYqDXZWCuWEKs9H8psr+llTCYLaapWqKHLQd2RYqWOcyvPbjfM0JTSbzDhXjhKoOWrJZrCI9pg1WqIM2RjX2S8y/sRRclx7L7kZlszGCA62mo3Jw/JOHD0fKUqDtr/Oa+8REGeec2d8h4ct4QyTOfcdB4uzL3FzDorZumYO7dy9y1fObANyM4ll7gRbw66zSKa1bRdlq2Xvc25qL9EnTu8gd/1T9GKVprRjw2IqzEpR8wo1adOK6XQ390caE6AUao0ZgWRT257cbszBcdfXbE1fcMBncVY8JEEJbl2xBh1kP2JJxRlmLujLAo9D9k1eZ9Vv3GTabAB6Wec51DngEcpe8Ce52J/X3qAd3xu+GG4+Z08Jv3XH1YzQM3+SL/068NgdV9xG7g1L+H9TRLi6ZY//OCYQlfr5Hi8KE7LdfH/c1I9lSCs3ZkMMdjxceUN9Tn4Z1aI0CHe0HLmBS0PjS3gVY9ty6pJWT2QiXHl4J13C6A1we3hvxWQ2brioFKfiHtKgi8A6PpV9jm6s1Y6+3zRfpOkO1uewz8dRpsPuWxJdEJXCC2UUlksgGsBvFe4B8FfFZDJ8+HDhywt3AMB4wWq6ePFiERTWpUsXWrFihVCOkEFt0KBBIr0uzqMOKJRwX4D7AT4IOgP2LizHoO+//16gOEABhZIKRAegJyCoBlZgtI/rQfDJhcKMNiT2L6xJOI++w2otCX8uOGCPHz9eoC3AkgqXC4zvzjvvFMVQ/+DBgwWaBHyDYS3GeTmBIosbohZh5YWlFm0iQxtSEz/22GN5A6WBR5rNb//O6x8Q8EyM/yYA+t1/fydZZfqr9W6CkhkuBYpIdlw9jCExFlLpH7z+4dSyBzVkyDIXRxfDAu1kOK6sV4ZSefggZzSk2gyjlv7jhzwYL37vugM2OnbWRh0XTgu3WyGVd7DrAj/1L0RE51yV0vsuypj/F6XH0U/OLPoZ8D1NeMn5n7O/EVHI6OK7nW4R/pdTGcYskRQ0oj1WnWvYjjxwWdBExmd/+yY5Bz5O9nqt2GR5llzjR5MHySeYopVpYTlmnOasN+4lgjU5h9rKDfyyopZy3+siyUv1HZupaogR80uP2MiWZZ1Ma7uY0v1marB+OXk2LvMdtnFfMwY8ShlVeb7lMbjn/Ea1/vyaavlK5N4wk019SVjW7qjfmZqVqyZcJ/RR872rN6Nfty6NSVIVfdux2o+bTJt1WCfrjp4DyAZZ69hXfORlQCXJ/v4dcl57H8sgW3ePHPD9PxDL4fr7e5/rg5Nd2+yp6eQCNnqUZDTfzz1mpyx+eHSI0TwdqrxphwL0Eb28yfNAhwABoQHJOx6d/bUI3sSxkW2upcqc3XLyzlV0Jyu7SxnW78NVU3CKuqbVFyuQE7d7E2KJg+orrhyISuHFkj2CuODDevfdd4vUcrBqwuIK31qgLcAfFb6vCDqDfy6gw+DTCv9cKIxwRUBSCumfC3QDIBcArgwEd4K9e/eKjGeSM/C3hRIJxRcBclA2oXhCEYUCjExlMhsbgt/gOoEsIHh7hFKLD643enuEHzBQEvDBeSjV6Kf0D0Z/oAQjiA3WTLSN8aJuEPyQP/zwQxo7dqxwkQAU2ezZs0WwnBFKA8apRXlAHVDCofjDrcEoQ5tsC2UtI34wZ49mn93CxcSbPw/QsqZCrdjetBNlj3keTtpExUqRe/HfRPgw2avVJTeyl+UsEXu2erGabVVq+QIyHGzJKABrhkXkmjjGsOasZ28yPJ6Ig3BjAJ4lJnNJAKZvxsp6MIUXL5RG/xm49Gh98WW9+l/Ic0JdF3I6ZHSfPLs2U9Zrd3uTUcQYhcRWsRr/h057IdDYYuyB0qvzD3YwDrDIAsdYxOEQJnCnhTKt7QteJvXk2budst55kP+PHAtxmn0v2bIVK6pclP/j/H9+fM53AiZKX2/1EuVYsVgpXtYAmbaDfYjDnaUQ0W4k03B10Lvc6dvHPgwaRi5mcZmjjTqkO6aXdddf3xI+RuRgZddIBv0SsOD5yS9v7pz51aieaI/FS54D9fO+6Z+bnkYmwQGT3ie4K7yyZIJP2cUFJ9mvHZZhUHtePXuFk1dgxa4kW3uBi46P1WQm06HIcyz7BlAB6H/JRFEpvBgIFLP/+7//Ez4jgORC1rKbbrqJkFkDhPS+UPig8ILcpB2vAABAAElEQVQwOTz11FO+7GKwmOIhiGMgWH7hIwxFD3VjMsKNQuCbluBaAEQEoESAqQgqk0kkoCBDEQfBlxYEuDEokkgKAZSFN954g26//XYfSoPMdjZ69GiB0oAxQUGHhRntIGUyFHucB8GqLP1kgMawY8cOcRwpjoE3DCUefUfqYCjwcM8wmljhE4xAOT2hLCbNhE+cCUonrOcHUgvbChUhW7V65Bz8ND/lC3Aw3W7K/vJVIk4VaytdgdystPjRyWNk42xa8iE4uAK25J5fyYtmB+4LWHYrypO19LWsWbKCCDoKhQlG8mh0zKyucMqa1WHp8Rgru+irLTWD5TWFUh56h3dYOy2bypHx43yKh61BG8KycfbP/yXnoOFhDW9YpSSRaf6vxZoCwUQVZutvRfbhHdKgq8gQVp3TG288uk8oIEhEEA4ZyaTRMbM6wylrVkeijweUwRyXJnuby8jethcHSR4m99yJlnX54VR/v3bLGoqyYmR6W8NpmSVV4aQUTXk14oVF4wnyCUx2pHd/skUfSuH4gL1saAAO+t44BLkZyaTRMdn3aH6hfyGeSo82BX0LQAUwOkJv0sc6RdNmpNdGpPB+8cUXAtEAjaanp/sUPyiHIOnHiW24MuitlLhGEiyy2mQMKI8bs23bNuFPC2suypsFuyBbGxRP+NhKgoIMCy1Ii9Ig+ydRGAKhNODmyPISpQHjWrVqlVDQ4XYBJRnWavgXS2UWb/xAcYDCDYsu3DACoTQgO50++QWUd7howLpsFAGcDFYyyeu4/bJ/IMjeoDVblB7mNMPnyHnd/cLtInsUKwmAluJjWvJgPw9AS2n7bPU2rLnweXys+ZUCE7gBB2HA+hDKJGwW4CNf/IL1Hf9hI3nGdflZpoECgaV+YVljCyiUi5RBT7FP5FIOBN1Hzr5DKeuTZ9k6mjce9MHuczzOI2j1r8zVNGHbMhElX5p9S59rex3dwP7pY9fPCrkLZjJt9rzRV2w2R+MZZmT51V+fFPu8WhaSDJ45RZ4tqzl4rR3Pw22Fm1tS9D8JOpHGbgzDW/XlrIOLaR0HAeNlDAR86sfmfMuBndl0N6fmvqdRD3p2wU+W9hj6j9RHtA2FKtPaa4JtQ8a/+eYbeu6550RSMW15gAi88MIL4hDK5VmFV6b2xUigcOoJWLdmhKAwTDJa0u/jHKzCYCQUYK0yi3NwMdASJh15M6Fcw01APoTxIIVSrSU8dHEeH+l7GypKA5Zu4bYwYsQIkVwC/UOA2tKlS0UTcHWA9RcE+Db4DwdCaUAKZny0BBcHKM4YkxFvLJtIOf2qPb2Btiti28MpKD1wF0gk5USEu2ZPEH6O6IqLA+qcQ9nFga2VSBxgCC2l8Zm0qvv2Ft04Be16IrY4SzKKyncvn8md9qIAyHLx/gWG5bML/kfX1WwlUmSuPLSTdrPloVIR7wtFoP4kK0qDAM9Pr0/uBZMDdT9h5zwc7OPijyTPmgW8OsGrEgwN5eh8NXn2bCV75RpE+CDIqHIG2Wo0YuVilbwkJr82rh8plt0r5/jXV6aiUGA8jErh2W79kqt/45HtHTl3mj5ZM913MfYB+9e4bBXfsVA2zBTeUK5FGczR8tmjvcZShTfG87STg9RCkUG4OxB/AFOJ5C3updO0Q076bSOkD6D6lCvkjQ2SA5i1Z0Mu9BR5zugXVtzHWlxJv3CKbUCXgQDZB/p9+wofktBv/HL2TOurRRIXwPlZRfFEaYASi7gn6EBS55LjQrA+VryTiSKy8MZjAH369BHBbkj2AOVPSwgSq1evnvaQbxt+gvhIf5VYozTAbQLuC/A9hnJbqlQpeuaZZ3xvVPBN/vXXX0VaY1iX4dOcV1AakE3N3ryLj5fYsJWvwpPhFnKF6NogrfsO9qmz2W1+dRnthBNB250ttitrNaADxYuIqkqXTaMWPHFMa92JavFLTKF6zWnlaa97iIOXlLqVZBeb9Fp0Bi88vPIrX24c8AEOoW9G/dUfS02rQ41bXErL5v1Khw543VpQpn2XAdykh9EkLsju0iIFuQ/+L2uiPgv6ZpZbHvniU9kagUldUv9abVnpPWKcj170zbrJWfYBvxFFtPNSISz9Hl7G9uy7wH9tvYG2g8pfDO5NpbTaYtVq3+5NoiuwvnQtXopWlC5FFYsVZ6s3P3B7eF2+SpTk4LYSpWh/0WK0IWfFzLT/YfStELfRtuN1dJBldHWxwr4qq9doRtVrNqXMneupdLvLuZ9uWjjnf77zEW9o+8ZKIWmmAjPZDNSWPmoebjlAb5iiyVxYhVOQH2XFN2j9bH2Vc0GgNmNxLiKZDtJwtPM035oLyCOA4mNEAvhg2zmQDQT3MFsRNmQxn9z8gmxv35vm7N9CpzkdN6hM2YrUil3FZnS83Hg+E6Ui/AogNxHWKOTBDOljaMNu4r+pdYMB1CX8dHORRm4cgm9EtTjxCuZPBKIBBlMre6gT0HnyWFX27YXBIaM4+0DH2rVO07dc/bbwAF72gI6Fj54Q9zR48GD94YTuW67wwrUAFl/49WKSwQdBXngbwC/cCuBXC6Vxw4YNIqUvOIJEDkg2gWvhhI2ED1AmgYIA31ookkCGgHUXdULRgqKLekDY/u233xKG0gB4tLyG0oCodffsXwX/8CXAyW//F2WP+zf7ye71HQ+0wY82H2FiDUafZVQJVsR3/uzO1VSvTkt6+3gm66t2urdmE1rCFspPCxCl7l1HL11yAy1MrSomngG129J6hjD6wMWQRQXl0zZ24i4jyKvnRJBPTmH/85x28HDuwQ+Ef8z8SkTy+gaA5k0jMmLXN9HeUeP7BUid+3LywcP/DJNxKgcHIT88/HsTSZFEtDv63ukNVDx31hdVHs4YQpO/6O5NC1s2DWvcnUad3Ct43KtaY2KwEHrrxC4OtMz06+4TLXqzX+Bur6XIJ7d+RXQ7wfvWvUoDuqVOezrB0EgbWbf5LKdeDj+i/9RqRm9zFPkK/h+lbHfQf7sOppkVKsUIOsmkbyayqRuY364+an47B6gNrteZVnKWt9n88pZevBzVK1WZ3l0xKTgsmV/N1u5EItPBehTreTr7i5f9mnTe9jS5eXXBl5CiZXexAjB25d8C//w+TkW8hpftRztZKTSdz/yqDHPHRG7CrAXFxTxdpaEh0gfm6XKcHjvXPB1iO4Ade5IzC47jNO7z916IHznHRg1YeCfvWEXAO/+Dsw7ieQVUESSqWJck+PAhDjNoMWSblQSdDjob0LcQg5VsFLFkYVBwF4CjMtwPQHqnZbgJ/PLLL+LcfffdJxRYQHYhaQQCwbDsgzK4HigPyID2n//8R5SfMGGCsPBCIYaSe8cdd4ilI+nPi7cHBJ199913AhYMrgNwkL722muFny0UYSAxwH0B1t5kRWmAM/e3334rxiy/wCO4SkBpN8qLDb5ZTikFyHnDAySifENUdq3u009bFtFdDbvSqK63i0QJ208eojeXet1H9pw+St9vnE9PtOxDZxlTci/vIw2kljwH2DXjbBbZq5bVHo5oO1AEOaJ0z3AfDp87JRRKWAy0CTIiajBGF8HC8PnamQyZ05WfVXY64zrPsDl/h6Ts4r9q5BuGZS0z31xttyHPsXTHAYC+jV1wXDN+Jnurntqmkmp7yYFtNDVzLfuYXkuANMI9eGvp7yEl+wg2EGQO9DCWrz3NGxluVL4JW0Jf5/9JS374IpBGElYgZAQ5juFlCB8gHiQ7YUl41Oop1C+jBQ1kZR4R8/CfhAIcDpkhj2BZWK4SBqrPTKbjMkejY3GYp11/fUf2wY/Te50HMZ8LcXDgXvoPvyBbRZ49R4VuYK9cOuomrJyne1ZtJP5Pg+p2IHwkLeSESAhOC/S8kmWt+DWTaSP3yFi0r/+fZHA81fXXXy90Ouh50MWShcLqCZASoM0PGTKElixZ4hsIArm0JNMOjxs3TggukBTg6wssXWDwjhgxQqA4wB0ABASH/v37i21AnQFZAQ7PgCpDsgkos0Bz6Nevn/CPxZuDTEIBpRYuA7gOjIW/LAiBXwgaA0ICKFlRGuAWAWg1PcECjkkTb0yJIHsn9i1kq1ky+UXizRmWyIJYxrY5CGDeWvqd36SBf4iHuvSh0p53r9vDy95HY6LwBoogTy9RnpUGO73GiTF49Zr9Y0vRT5sX0Q9sCUgGmrdvs0gBW5wfXkZ8Musj5NHoQR6OjIZT1qwf4jgA9K+5h7I/f1EkighYNglOfrNxLuGDAMFAlvRXl/wWVm/dqxlukVMLB1J431k+SdQJhVeudchG3ls+me5vchlDKNUSGQrhZ7ierXfJRkYwUYv2byV8ShQoLOCg3BEYAsxk2kjOjXiCcjGTaaMGghyzYp7O/vwF/1aPHaR3WU4KsZsYsgPq513/wtHvuVewaxInnqAYKLxWztOYzwPN6cGeV9FzyriGaGXauNbwjiIQH3kHkknZxQjCUni1Q77iiivoySef1B4S20jwIAluCFB0YdGVBH9cTBDIliaDtfBGIAkBZkA20ProYh+BaggYw5sDFET4yQLJAH6y2G/SpImswu93+vTpwhUCb+wSdUGiNLz77rt+GdVgTQVEGM7HC6Whc+fOwpqr7TT8gzE2+Mfo355QLpZWMm27vm3W0hytL2Ww8e98h5JpA9adc2TgY8WdRKrWcJQ4K8Z1lC27k3aspHGbFoj+YFnrn4yKsOLQjqRabg2XT2bBEEZBO0Z8NZNnlA1XpgGQ72LkA/jtIjNaXqFAym68xwDlt1PlunSCsxbCjQJuA4BVAh7zAU32snj3K9z2jnP/IyVYvYxWLaKVaSgd+tiTSPtoel2c5+mzRvEHpp1L/hPxmqcDPa+s4JKZTFulfELO4X4KXQWoU9DVYLDDanuyUcQKbygDgf8uluaRaldLtWvX9oOwkBnUZBk8WLWTkHYbCR2gEDZu3Fh8YB2GCd+MoMSiPi1JlAYo3VIJxnkJjYQ+awnJJmQEYrgoDUhCgfaR1c2IYDXXYwz/9NNPYrKEgBopvFZPpDb20QLurXv5DKMue4+xFdXeuR+5p4zzL3MR51yXjICfFj6SYIUCBiOieQFPA0QEWFklIbChPsODaWkD+3ntiQNeo7bNYNuYSENVBIzqwrVm14ej8NqbdGT/8roiYlxsV61DtqLFCdvu1fMQkWPUfEyPYem/b3pzsWyJiuEP2zmN+2RA8/ZuEpnCDE4l9FAdlscODI5/z/QxXkzmbURPMGboFdWb0th1syzpG3xt9fKvbcgokl57PtbbkGk5t0dSN+ZoI0UiHgpvSPO0ZlCmKB05ZeyNO5DnOGdZ02Qg1Fye7zYDzdPa9NSBZBIW76symgsDx+kw8Z+tYmi0Mh1uv4BGBV0K+hMC+nv27ClW7F988UWR0yDc+qwsb6nCC4QEBKLBXUFisMGCOm3aNJGcIdyBQdGDM/S9997rc4FAHa+//rrpshLaBU4uJiCpOCMgDlZmvSIM6zKOzZs3z8/yi31YoTGxhYvSAAi3ULJQhcsLRP9ioo5msjZr03HJlYRMZZggDYnbdXS/kZNA1CXP5pW+IiLn+uW3kms951zn1K0pnJIyWM51fS51X2Ux2PCLwGe3510p2+kM+1Aislb4GsSgDVShjyAHNBKWV1fngJLbWRkqzsuuCKAY3uoq2sQA+fDvlYQJFVZg4ONKGS3O6T+1k64sG/dfETVtvQKJcYUc0V6BkUOO7Cd7hz6CHbai/ILKWKKIMvec5pdfRvMIlYLJH7zlXRwQC7Kzwg6fVxs/5Oq36k5lKqZThVXzxTkbK8BNKtYR2/ILyAgFOFNh422bKDvLJQ/H7Bc+uJOyeEWDl3+vO5Ut+hao8ppc1snXyDGnVihF5xnOb9Cx0yT/K7BA12NFWEaWB6ov4DmN3MiIdrhzDGUffL38y3rMIunl+Zj98n8zL6M0SD4EnadlQfwCa5dTs3syN5L7+GGfTOPewO0K0H4Ohidzz5/ECAK5ScpM7jOxPYLn9IRsNx1g3+T0kpXwJhkzCnWezva4fPLvJ5PsuyzlRsr0pezHi1WSXSeP0NFEBv3GUaa1NwRxHcgzAIAA6HpI2IXYKqBrvf322xeXwnv11VfT+PHjhT8tMq/hYY60v7DmALstXN8nKJzIYoZkFEBlwGfMmDFCoZVWT2kRhaIJ6y38hOHyAMQE+AHjDQRBcEh9rA+ygzIOJIiff/5ZZIwD0gL8kefPny9uIqzF6PvKlStF2mQEzuEzZ84cIQNoA9ejPOpCMBr6hX4/+OCDwgUE52NBVkT/+vqFtL2L/jJ807dV4iQgNz4s8rEjja/WGiByrrPFV0b32kLIuW6US93Xjyg39DnUXRw562G4mQ2M3hBL0keQI7BjGIOMT9u1zheV72DZv7J6E+H/BqVCq8wiehcA+hLDMZZ9y0t1hSrTIkpdkx4VOMj2dldQ9gdPhD3ccOXPT/45+Kz9gqkX2pz314VtfmCnPPg2uab+QG3m/HHheIy3lh5hBTzLRh0XTgtas4PTRxO/iJaVfd6ynlIadaQOnBHpwyIOgUfaoHSagPrSymfQikMoIJEi4P+pl3+JeNIsB/EEaa9j3X4IXbSkSKgyHVHjAeZpbX12dk9zXHkbL2Fy4gh+wSG24GrDEj38cg0XIaS79hxh45CBhTfc/4m2/XC35x6z045SZXieNkaaCbc+WT6UeRoucXBHS2HDyB31O/ujO+ieG1iVE8YTbgC+wgfOmq80yz7kt18Y3OC2CsMidCqstmNlH/FaUH7h3iCNnckwdkstvEj/+/zzz4vUvwhcw8BbtGhBDz/MChNeK8MkLIcC7QHIBlBGoTAjZS/8ehcvXixqg3sEmI23C2RDg/sCrLNwE0AwHG4QrLRwhUCCCkkwx995553C/QIO1wsWLBDtYHkAfsmAVQNSBBTqoUOHiuA5vNHg7QbtI9kEECUwTgTOIQ0x3noQOAcBgMIvg/Rkm/iFi4ZMgyyPQ6HHOBKF0mArn0aevReW5GW/8Gur08yrDGduIicrtFqKd851bduhbNvrpZInvVwoRaMqYxSVv/nYfg5wWJgrUh4NVS9RjoPtVhKy9SBCfgdDLrGBLOkoWpQGTH7SbSjpBhdihwLJv7YKR69BwgqNDGtWUq+SnNGLrTsRESdKcf3wHjlv/Ae9Z2d81YLF+CVtrSUvXmZIEeh3oEj6iMYVxkVmEe1Y6ZPGk0DVJRKlIdA8re2zvXZTyh77MtnrtyYqVFR7Smw7+gzhZCSz2Re+Vq5ziTjQp7SbljfjlzOLyWielugpwWSyMLv0IXPafxmqbSSjryQTmck0dJlYE1av09PThYEQsU+NGjUSll7oOtDPjNx9Yt2HcOqLSOH95JNPTNtA2mEtISgLH1g8ocRpmQ4FVovhhuvg6Kx3doZSKKl79+7CTL5//35h7TVi6MsvvyyUTExagDuTaXql/yAC3VAGkxWuRx/gKgHFFMfxxoKHM6DR3nnnHUISDAgRECGQOSQtLY1uvfVWYS0eOHCgKAssYRD8f1/gdHqwBP/xxx+iPFwi9H66cjwYB5RmPaGvWN4J1wquryeS/axnbzK9zM0QUCAb+03mohzfyXjlXM/VfpADtvIlYrlC5mvNKILcLCpfHymPnOtIQzmEYcIQ1Vu9eFmG/dlHryyZwNBm531tJMMG5BEfPYUqo1bJs3vJVMInHhRQ/mUH2OXCzm5BWW8/II9Y9lu7cOhVC4hBXXH3illC2RnRqx8h+AuQaVZQIKSIQJH0VvRFW6eZTBvJufY6uW2VTMv6A/0Gmqe112V//YZ3Fwqvzs6ENNdwRcv++b/kHDRce1nCtuuxTNtSo4ck0w8gnHk6mEwOqd+FpjHUIMolG0Ur0+GOB7rPP/7xD6pTp45IxvXKK6/Q3r17hVtoJIbNcNsPp3xECm84Dciy5cpFZlmDvy+QFp599llZlfiFvy0iA6FMPv30037nsKP1m40WUQJtAx8YbglAnEB7bdu2JeDlgv7880+R4AKKNN524MAN/GFYd4O5MOCtSK+0I6UyXC+g9GpfEERj/CXdN+R+0v1i6UzlXBe3JVhUfiFnCv2VuVq4NOzjILXSBYswXut1dEPNNjR2/aykurVYoTHyGZcvksE6i2uN5BnXJb1MBxuc5ryj9WXs277CL9W05nTybfJLTLLgRMebOTCKGD2U9XOyWb/M5mgoHTCoJDXBr7fvUMr6hJ+tbI27mCnYPK3lTYfU2oRMf8B5R/BqslG0Mh3ueLDyB79d6bqAeR6uoAheSzaKm8Ib6cAzMzN9PrL6OqAUwl82FmSGKIFlLfgDw2L7yCOPCGUWyvby5ctF0gy03apVK+Hm8NRTT4mbPnjwYOG+ALcK6Wph1scOHToQPlqCZRjtYdI1AvSPy0RaqIh3CYz/0O5VHPnOKTtDpbyecz3UcQYqV6VoGapbuhL9rUl9alT+CPP1kzXTfaewP3vPRk6bWsV3LFk28F8IVbk16jPk2UyRSFqFN1zUEQ4CsrfoStk/eBPoGPHhYjhWndEY4JOLlNWLOfFGJBi58eATZNroJS7Uts1kOi8ovE4OUvPs2Up2BCfjU6IMBypnkK1GIzZY+GPrS34guM2WXj+p8Nll3+LxiwQnsO4i3qJdpZo+hbdFhXSBzAOjhZZSGZGnRfl02n7ikAhiRqCp1RStTIfbP1h3jQiKMPSXQ4cOCUMHVs4TTUmv8MaLQYEQJYAKMWvWLJHBDck0JJQZlG1AciA4T06asCwDIxiuDTiGBB1WkJUoDUBncPS6RSA14M3f0fsOck37wS+YARMfcrDbqtfzDs9mEznXs5bNJA8HPzjw5osPp9i1cQpgCrI8L6PEY8krv0hznmdcOS4XQmnj/lpFiAYe1qiHyMe+kwNwtAQLbmFHAV8UMCwFjctUpT+3s0WQCX2rUqw0HWXF16//2kriua3hm9XNhozSYHVHcuoXqCNX3EaudRwfwC+AWtSRXPIvr2HkEv7jc4AQI27I/4ZF/cWjEwgSUytX4f8bt2mPXKZjKWtInYz6Vh7cSZ1S61Ifhm/7aoM3sFcv/3rW6CPp9edjss/WVxltH5P6AlSSVDINhZbhJonlEnLDEkP2YiUFugmGYCtdgWxFWClh/rhz5kq/oSHhz3X3c2a/8wL/2u9cjHa4aTFP33n8nJgLjazvMWpKVBPKc8cnkzzuYo6CApGhZfkMcb3sX7e0BkL51QZbXsnwfoCiXHl4J11SyWsVfm/FZH4uWOA2FEeZ1vO/b9+++kNiH64OSPiFbGtwBcVvoilfKbxAbUBgGnxyYbGFAzWOScJbNxAYpkyZItIRI8sZ3kBQBkor3BAQmIYUxfDfBWF5ACgMMNnjeiTbQAYR+CpL6/K6deuElVe2g/JwcwAcGgLg8Atf4Hvuucd0SVdeG+qvldG/ju79OenE9z60BXv7PmRv2Jayc/x3RR+xBKZDaSDOuZ7SuL0IhCGO/LV3v14ozWJ5N8jAPsuwwKLJMDKJoMH1OjG0DwPPs0+udgJEX9pUrEnnOQe9PL6dA9QG1+vMk2ImW3Y3EHBK65WqTO+umOQrk4gxJKJNK2U6kvEI1BHt/0CLOmIk/9wIlFzP7q3k2bY2kibDvgYLqmNrp4d9Xa4LYvRfKV2wKLWuUIMem/0N7TtzXKT8/ZBTgQN/ehvLul7+9f3QR9Lrz+e1/aSS6YbtyMMoHRKlwf3lq34QZM7bniY3W3Ylyo6e146+d4o5nzgDpxGSg758pPuQ6c67NkV6eVjX6ZF8jC72yWQOSsOSBTt8xZAqfOyld4sU91qUBmT/g2X30dlf+5K4jGxzLQdoshLI2UDzE02dOtVwOPJlAOhV0awMGlYe4cE8ofDibXz27Nm5hqhPyQtn6aVLl9KAAQOofPnyJH1h4UwN+vLLL8UHvrjwrwVsBpAckLoYqA+ASvvxxx9p1KhRYuk1g9EdoPxiH4kukNoYSvGwYcOE4op9KMYI4rvmmmtEG7t37xYKNerCMQTcIQAO9cLvV2Z5E4X5CxBpgG7TEuqA+R+uCwiW0xMUbyvJNf1/vNS15UITsFbBohuEkHMd8Dcpj33ITtTFyLNjA2V/93aQq+J32jV7A3n2H+eUtK0saxTR6PVKp9KvW5cQoJiCEbLwjFo9hfpltKCBddoLBeHnLYtp5aHMYJfG/Txg/OQkpm0cvltYRgtG+B/AVSevUCSoI/ifePZuj9sQP9xro+xfl7IvZvO4tRmooSOMLz106qfiZQ/lyhQqJtLRJmuWLjPkERg6zPzNtePHHG3kjmP1HK3tQzy2bbWbkS29AbnY6GFv1dPSJt/ZY6csfsQ9Vjnv+hW72RiEwGNtxsKTDItZpVgZS3mHys1kOhR5jqRzZq4KcAF96623DN0yI2knFtfkCYUXE8ozzzyTa7xQhGWWNgSKQXFFuUsvvVSUbdeunUBYqF+/vtiHNRcuCPI8lNXbbruNNm/eLM7XrcuJFFiZfOONNwS0GATk77//9rULXxUovECRALQaCJBkULBRx6uvvirgzi677DKhFON8y5YthTINpAq9sovzUG6hpOsJvmHoS7yW3rTte7ZcSCZB8ElkCy+iubXk2bmBskbcrD3EOI4HyfXNm+Ri6y6/0gncR/8CCd5zi/UyyzpRnPF372zYlV5b8hvVKFHesJ0v1+d+cUMmNnxgFcCkmKz+jmZoDNKdx3DAmoOJkmdNF8LbzFnWNUIdMZR/rh0wX/EksX6VZAFHWNlABqohDboIK9fXG+bS3hzfRiP51/LLKJJeez7W22YybXZc335ekmkjlA7teLI/f0G7e2G7SHE2EtxD2Z+/GJc03tk8TeOTLBRIJs8zrvuASe/n6upJTn6zJifxEE4ipgNpu19Y5G/cynVhDA6Yya7Z8WibhFILl099XoMjjO8NgyFWuQElK4Paom0vmuvzhMILv1gos3pCimHg3oKAgQsCasOiRYvENr5wrVRooZzCKgwfW0CVAVUB+02aNBGwafDPBQGzF64PILgjwOUBVlhYgUGAJ5OEaEQQLFdITCHhOOR5pCVG/WY3GwFvsHxpCe2jX1gGMHorM7IoaK+P2TYjCDhvelT4dLn++CL0as/nHSte6IMKXvKuht0EaDmA880U3kC1ABYqmQkybKTchrpchWuN5BljjptMR8JghToSNtcQvQ53HWQM7Fi5jnih23P6aNj1WH1BtBHtZnM0FOG4BBdbzSCuH0kpXIwn7dm3Iy4KbxyGFNcmgK8+vFVfwsrduiO7LW/bTKZDnafD6SD0HqykP/fcc6RH4nr88ccFRCvqQzkzHSic9qItmycU3lAGCUB7PFCR+Ux7Y6tVq0YISAPBvQBKMtwT8EHyCekyAJ9fJJLAtampqb4mMWlhGVdaknFC+9DWLvGiDpDE5BU7/AUBNKOuXbsSPloCDBvGAytvwlAa2NcLPl0INssezdZ1fmNNNMG/tRJHvc7b57XIy/7g7bl5+eoCWmkBn4PPVbzpkkq1RKDOvH2bOEChFtXmoB1kXMP2gn1bCBl8Ek3RRgxflCgNfNOsRB1BgKitbCVuwxvUJWUErhG2ui2Jjh9ilJS5iE6Tp/LEL1wYkLEKn6db9SMEso1ZNzPmfY+FTBu9xIXa0bigNPAqm3ApQEptTuXu2eU//4Xa10jK2Zt0ZL90DsTkxBRim/HXbUWLi2336nnJLZdAGmramYizHro53T0dyIyEBVFdg6C1x1pcSb9sWWJJQhejzsUTpQFK7ODBg0XmWa3ehX7BdRTBaslE+UbhhU8uTPbw14UrgyRYZoGqAAsSkksAcaF///7yNL3++uviOnkAE5h0V8AxuDTA5aBbt25B3QsAVwZ/FvgFN2zYUFSJdnG9XqmV7UX6ayVKA7LxOPsOEWkmXVO+ZxDw9JC7KYMEgdJgCyNqPFiu9oKMZtCieW86vG87Ndu6wdefStUbUK3GnWnvjjVUrnYjuiW1Hq2Y7bXO+wrxBlbIpma5aI/LQzed4gjlGKM0VGQjvevoARpYkR8OTAUKF6NCrPAOqlSPWuzYyvqKSxw3+gJUjTxv5xeucPoWSpQx2ow4Ylh4gcRH2UqqiPYc1BHXyrnkOrxP3DYbL9OHijpidJ9zHQMO6tXDyJO5kTzHL6B52Oq1IgfciNYvInv6VRxFfxW5JnzKl/uv82LPc2w3VXS56ToLZDpXf0M4ULR4GcJ/ctPKmb75svSxw5RapAQVOm3+H9BWbb1Mx89VLGqZ5nkUmfA8R/aLj6PLteSaP4k8672ZRbV8C3UbchMqukdqlTpU+fRxIg5ABhXkZ0NhnouP9riBlpUsxvX4r06KQtF8cecOTFnBsuOmqe2acRRoZJUVYDScSzr3p3O82njwwE6q3PNGWrdqJu3dtdGvwrDQSTQuhg5+CQnWN6Qd7l+rLU3cvpzWH91jLeqOpm9+A7R4B0oust8iUB9GRBgGV6xYIQyJiHFKNso3Ci/cDJDKGEotmF67dm3hmoAIwTFjxghraZkyZQhpe6GU4YPjcFkIdTlV+sDghiJ1sJaADoFsavDn/eijj0SQGiy+Vi1tWRn9C8uuh5W37J/YN4n/SD7iY8GIPXd9pLnSd8xsI1CudpELviu/pPDScoWDe6jsgqneanhSS7l8CGV//DRV2MkTGbtglH70A+pw/AR51l1wa5FtnjhuI7zjd1w4TR6K3a/sU06N9hbdyN7uCir2wePUNnat5KoplCjjvBIxbKVM52JcKAcYdcTBmagIfrm8NO/gh6Zn62pvUolQrg9QRsg0B3hCpj0nj16IemeZxv8ve/S/yMMy7WKZTmGZJl5xMZLp6i4blWQ90hKZDtB/01OccjXliT6UumgK+/1zutqK1cjZpya5Jn1J7XX/EbM6lExf4Iy93eXiZSh71HBx0NOyBzm6XkdZk7+6UCiCLczTIaF7HOB5FZ8c6ly5rrDW/9/SXzlOgw86ItRIZYUGv64KJUQijM8KRV73rbw6ctB1nv457zvRQgaPASgJHxzfTX5JJmKETqIfRqkCRejJln1p3OYFNH/vBYs8/NtPcJxGfiIYDZEEDKvnM2bMENlqS5YsKTLsPvHEE0k11Hyj8OJN46WXXhLMBooCTO1AWQCMmHRpwJsInKfhiwvl9fLLLxdBZ9rkEFBQkdBCElIi4xj8bOXSFxy00YaWcINBNWrUEP4qcNgGwccX8GfSdUIc1HzBb3jixImaIyQyuMFSjDYRcakn9McqwgPKXo+XUpkKPD7Kr5nz/3ddQjLymOWCt1VIEy4XUAwEsbXBzZYPO0cUuwwU3stLWcc3P0Yl2U6sIobxAqd14ZHDxKpIqCgNob5cyroT+Wsl6kisZPqqMkkm03CB+vF9cnS7nhFbbidinFf3rPHknv9HTG9lrGQ6EPKI1nXNrPNweTPy1Y3VHO2e9we5F0/1NW8rU5E8+TxGwtEqwzfeSDeq8krDwv1bfJcjBTDcbBqUqUxz93rjfXwnLdjoWbURFeGXv0F1O4iPbGIhu7e9uex3uWvJr5lMB3KtjLQjkHOsgn/77bfClXP48OEiCResu3369BEQrlJvirSNWF6X9ArvLbfcQvgYETBz8ZGENL5I/YsHMx6sel/a7t27Ez779+8nWHvxoJYklVxMXgMHDpSHfb84Dwsy6n/ggQcEugLaQxYRkOzHu+++K+DH8NYj6corr/QFzslj8hfBc1gO0BP6BqVcugjoz1u1j8CE8095IdasaiPces1ywdtK8+TP1jE/4v1QINT8rrFgx71kKuGTDBSriGEzxBAjJdho3Jgc4y3PRv0I+ZiFqCN5UaZD5Rss0dl44WSXDTrDL+wW+B9bLdN6f0SzscdFpjl+QiQD6jlABI1lj33JrDuWH5+xez3hk+x07NwZxjS/gJRTvlBxgYJTki2v8aAf2LKLTyLIbJ7W6jux6hf0H9QLXQt61Zo1a0QAGwx2+A+hL0rhjRW3TeoBMgM+ZgRfWzMaOXKkQFXQny9ViidvpmbNmhEUXVhlgfIAZAbAnqWnp4s3/X379gmrLDB44VZx7NgxYd01wx+FK4bWpxht/P777z6UBqO3MiOLAq676Ijhcui8fzCdJ4uDDAsUvOhYEeqAo4kYxqqJkXIbqnKAic9IntH3pJbpeFrU8pNM619GQxXSMMslo0xDEdaj74Q5LP/i7FPu3raG7GzhtXfoQ65x7GaTBIGw/p1Mnr3JO1fSM62vpjsZGg9JfnpUaUjAiM62IstZ8gxb9CTaeTqc4QCZAboN8iRgpRx6EIADYPDDyjr6kkx0wcSZTL1KYF+QElgPr6HvDiy2gA7Dwx9vOEOHDhVF5HItkBXgCoFzcHWAWwOUbEyCeoUBmMASF1i2s3DhQh9Kg5HiHtOJVDYao1/4rrp3sAXg4O4LNXKwjy2jocjT7oblR57j6HQ7g5nryXPquKG/Yq5yJ46QjYMotGSDryOnNs6rZGOfUVvF6sKn07NttaELiSGPQxhwtBHDVqI0JJvCa4aegBTD9voceczBRO5VHKXOKaB9xMdsNRsLK5xn6xq+h2t9p0Ld8OQTmUZaZoEywVZyESEPS6+GvPNBDRKR/iHEBmgu9duMVqYxV0djgcID3eihjrkexo5YEZAZ8HEvmUYpT45mF5HJxvLFy+j2zv3IPWWcf9P8ImWv34b91AqSew1bHvm+6MkMBUdfLi/sQ8l9afGv1KtqY2pWrjqN27SAbqzdlrP/Gd8TwOj15RTYP23JHfuB8XapXI8V5z20JwdPulGZKlSWA5P1tPnofso8dSEAVX8+HvvRynS4fXzqqaeE6yhW71544QVxOQx///rXv8KtyvLySuGNgMXw/YUvcMeOHf0AlxEsB4W5RYsWIof0iy++SJ06dRIKMRRXvbIbQdO+SyxFafC1Et6GrVZTcvS4kVy/jiYbR/IKlAaeSJwDHiXPicMCxzGlY1+RetU1nV8YqtQme/Mufo3YyjP0Dmd5c+kekKJQTi54pG8FCWW3DOd/Z2Wak797j1Xl5CH7d4oUr+JAzhc8HRGZDAoXQSLUqHFReSRf3Dks/TRrfTmVY2V31851VLpNDzp7+iQtW+Tv75WaVocat7iUls37lQ4d2BFy5G/EEcOibxcZSoMGPcHN6AlSbpwMyeS8/BYRuMb+RuTo/f/snQe81UT2x+cJ9raKFRVFxN5QrIiLvS22P4rYFntj7Q10Fexd1LXi2li7rh0VVFzFxiooKmtBwV6wYAdB7j/fwXOdm5fcm9yb3OS+d87n815yk8mU30wmZ86csr/57el7igZnbXbYzzR5z870GBPeg5me79KZ418qPyKSGtO4rmoKLir18esrdvH2K5g1u2xlvvDe45mF30y7HQ8yLz57txeR9iebcoNNetnrU77+zLTfYnczccIY8+HEccVcolrOVz+mG8dLQ9OK69i5rTDhtVn40MneIqtppS7eb2Y1h7wdlDab9zZNHbw50HNfVqR5FzBtd/c8PRCu13PR1WarPmbavVeZA7+f5q3bZnmFCfOCU8wjpZNmY9OZb6J4QgirFkZjs3tte+SDV20SGNoOnlvLedrO2WzOxCtOr07re64kFzdveCHe/bSGFz2zV6f1zNC3Rpl5vpklyOnuGe8t40RNa2vzb2fufe+/5rWv/rAB8ueV2m9vgRWmypBamb9nvMUWW1heCBVMcaNK8K48kjK8vl559913AxlTpFviakykUa+99po1jCMoBlJedFkIKYwjZoiVPy7KyHPllWcxab7iqv6ZK4t2LNi9j9psK3mTs6dvNpsnwS189LadjglDWfCkCTM8bwXQbwu2s1bnhfuutozCzOceKmLQtOwqpu1+fzcz7r7cmK8/L14vnvweC17iuHOcrdsOs4zUnrzTk66t6UlHlzEz7vGe99z4+OmlH5rMF9ON2cUz9PF9KvxJS37f2HHpkt9p/FjxT0ub7T1m/fjnbjcErjDet+my7nubScuvakZ+8j8zZ5vZzf6rbGqW9aL1EI54uLdTNG5O7+MXwcq4USyG8zCm/d4TjDfGxPNIm633NL89eZeZ+ez9dggQgXC21TYwM7xwq02rb+T5J13QTL/wMLvVPNNbhLXd+yTz2whvLvAMuUKpxjE9yvM88oO31vvLQuFjuh7j123fVat3N/dNGmOlalzv03kj03GdLT2J24MGiexGniFbv6dvsv6y1/nxc7OfN67P+NTzLyuZ6JgWJDw/YHObtrsdaaaPHu7FjP3O8/yyreeNxvMrO3qE9+5PLqZrWmI506b30Z4611x2/MkcSYI2XvCgmaMfNxJprc2OB5vZV13PbMo861GoFxx7N91/QR45Zo7/xGtDwXu3qp93kcAesvJm3nx6hw1zzRiEEX3J58O9w/ztTL81tjJzefMrES6RDAvJnLv273Puxz99a977cZZk/Oo3npRk9rjvSpuYnzzvC/d4kuTiOC5J0bJ/RDHwzAMCyvD6euGqq67yXZn1E48PBK6A0N/F6O2bb76xBmznnnuu1WH585//bPbZZx+r13vvvfdaMT+6v6hI4L0hiIjoNmKEN3k5xHYAEdpQXfCH6yMZ22V5oqZFl7KT7PTLjjGzH3FhSdVmegxDARUHIc+YAGpapL0n9Z3lycJe8CQPbXf726xJOYjZtYma//vNswhvu9eJZrYNtvEUQaea3x65MZDZ5cn/ebvPb01tsgxv85yyvbLMfO3MZz9NmcXs/l4VJmi242B428/rSQ49nb0Tn7/TnL3hLJ+YUWuclMUwwVCCdilY6EWZ8BjPsliMWvd6pwvznkA9fvvPfXb3oVinX34oGkjO5knVrLrO73qVuC+DmpZewRQ83cs4FGdMv+6N6cnTmyzDG6eMtNLO60nQFvbUjFzG4tWvPjB/WW5tG26YcmEu5vAk0jO8hcDcXnqkb3FntKTGdJjnEYQVYfrmLnZhYzqpObrwzljL3M5+2HlWOsvOwoxbLyhhdqlP04prm5kvP+H5dJ7gCQ1Oc6toZuuwsplx5yXFazPffMHz7XusmbUn5jG8ndcyM245d5aqjk9FrPhQHU8KH3kqaZ4fXlMDw4ukdszkD8zg7ntZvd2Pf/zW/OP10u8sTVqrXQdvfh1v3vvuS9PfcyPmUtQ5d+WF2ps/L7WyOcmbm+OOY7e8pM7LjemkymjUfJTh/b3niMj27LPPVuxHtg0wKsN47Y033rARRoYPH25DEcPwMlEecsgh1g/vKaecYrbcckvrmiOIUaAwQiLjw85P5MMWQZ71daXO6Jbxkbbk31b1otu4k8BsG+9gjMfoFl2J/Z7JbN13NoVpU73J3ZNkhJBIKNzblD39gkNsQADybVTCN+SinhrIvLPP6UkKZhnidVpwMTP9d1UN3Opc++ZI27ywsRTW9qQshst5WIjC8MIE5H08h3lPANvC+842scewIeGdOW7UrD5ZaDGrylDSB55Ermn+hUrGf8l970dLG9M/e0zsr94ORMcFFjUf/jBLlx7Vgzbedvv8XiAWHPA/OHGMuWSTPc373pgmBPcV45ozIn6c/L/THtNR9XrrMUf/NuI289sT3k6BN5aME6DExWSmt8sANXlqNyXkLSaMpzZT+GHKH5e9cWk8NQdP58wKKsqN+T8earyzG//3jFVDQN0Al2RB9NCksfYyY9RPUefcA7wdiocmjjVfTf3Bn0Umv8Pm6ajGxZlUuk6FKsMbE2jUFzBGGzNmjPXmAPOLFBem9aOPPir6/CVbXHVUYk7w8NCzZ+nK8qmnnrKSMCZdGF8/5Z1p8NdXftuts+47mRk3nFG6zevpULVZbytvu3iWk3BJH+vYwMwu7USa+5UnMTyhy/bmyY/Hm1U9qQEudD7/3UgiFhYpJUaSGzSeuR6FwsYzzzbUmPaCQbT1tonZdv3tsaGzmu4ZBfnDbxdYuNTiMaQBxzSKFSM+esPs0XlDy+C28ZiqDRbvZDGa7unzEhp83cU6mre//dxM+O4Lg7so7o//5pOyC4NZICf/P2xMR2UOwsY0i7swxqOqVrCrF8Lsls3PM+Jt8r4j7H4JWU82XPN21dzrcr8lHQkzPwNpcUqEvvmS3u7bU56UOC9U65jOSzvSqEe0L1UaJTdonjhYhvhAw/hK0AgkXA888IDp169frJYRBIM/lzCKIxwxk2aQIjpBLGZMC16xuvlkcT67F+Vsxi9TPWltaf2IDgTDO+OKk71t4YklVWvyttTaeumnj/b0ojxc06Jffm0y07zt3++nxZsAp/80Sw0jrXqRL63uP/I2s2PHLmbNeZcw4z//yEz44hOz+NwLGH/5P3iu8Kb++HOz62nWD/d7GGWGMQJRJLwwAEHjmXqTf57GdJufPF0Bb8/3N984tmG3e5+AHzUz48r+RYahzeee3qEX9cxNP/t072P7mbfD4c8jwY6aypieUX5M+8dPgsUHZnXjmCfNJx2+8Jz8L2UXbFe//Jg5du3tbACe7VfcyHz01efmslcft88+/s4r5lJPV/3J2RcwE6Z8EZhfWhcZcxgZB0lzWdhFUWkIG9PM3VmM6aZfppm2Xrumy5j79Rsz+5RvzfQmT3Dy+7Um09a0/eJTM937jrgUOubdRAmfB43N36Z5Ou8ekzqzDvOuNGda259tfwXVhzRhc+4Gy3YwT777qvn622w9M0g7yo3pRBdgUmCDHZu8F9PdcW6w6te3ukRP23nnnS1Tu9tuu5UUjlXiyJEjrbsytrm23nprc+mll5quXbua448/3iy++OJWtaHkoZAfMLxIkpUUgUZAYL311jOjR3uujioQhp14N1FSBPKOAAGKiCBViXr37m3uuuuuSsn0viKQOQJEPxs8eHDm9UiyAsstt1ys7FTCGwMudHXZLsAPr58Io4eEF6YXXd5a6NZbbzX4thsyZEhgNjh2RiqBr9+wkMWBD9bhYkfPuA8i6kqQwV0dqhBYBBIbeTk+//xz6+c4MGEGF5GaokMOffrppzZKYAbVCCwSlZqll55lLf3xxx+npnqg4yYQ/rIXddyUhSf0JrsRBA+CiKAZtusQmkGEG3meb/h28A2B8jbf8H0lYAGU5nxjC4j5rx7jJmaVisnxIrXkkkva30RvRegWRPKdCbrXGq4pwxujlx9++GHTrVs360HB/xhux/DEgNeFWhleAlUgFfZLkaXME044wTJsxx57rK2PXM/DkbDLbBrgdBp/xHkhgoIcd5ynd+kRXjVWXXXVvFTNbvWeeuqptj6Epg7z6JFFhVkciDNxpLMyqbp1IbhKFGK8+qMKynNHHnmknaTBYd1115XLmR/xKkG9oXPOOafomjDzinkVILABhrFQ3sYNnmbOPPNMW7ewcWNvZvBv0qRJRvyE3nbbbc1C0FMlvPBEIcbrgQce2CxpnscN294DBgywdUbi16nTLB3rZo3I4II7bq677jqz1FJLZVCL4CJhJIkgBiGUijpGgnNL9urbb79trrjiCpspQbGCAlYR/CpIfSfZmuQ7N2V4Y/TP0KG/G6iEPHPzzTcX77geH2RyLd6McLLiiisa/oIIqRsuomDa/FHagtJncW2NNdbIVd1caTOMOAFB8kJM8kLrr7++6dIFp/L5oPfee69YkY033tissMIKxd9xTzDQ5C+IxBgub+OG90yIfql1MSt5JXFkF0UItZI8LTAJuy600UYb2TDr8jvr47hx44pVYB4IWsQVE1Q4Ybzy5yc3lHzexg07g0Ko3PGXF5o48Q/7DsbNSiutlJeqGf+4kV2CPFQQN6ZCm222mdVNl996/AMBz1RTSRFQBBQBRUARUAQUAUVAEWi5CCjD23L7VlumCCgCioAioAgoAoqAIuAhoAyvDgNFQBFQBBQBRUARUAQUgRaNgLola8DuxT0ahmHo7UTxf1rPJhJuGZpvvvki+bHMom74kw0K6FHPurhlYVE7ZcqsSEj4BcVSOS+EBTvGURDGaWF+eGutbyOMm7yNaR031Y06/JFiuAURNCgtQ568jmm+HXxDoNY639jGx/xXr3ETs1o2OXEBxGNTlIBX1ZTREp5Rhrcl9KK2QRFQBBQBRUARUAQUAUUgFAFVaQiFRm8oAoqAIqAIKAKKgCKgCLQEBJThbQm9qG1QBBQBRUARUAQUAUVAEQhFQBneUGj0hiKgCCgCioAioAgoAopAS0AgP9YxLQHNOrThrbfesuGLcR5OkIJtttmmDqU2L4JIQv/973/NM888Y0NzEpSA+PNCGEUQxchPRI8j4kuaRL1Gjx5dUgTGGfvss0/xWhY4PvHEE4Zyg4jQ1IQ+zgK3CRMmGIKqDBo0qKRqGEI8//zzhiAqGPptu+22zRzBJ4FjEnmUVLzKHzqm4wOnY7o5ZlHem+ZPpXNFx3R8XK+55hqDgZqfMCY+9NBD7eUo3xj/87X+znqerrX+eXheGd489ELEOhDphdC9RHsiAg3hRIkFf9BBB0XMIblk/fr1Mz/++KNluAk/S7jed955pzghvPHGG+aee+5pFsWHEL9p06OPPmqj4nTs2LFYlBsGMiscv/76a0N4SpeIF8+fRMyrN25Eeevfv7/hI+0nJn7Caffq1ct6kaDPL730UrP66qvbpEngmEQe/npX+1vHdHzkdEw3x6zSe9P8ifSu6JiOjy3fVP98+Nprr9kQ1MLwVvrGxC+1/BNZz9Pla9dAdz0XJUoNgIDngqjQp0+fghdfvFjb5557ruCFESx47m+K1+pxMnbs2MKmm25a8KTMxeK8sMr2mheK1V775z//WTj44IOL9+t5svfeexduv/32wCLzhKMX7rjgMZMFj4ks1rWeuD3wwAOFrbfeutCzZ8/CTjvtVKwDJ15s9oIXdrXw7rvvFq+fddZZhWOOOcb+TgLHJPIoVq7GEx3TNQL4++OtfUxXem+SQTlaLjqmo+FUKdXLL79sv21jxowpJi33jSkmSugk63k6oWbkIhvV4W2QxQk+HT/66KMStYENNtjA+rp98cUX69oKfNiefPLJpl27dsVykaZ6DIyV+nKR7ReJg14Pqa5UhLJYoVO294Y1W6nnCccrr7zSzDXXXOawww6T6tcNt6lTp5ohQ4ZYiXzfvn2L5csJktdlllnGrLDCCnLJbLHFFlaNxVvUmCRwTCKPYuVqPNExXSOAvz/e2sd0pfcmGZSj5aJjOhpO5VL9/PPPdvdyjz32MF26dLFJK31jyuUX914e5um4dc5zemV489w7Tt0+++wz+2vxxRcvXiUIAFv16PPWk1ZbbTWz3XbblRT5yCOPWAZpkUUWsdc9yaBl0A888ECz5ZZbmj333NO89NJLJc+k8eP999+3jDdbTrvssotVFTjhhBPMl19+aYvLC45skT344IPmxBNPLAkeUi/c5phjDnP33XdbjIL6AZz8utaLLrqoTQqjmgSOSeQRVPdqrumYrga10md0TBv7XpR7b0oRS/eXjuna8fV2VG1gEr5jQpW+MZIuiWMe5ukk2pGXPJThzUtPVKgHOjxEBCK6mksYE0nUHPd6Pc/vuOMO88ILL1jmjXK9bU2DXi8ShuOPP97qGi+99NJWKjxx4sRUq8Zk1NTUZCjv8ssvNzC7SHwHDBhgjevyguO9995rVl55ZbPGGmsU8agnbowlpMthBE4Y+rnEWINgeJPAMYk83Polea5jOj6aOqaNfS/KvTfxUU3uCR3T8bBEujts2DCz8847l0TmrPSNiVdK+dR5mKfL17Cx7qrRWoP0F8wJKgNYj8JICrG9IoyIXKvnkW3xW2+91Zx66qlm7bXXtkXDlGO9jVWrhKLt3Lmz2XHHHa0RFIZ3adEOO+xgPL1mM88889giOnToYBcK55xzjvWQkAccWaDg3eKkk04qgSFL3Eoq4v0AJxhwl0Q1hQ86oVlrHY956Au3fXKuY1qQiH7UMT0Lq0rvTXREk02pYzo+nnzDCK3ON8WlWdjT5AAAQABJREFUSt8YJOv1okrjLYl5ul5tqUc5KuGtB8oJlCGqAhIDXrIkfvaSSy4pP+t2hNnBMwOeGC688MKilwGpwJxzzllkdrkGU45Ec/LkyZIktaMwu1JA165d7SlqDXnA8bHHHrOSeteNm9Q1S9ykDhzByT/W5PcSSyyRCI556Au3zTqmXTTineuYnoVXpfcmHqq1p9YxXT2GDz30kLVbWHDBBZtlUu4b0yxxihcqjbe8zbEpQhEpa2V4I8GUfSK26GGGXn/99WJlUBuAieNevQmfrfi6veqqq8x6661XUjwqBPjb9SyWi9dxYcZvDKHSJPRSDznkECt9lHJeeeUVe0rZecARH474UKY/XcoSN7cenHfq1MmgT4zRhBBjDz1e6p0EjknkIXVL4qhjunoUdUzPwq7Se1M9wtU9qWO6Otz4XuEffJNNNmmWQaVvTLMHUrxQabzlbY5NEYpIWSvDGwmm7BOx3b399tsbz/2X1aFke1mYTfGLWq9aIs156qmnrCHalClTDAyl/KH3hBoBUsBrr73W6rShq4k+LfpInvurVKvZo0cP6w+YoBdg9OqrrxrPRZn1B4zHgTzgOGnSJLP88ss3wyFL3PyVQS0ErK6//nqrRoOHECZ68eiQBI5J5OGvd7W/dUxXi9ys53RMz8Kh0ntTG8rxntYxHQ8vN7X4Sw+apyt9Y9x80j6vNN7yNMemjUWU/JtwjhYloabJHgHUF84880zr7QApG7pCWPnXW6UBi1VXeusic+ONN1pXVrglO+OMMwwfQhjd9u3bm9NOO82qNbjp0zjH+wHukYgyxJYeq3SCK8w333y2uCxxZEFAdLzzzjvPdOvWrVnzs8Dt3//+t7nlllvM/fffX1KfN9980/YZixp0xQiOcdRRR1mjQBImgWMSeZRUusofOqarBM57TMd0KXaV3pvS1On90jFdPbZ4HSLIzvDhw+33y59TpW+MP30Sv7Ocp5Oofx7yUIY3D70Qsw4wCVCWxmpRq0wkJrwmuJHOoj5bSzqM+1D3wFewX3VA8s0zjlnhJti4RyT04IgRYhAlgWMSeQTVLY1rWfWNjunkerMeY7pSGcm1pvacdEzHxzDK+xg/1+qfqDTeGmmOrR6F8k8qw1seH72rCCgCioAioAgoAoqAItDgCKgOb4N3oFZfEVAEFAFFQBFQBBQBRaA8AsrwlsdH7yoCioAioAgoAoqAIqAINDgCyvA2eAdq9RUBRUARUAQUAUVAEVAEyiOgDG95fPSuIqAIKAKKgCKgCCgCikCDI6AMb4N3oFZfEVAEFAFFQBFQBBQBRaA8AsrwlsdH7yoCioAioAgoAoqAIqAINDgCyvA2eAdq9RUBRUARUAQUAUVAEVAEyiOgDG95fPSuIqAIKAKKgCKgCCgCikCDI6AMb4N3oFZfEVAEFAFFQBFQBBQBRaA8AsrwlsdH7yoCioAioAgoAoqAIqAINDgCyvA2eAdq9RUBRUARUAQUAUVAEVAEyiOgDG95fPSuIqAIKAKKgCKgCCgCikCDI6AMb4N3oFZfEVAEFAFFQBFQBBQBRaA8AsrwlsdH7yoCioAioAgoAoqAIqAINDgCyvA2eAdq9RUBRUARUAQUAUVAEVAEyiOgDG95fPSuIqAIKAKKgCKgCCgCikCDI6AMb4N3oFZfEVAEFAFFQBFQBBQBRaA8AsrwlsdH7yoCioAioAgoAoqAIqAINDgCyvA2eAdq9RUBRUARUAQUAUVAEVAEyiPQttztH3/80dx///3Nksw222xmzjnnNB06dDBdu3Y1TU1NzdLoBUVAEVAEFAFFQBFQBBQBRSAPCDQVPAqryMSJE83yyy8fdtteX2GFFczdd99t1l577bLp9GY8BFhs/PLLL/Ee0tSKQAYIzD777OZPf/pTxZJ/+OEHM3Xq1IrpNIEikDUCc8wxh1lwwQUrVuP7778306ZNq5hOEygCWSOAkHKBBRbIuhqZll9WwuvWjJd/kUUWMfDHM2fONF9//bXhAzZhwgSz7bbbGpjjueee231Ez2tA4NdffzU//fRTDTnoo4pAfRCAOYjC8MIY6JiuT59oKbUh8Ntvv0VieFnA/fzzz7UVpk8rAnVAAN6ttTO8kXV4+/bta5nb9957zzK3X375pdlvv/1sN33xxRdm1KhRdegyLUIRUAQUAUVAEVAEFAFFQBGIh0Bkhtef7VxzzWV69uxZvNy2bWRhcfEZPVEEFAFFQBFQBBQBRUARUATSRiAyl8oWO3qlENs9qDJcdNFF9vfiiy9uNtlkE3uu/xQBRUARUAQUAUVAEVAEFIE8IRCZ4b366qsNf37CqO3xxx83GK4oKQKKgCKgCCgCioAioAgoAnlDoGqVBmnIp59+au655x4zY8YMuaRHRSAXCHz22Wfq6SIXPaGVUAQUAUVAEVAEskUgsoR3r732MieffLL10oB6w7fffmtuueUWM3ToUNO/f3/rseHss8/OtjVaeotBYKONNjJHH3206d27d6w2vfLKK+aOO+4wY8eONd999519ds011zTHHnusWW211ezvQw891HzyySfmwQcfDPQhjbpOnz59zBlnnGGox1ZbbdWsDvPNN59ZaqmlTPfu3c3BBx8cmE+zh/SCIuBD4D//+Y85/vjjzaOPPmq94PhuN/upY7cZJHohZwjEnbunTJkSa4497LDDzMsvv1zSanaY27dvbzp16mROPPFE065du5L7+kMRAIHIDC8uyVZfffUS1Lbcckvz7LPPmkmTJllfvMrwlsCjP+qMwKuvvmqOOuoos9Zaa5nDDz/crLHGGmbcuHHm3nvvtUzFXXfdZeaff37zl7/8xQwaNMi8/vrrBmbYTzAf88wzj9lss82KfmN33XVX+1vSIj1+7rnnzPXXX29dbcFQKykCaSOgYzdthDX/rBCIM8d27NjRCjGkrrg7/O9//2uGDx9u9t9/fzvnqyG9oKNHQSAywysPuEf8uk2fPt1eQrUBY7Y2bdq4SfRcEagLAjCgSISR4l5xxRWGaIBQ586drZR29913N4MHDzZ///vfzeabb27OP/98M2LEiGYML2ManXQWc3gikUAJyy67rNlwww1L2rLLLruYAw44wOajDG8JNPojJQR07KYErGabOQJx5lgEF/75eIsttjDkcckll5g333zTCj4yb5RWIFcIRNbh/fjjj83TTz9t/5566inz8MMPmz322MNuDdOijTfeWJndXHVty6kMUtSrrrrKbvuiWtOjRw9z5JFHGhZZQqNHj7aS1oEDBxaZXbnHVtdZZ51lunXrZi8hvYVxeOKJJ2wQFUnHEVUI/EojSYtCK664oo20pDrsUdDSNJUQQD3mhRdeMBdccIHZYYcd7B8LNRlfOnYrIaj384QAO2x//etfzYcffmj69etn527UxeAholKcOZa0kAa4iYpu60oXWcLLtjB/QYQ+o7goC7qv1xSBWhAQ9QH0tHbccUcz77zzmiFDhlidcvTIofHjx1sdyCWXXDKwKJhkl2Amhg0bZhncddddt3jrscces7q5Xbp0KV4LO3nrrbcs04w0WLfPwlDS63EQeOedd8yZZ55pxyC7B2+//ba59dZbzcILL2z23Xdfm5WO3TiIatosEYDxZG5GQMGcyk7YfffdZ0466SR7XHrppctWL84cS8S7G2+80UZ9XH/99cvmqzdbJwKRGV4XHj7uMB+EEyWsMPqSQbqQ7jN6rgjUggBGkiy43Any0ksvNcSyJ1zi//73P9OhQ4fIRXTt2tUstthiVudLGF7Uc5D67rnnns3ywRCOexChtSdPnmyINgiDHZS+WQZ6QRGIiMCiiy5qF3SSHCb4pZdeKjK8OnYFGT02CgLo58qCbYMNNrA7aOzKufN5nDkWw2J0dYX4DmCIDGFcrwIIQUaPLgKzuT/85yiGo9Po/4MxYDXFlvINN9xgmICVFIE0EYA5dSdHOZc49nPPPXcsF2To+G6//fZ2a022i59//nnrbQQJmp8WWmghqx+GZ4avv/7aMrxIKzCEW2655fzJ9bciUDUCsgCTDFjISdAfrunYFWT02CgIuGOaQFVzzDFHM7WDOHMsz6Ovu8wyy1gPOR988IFVq7z//vvNTjvt1CiwaD3rjEBVEt4611GLUwQMk6FLTHgQhpIQulsPPPCAPQ/6hwQAg0rUIYRgbG+66Sbr4gYDCLwzMDEHqUVss802RUkuhmxHHHGEue6662yEQSZdJUUgKQTYOXOJ3TSEDi7p2HXR0PO8IxBlTMeZY1kEnn766cVmM49feeWV1kC5V69exet6ogi4CJSV8LoJ9VwRyDMCeGP45ZdfzHvvvRdYTXTM0bX96quviveRzK666qrWywK6ZrjY69mzZ/F+2AneG8455xzLhODzcdq0aWFJ9boikAoCOnZTgVUzTQmBpqamWDnHnWP79u1rvTZcfPHF1kNDrMI0catBQBneVtPVLbuhm266qUH3kVW/nwF9//33DcZo6JnjT9olvDE888wzZtSoUVYCjPeGKMS2HFbH6JLdfPPNUR7RNIpAogjo2E0UTs0sZwjEnWMHDBhgdXfxyCM7fzlrklYnYwSU4c24A7T4ZBBgy+zcc8+1DOjf/vY3gy4XgSXwyYv6ATq+SGP9tPXWW1v9SNQT8ONIuqiEIQYSYhhe3PYpKQL1REDHbj3R1rKyQCDOHIsq2oEHHmi/AXfeeWcW1dUyc46AMrw57yCtXnQEiLBGtD+ckl9++eXWihe3ZRia4ceXsJN+WnDBBW14YPxERvW9K3lgPIRFMEacF154oVzWoyJQFwR07NYFZi0kQwTizrH4aUfd59prr7WGxRlWXYvOIQJNnjFEqTVEDivZGqv0zTffWJdbrbHtSbQZ12FIXVFhwFm/UnoIYEBIcI9KhP60622gUnq9rwhkhQA6pEsssUTF4nFNKJ5iKibWBIpAhgjwHcTbUWsm9dLQmnu/BbcdyUAcv7wtGAptmiKgCCgCioAi0OoRUJWGVj8EFABFQBFQBBQBRUARUARaNgLK8Lbs/tXWKQKKgCKgCCgCioAi0OoRUIa31Q8BBUARUAQUAUVAEVAEFIGWjYAyvC27f7V1ioAioAgoAoqAIqAItHoEMjFae/DBB80LL7xQBJ/QmYRn3WCDDWxwgOKNCievvfaauffee80ZZ5xRIWV1t8eNG2fuvvtuc+aZZ1aXgT6lCCgCioAioAgoAoqAIpA5ApkwvES+glnFcTpESNixY8eaf/3rX4ZoKcTUjkK4hHnuueeiJK0qDfX67LPPqnq2lodwp/Xvf//b9O7d2/hjkFebL5FnCO+I94IsKIvy8bg3efJk891339m/1VZbLVZgiaRwwkUadQH7uCE2k6iDlN+mTZsksmuWR9Q24TcWH8lJURZjSupOf4Jr2u8U8w9z3LzzzmvnS+lD5ibmCTyRzDnnnFKtuh3THlOVGpJ2+VHnyYUWWsgwrpOiPIzpNOapb7/91kyZMiUQJtxlEVUN926ffPKJ9aOLEKzelPaYqtQeyoeijr1K+fnvky/zRkuiOIGiaHcmDC8FM1EcffTRnBZp0KBB5vbbb4/M8BYfTOkEiTN/9aa33nrLHHXUURYHJoIk6IcffrAvUtwBkkTZ5MFkh2/Len2cmTgJJPHqq68Wm0Bfjhw5su5ML6GOmWj4MEZlDouVTuCED8mMGTMy90ec9Efs+++/t6FE6zWm3K6A4WUhxfuUVvkENGHx/84779ii2Wk69dRT7Tk7ZJtttpl55ZVXzDrrrONWrS7nU6dOtSG8k1qQx630Tz/9ZBccWfvYTmNMk2daY6oczjBcvFNgim/tJInAP2E7pTvuuKN54IEHzPDhww3njPfOnTsnWXykvBjTv/76a2bzpPgoT/MbzXexJRFBRuJQNuK+kBoSEYsB59Lo0aPNMcccY3bbbTd7RBLsJ9L069fP9OnTx5xzzjklzu15gS+55BIbcpA8YLJJD5EXeTPQXeLFfPzxx82YMWPMcccdV7wF00II2r59+5o999zTRvXCmb7QNddcY+644w75aY//+Mc/rLSWH//973/N+eefb+677z5DRBhC0iqlg8Bf//rXEmaXUl566SXTv3//dArUXBWBhBEgWqAwu2R93nnnWQlYwsVodoqAIqAItAoEMpPwwti+/fbbFmQkYB988IFBt/eQQw4pAj9q1Cgr0dhll10sg8gK8NhjjzWXXnqpWXvttW068rnsssssEwrj+s9//tNcdNFFZuDAgfY+DC6Sxf3228/+vv/++y3Tg+7vSiutZN544w3z7LPPmq222sref++99+xKE4bp3XfftUyxveH9Q1cY6SuqBkTwQhpN7G5UMeabbz4bw9svkeWDxTYV9PXXX5unnnrKltmjRw9D7G+hL774wubH74kTJ9o6056ktiBkuyap/KTecY5IrJCMpUH0G32PZISV8pNPPhlYDCEnGQ8Q/cKCh92GNEn6H+yzkPAi3QX3tPqebfYoEiHqwRhIimgTeabVrnL1lHFMe2Qr0p+ed3rw4MHF8ea/X+43EswhQ4aUJOEaamA9e/Y0H330kb13xRVX2O3gkoQhP4iGt/feeyeywwHuUBbYUy7vVJpjum3btiaK9DbpMc1YynpM802VOQusw4jxeMMNNxiiglYivrFhNH78eBuinTkcuvjiiwNV+ehvvudp7SqAe5pjKqz9cj3tb3SU8Sx1aanHzBjezz//3DKLLrArrLCC2XDDDYuXYE423nhju73Pxa5du1ppLIwNE73QaaedZplXfiOyZ9saYht/rbXWMrvuuqs1iuMaxnFIV/lgrLHGGnZbEGmuMLyco+uJbhwMr9Drr79uYMDPPvtss+mmm9rL6667rtlpp52s4RwMchRie5ltSZhtl5g07rrrLnuJCQfGjY8p50kQLzJ/SeVXTZ2YRMOYg2ryc5+588477eQLbvIxdu/LOYsIJmmIuqy++upm5513ltupHMEdAvssGF63/DQayEQaheEF7yTHX5ZjWjBlrIUxByyub7zxxqq2p5m7EAT4Cebg008/LV5mKxjmLAqxg8acxZivlaT9tfQnu2MIDqqhJMovVy7vaRQGgb6vBYOgOqQ5TwaVJ9cE03JjWtJyZIeU8Y0gpxLBHIfRhx9+aOdkGe8YiouuuvsMOqh8s1nwpUHS/qT7M2pd0y4/Ld3gqO3LQ7poM2WEmjJor776aqsnevrppzf7AMJoICEFdCY5GE8YWgjGDgM0GJF99tnHwLzAuCD1xcjl3HPPLdYAqYlIN7hIfp06dSreZ/KHmYZ49sgjj7Rb2xh+8BwfDEgkTdtvv71dNaJUj44lUuSVV17ZqiG4xggY2pE3TLcQkkGY4wkTJsilikfqu/zyyzdLt8oqq5jnn3/eXn/iiScsA0793To0eyjGBdHhxfglCxIdXpGuJl0H1FBER4wPxtJLL10cB25Z6D0iZa8n5UWHd4EFFqhns5uVBVMchTFu9mDIBdHhzUKPk49TJR1epFH8xSX0zmFMw2jFFVe0OxjMSZyn9U6Flc915nP+qp2feCd22GEH89BDD1WlrwkDxQIqSSPIcu0Nu8d3ir+kiDENo52mHmdYXcEzjg4vQqL//e9/YdmVXEcoJfNzyQ3vx7bbbmt1eCl70qRJVhiUJKb+8sJ+M54Zl9WO6bB8o14XHV52i5XSQSAxhpfBigQUQle1W7duJTWGmWNbA6YRqSgraHeyWnjhhe22BvdgTnmej8oSSyxhpa2SGZJXSFZD5MefEPXgxYUYwKhA8BJh2MHqcJNNNjEnnniiJLeqEZTBFjgK0DCGPMf2imsMwtYdL6H/g83H1l05SdlSgDDW8ps8okgOJL0e4yOAdIAdgD322KNE+sZEwnZZ0sTHl10FjoxbmG0lRaBaBGCS/fOImxc2CEjBUPVq1LmE9xCVNmwkUGVTUgRg8vm+u9/zalGBF3F3aN18EC65O8nuPT1v2Qj8wSkm1E626vn4+xlepJZIdcu5+RLmUJhhJFJM6KggCGGVjLQwytYwgx4dXT4Oolv75ptv2qzcD8p2221npX7Ur4enW4vkxk/cg6EhP9EfZjWINGb//fe3yWGGUVlwie1HJMZK9UWgV69e5plnnjEY/rD7gLoMBmtMdkkSizjKYocCYrwiyTjppJOSLEbzakUIPP300xVby7azSIQqJs5ZAr4BsmuHhJddNXFRmbOqanUSQgD98bBdC+bmpIndYtQtgujwww9XhjcImFZwLXGGd4sttjA33XST1WsSaSiMIhbyMK5Dhw61sCJFReo7YsQIAxMK88gkznbC+uuvb9N07NjR3kdCi14N0l8YGKS0bKXwLM/AdHTv3r2ku5CC3HrrrVYSjGcE1CmQ0mLwBiFVwIMC+aISgaoDUl10dP2eFlCtePTRR61eEYwMDDIeITBWQ5rYpUsXq86Bni8SZnRx8fCABBtVif/85z9WVcOtIJ4jcL2Slj6SW1ZrPUf/mz9UHJDcJ71VxMIL/V/XaINF28knn2x1x9mqU1IEFIFSBHg/XGYdw1H8sich2SstSX/lBYFDDz3U8KfU8hBgJxV3cvBFaRuA14pe4gwvWwWsrFy1BhhSJL+LLbZYsb5IxEQKhrQW3VLUFJj0ZJtO9JhgbknDqg39XyS3qBvALMJ0Dhw40HpmgPEUEs8NMLYvv/yy7RAYHrxAcA8GHM8N4m0BtQTuswp1GV7qdNhhhxmY77/97W+WicbDA4EhUIG44IILbB3Qp9t3332tNFkM6jCYYwEAw4vKhKhw0HYMWnBxJsQ1JNEQjDd6eSwCWBgkQUi0aUtS+VVTJxYnWZQv0nwWN9QhKrFoeuyxx0I/xCxwXGbXzff4448v6mRTPn/yQacfkGqwJS2LQvfZpM9h+NPsexZ98s6Wqzv1kF2ccumi3qNN5JnFmKJsiPbIedR6J5FOxjRzhKtSlUTebh68LxjyYqDkluMf0+4z5c4xKr7llltKkiBswGe2CDpKbob8oN+hIOMmdvMQrtSi2xx1TINPnDklpDnFy4wl8st6TMv4KlasDifSp4xpFkBI/2XOjFs8Aqcw4tuPTrGfooxp+gYhx5prrul/vObfgnlafV8tlpUatvnmm1shH8LIP//5z5bXgg8Mejcr5ZX2/cQZXj7gSFsxDBK1BtQZttxyy2Jb0FGDEWRrwfV6gDTU9XrABAuTzATJPc5POeUUe86WGB9Z3IIhIYDJhuFFTxc/t+K5AcV6nF5Tn3vuucfApCDpdcuFyaVcVimuqgR1RPrMi4dkmLb93//9nzWqQzp81llnWTUHGHruY9CGr18YYtwRsfJBGs09PhrXX3+9VdBH8rzsssuWqDpgsYxkHOIjiq4vDBovfxLERMpfUvlVUycmC5nUqnm+2mfob/S5MBaMKuVl8mFhxHZr2ERRDkv0E2Xh4zJEjC9+s6uAeoXfW0e1bSz3nJRfrr7lnq90j/cwKsObZB1oF+NJPhSV6pnkfcE0qzGNhJRFNrtEIhhIsn2SFypZvAcwIMzHYM1R2u/Ol/JM2JFnXMmum473jGAaLlPt3veflyuf7wTv1Xrrred/LPJv5vrWOqb5/jCu602u0Rrfz2HDhoXOvZXq5lctdNPDA4gLNPd6uTEl6cCFCIcyt8v1JI5SfpJzZBL1qpTHwQcfbPhDEImgBwEj/AtGqfBUqIPmhRJneNHfZUDAYCDBhdlAOgCjijEanYouJUwEf5W8HsAYsFJgEMjkinTXnYyQDIiqBMCSDsmvWIXyUUAiC0XxtkAdke7hfYH0SAyQyO6+++42Dyz9YXi5z1Y5HxykuUKsePhICOEJgvLRJUUpH6kh11xaddVVrVSca+qlwUWm9nMmN1zJseih76ISC5dyxLh2DRvdtHgqkahYjF3eA9R1ZAy7adM+l0hr6qUhOaSZIyp5aUiutOY5MfbqEWmNMYsuPMS8jgAB6RnjGElUHJ+ozMl44Qki8MRH8JVXXhl0u9k11ORgvmXXrFmCOl3gw85fUgTTx7ctzUVMWF3Bk/LTiLQWVqZ7nXEmkdbw1FQLYVcTpsOLkCxonDGemavVS0N1yOMxBoES6p/05SOPPGJVP+Ft2FkP0+GurrTqnkqc4XVdf6GjywuEkZfodvBSsRIAHCYK/5au3+sBAP7973+3jKLoALVr166ktaglsPJi0oRgkF988cWSNPIDxqNSueSDazN0NEnP5M65EJMRkgj+YCaoo8uAw8i7jA3SBhh3pLwwXjC+aqQhaEY/YuxCv4I3OCb5oYleiz9SsqNw0EEHNQsQgOQWd3hKikBLQgDvNgTeQY1L1NGitg8GFd3dcoSbStTHkvATXK4cvacI1IoADHmYFJxFoau+WWtZjfY8vBC749jPsHPDDjs8X4tkeOkcpJjoaiHVhCkkHjzEFj3MJCJuGNtKXg/sQ94/9CTFHRnXEJm7hAQPBtplMt377nkUbwv+9Ogjw9gI8RvGnTJhflFHmDx5sll00UVtEurHfZeQ6KJPTPuRSPuZdjetnjdHgI8sElPRAcX1FxLYOHp/zXOt/QofabZO0ftmPPfwvHzADGQtTa29ZZqDIvAHAggviCwJ8S4SWl3muz9ShZ8x3xNBshLlUe+vUp31fv4Q4PuAemMQEYClVjrggAPsfB+UD15WWjPDiyct1ECxW2J3HFsp1BvyQIlLeGkUkjeUuseNG2cln+jLQqg5wJQSUpetKJhPDNHYygIgGEkYGvR1hWHEYI1gEzCUMJYQkj6YHxhhPCigF4bEz7/iYuChL0skGO6RNwwS5aJPy0uBmzHyRRcwyGUVhkVIZpHYobNLeejv0EZ0kTFmIx/UNNhGQe8IHSEI3V1WghizgQdqENSHlQ+Sb2WKLEwV/4GpX6LE9ip614wpJL5ZEeOZxZC7IMqqLlquIpAGAizSjzrqqGLW7Gox/0rgoOKNMie8J+4uWJmkeksRqBkBvDLxp1QfBNgRRwCFmtcRRxxh1a3Qw86DVNdFIBWGlwII54ue2UYbbWQ9MHBNvC1wjuQXwzOYSJyPQ6wGMG5DDC7GTfjvg/nF4Aj9HiRpBIoQvTK8O7C1DHOLZaCoSKCPA4NJyF8MLmBacU/GRE25bJ2JIQbSVlQueEHQM3OJSGowwhivwWBBTN7UGcYZKTYMNUZotAWmmg8EhBcJLJAxoKNtpGFgwGRTHwzchGDoMbaD0A2GoUafKCmLTTCkXknlJ/UOO4qxnxh8seAQNZCwZ8Kuu14z3DRgiT5WpS1QFkgQfYseeRAhWWJM1GLZHZQv12QhBvaMnXoT71KafQ92UZgZ6iES+iQwoE3kWa8x7dZZ3nH3fXfvV3vOfMFOkLw3YflMmjTJ3iLMOgvCNAmdXf+uGq4XmXuDokamWRfJW74PrkSY9wxpEnNtrRR1TFOmvN+1lsnzjCvyy3pMi8ApiTZFzUNwZCc4SvtRN4SpijL3RKkDbeav0rtHXtQxjDCyx5A9LgWN6ah5UG/cYJbzHhGlXVHLc9PhvQrPF7x3fEPhk/K6g50YwwvTQSeL9SJSTLaxRJSNcRf6vTCI55xzjsULBpB0bHXdfPPNRrYaWBUMHDjQpgU4dHdhfOlMmB8MJbAKRDeY+zBSMLaoNiCJZdLAYA3vDXiKgPFEcZqXg+eQrlIfmN8NNtjA5ud6iEAi7erYsj0Bc8uzGMe988471ngDppR7SHh79+5tJbYDBgyw95FK46EBhh+mjDpg8SxGejBeLsOLWzJ0XSAGPqoSSTK85AsuUSYS0tZKLCoefvjhRFyTlKsziwR2B8oR7eZlv+222+xYCUrLpElf4oUjLaI/s6RyONZSrzgW7UnXgXdFPhS1tKHaZ5NmeHhvGM8uIxdUN/kwY5gTZRHFOwBFSeuWx3MwFkHEnM0cGDfPoLySuCaMYiVd4Shl0a4ojBRjr6WNaRZxSS5Mo+BNGtQW0RNnIRUFU3ZLzzvvvEgMatQ6RE1XDh/qVen9jVpO1HSMQ1QJeCfDKA1hDmXBfxE5EV6L8zxTYgyvv5F8BHFPBpOBtR7SOJg4gMHzAaGEYTLZkka9Aat2Bslynm9bGAMmdBjhoJWCuPTCgAkVA/KDmXZXN0xWIk5HtUI8RJAWZhQGiPrBlENIeJHmIl31S3mlbaxg8BeJKgPSZphXyiQ9+aD+wIvKywCDjPQD5hqiPtSBAYluiz+aG2UTtQ0SLw2oPMSxgrYPh/yDwWcwMpnUg3AB5xJGf7xw1bx0LExELcbNk3PUYNx+99/nN5MB7Uf1Ia1VblC5co3xDNOgXhrmKO7ACDa1HHm36E95h2vJK+6zMIJpeGkQX9yV6gNzhyoXc2eUjysqCQgl4qreoEqEzm4QMUeziyaRJoPSpHVNvTQkjyz9yTvF+yQ7pcmXEp4j8zjG3VHHdL9+/Qx/SRHfbubqKF4a+JYwBoOI3Vv80cYlcdmXpYpe3DqTXnboUTMMoyR5mbAyolxPjeGlcCz1cEcGo8UgYjBj6MC2HYwnriqQqgEGA42XTIzTcCWG9wU/wThO8rbzcG+DUjp/SEphaFxymSuYDdQOIJFYsIL3v9S86NSV+oURYYJhcGFaEeOjx8ZzMKnoBcNcwTzjlsxd7fAMkmppX1j+er05Ascee6w1mGHMuITEvRKz66aXc3zk8sfCyXUnJ/f1qAi0JARQJWP3CCEDrhWjfNBpPztm7IqVI3a0mH9hUpQUgaQQQB8UBjyIcPsZJAgLSqvX0kcgyq4odlquHUD6tQouIVWGF+t1truQwiANuckLrMDEyEpAJJ4MXPRXYVxY2RAhByMydHKZoCF3y4wJFl0VglaIX1zSXHjhhSUvCAwwklyksqgtIE1GSojklRepnIcIv74a+QvhXgNmGRUG/sgXi2WYeyKwoMdLO/wWzNQva48C0oZGOzIOkObTf0StYwyBOQumOMSiioWSqxOMzjhStaQk6XHqo2kVgXoggGoXEmHUprBTYJctCrEgDIskiDSMP96banZtopSvaVovAszvYXqy7JSiCpkl4aEpLLhFFjtOWWKBB5dK5Ar/KqVN836qDC+MLhJNJAUMUFcKwKCAkUGKi0QV9QXCrV599dVF4y0maaKVIYnFMAMmEqaS53B7hlQVvWG8PsA0s70NE8pzENJXdIII9EC+6NOib4tEmbqccMIJVtqBURqTNluD1NPP8DKw8T8pecNMo6uMFBqVCu7TBurDR4V2sA1PetF/8+v8UF909dDtVaqMAH1fSdpUKRf6xGV2SU+fISnGCEdJEWhpCKBHzxarEPMWC0U+2JWI+Zu/IGKu5K9eKlJBddBrikBWCJQLXZxVnbIqV4zCy5XfKlQaAACdXPRi3dDCXEdywB+SXRw1Dxo0yBp1sRJgMoZpJBoKzCwGZOiRoSaA9wfcXqA+QPQzpL8wjTDO6IliEMeKA+M4PAVgNMdKkXS4DoMwDGGyhulGCswfv1GAx82YS6hC4G0CqS0MMjrHKKUjYYbIFyYaph7DOdyQwfzC4CKJltCxMNESahnpMhJvGGYhpNpifIVRHFJkJJJIUZIgpNqocySVXzV1YiFSj/LBfsiQIcWFD+1G1eSaa64JrDb6xow/dMyFSA9jkMRqXRZgtN3drZCy0j7SljT7nneHHZtKRD38C79Kz5S7L/1ajzHlrwdlQ7RHzv1pavmN3QGRAcMkI9J2sA8bU4y7Sy65pKQa1BcrarwZ1ELkTT5ZSXfTHtPlmH0XN3CQ99u9Xu05/Up+WY/pMHWCKO2ib1hoIQyKQ5TJH2OaPMII9UB2Wqsh+hU99qD5CtzBPwvsaYtgnlb5YYvXanB0n2nVKg24qHAJ7wowb1gQ8sdARrqJRFdcjXEftQMYY9QehMlgFYX+GbqvuCLDWIzVBDo86NIiQcVKGeYYFQi2QTAk4w9mE4YaYzDu4RMX3Vp85PKysD2O716ktaRjqxu9Tlyb8cKJUdrll19upRzoFCFp5hmY64suusgy4NSd+pE/5RJgAomyGFkhmYRJF9+8YEM9lvMWArRBCKM+8oQY+I3upUHa5R6T/ji4ebvnLDpwBceCARLmwE3jnnMfrx4u40AfMFaTNIqQ+rhl1/M8rYmU9yLoA+JvG3gmXQfy5K9exHzh7lSlMabZMWJxjvqOOybdNsqYLsfwMoblI+o+y24V+Ycx027asHPKZ2Fe665LWP5Rryc9nqRc5vUoDEJLGNPSZjmyKKplYYpwB9udSZ6tTdj4lbLcozumy73T1113Xax83TLoV3Yl4B3CKK0xFVae/3pa5ae1OG01Kg1IOwkccfzxx5dVImcgy9Y+LwC6rAw4IrC5kwqSXWF2GQQwxDCjEB4XYJ5HjBhhJakwlagiwOxCGE4g1UP1AaYVaSrlMKm7eWL4RJkwpxAfL/7EQwPXUJcQ/RzSUzYfdSFUIyAkynwAYU5d4yeYd/dFhwlG4kxIYVQ8qDvXXMKCGok0xDY7Uu8ktwGoJ1hktQVZi5cGF6co50hmXf1eFk70IRJ1xqyfWEyhVw7zkAbB6KqXBmPfIfc9qhXrentpYGfnhRdesJ4JmNPS8NIAJujFYhRbjljEb7bZZmb06NFmnXXWaZYU1SrGu3iJ8SdgrmUR7s6//jTlfvNR5i8r3Xf10lCud6q7x+IoCS8N7OYivIpL7HAiaELIxfcwTIcXH/xp6PAynpmroxp1xm1fpfSN6qUhirCjUtvrdT9YQSti6RhsIS2tJAWDoRw4cGDFXP0rEJdp5GGYRPTRUB3g6K7SiGYGU8PLgPcE/LPCmPpd6sB4sMrzf3hhikXiAXMozA/p8SThEgwuafmDMUbdwe10PiJu3VFrwEMFdUZCDOPr+vl1827N5zAQfMDBE4PHaj/GYRjiFBs3S670gn7CiEf6O+xZvd66EeA9x78rO0zs6LBAzjMhYQtjdqk3Ul5UxvJgOZ1nHLVuwQigwkcApSBiB1TU94Lu6zVFICsEamJ4611p1BSILATjiFoEUlCIlRm+fgn0AFPDH2nwmIC01iW8NJTz0OCm5Zz0MNBIdISJxTiO1TBSEphf9G+pj3hmYEvHv5UIs04EJfJB8qxuVUqRZpsK92P0DYR0H91aouglRQQqYYGGnjcffNRKKFMXH0kh3HLzQb9f/EyiWsVOU54JrzD+xb6/vkkvKP356++WiwD2M+zsBhE+aNmBqIWwpQmT8PoFY7WUo8+2LgQSZXjR0SUsMAP+9ttvt1v+bA/A5OElAV0PVBBwJ4Y6AkwhHw+s5NnGgNlB9xImhCAM/CF5g5nF+AwXY/whkYO5FV06DNjw1kCoYPR7YWrQrUMCi0seGFHqAMFsEtENZhipLHWgDNIGbZMQTALmGR1ftjuQDCP1pf4Y1PHHy0k0Ne6RJww4hNI+HiUwlqIe+MJk24b2YUzClqN6aTBWVxGpmfQR2KFKQp+jJ4jENymi/5mslRSBqAjwHot+Pc+gbsAYQqUgr5SVqkFe8dB6NRYCCCSUFIGkEUiU4UXfFstijL9QO2DrHmYUhhIdTpgaVA0wBIMBhsHBAwIGFDCG6Oiix4qKBLqm/IYhRboCowLjynUMvLbbbrsiFjBFMKCUgbU9zC7SC6wHCSnMx0qU4GFIYZqxghammLSsGkVCW8zYO0G/k3tId6gvUl7+0NnlyDWeI2ww256oS0hZrFBh8NFnQuoLQw0Tj2SYyHJINYXwBvHoo4/an+BHPjyflKET9QTLpPKTesc5shjyB4/geXQjqZ+f2D7GI0eQlBfpvuhv+5/z/6afWOQwDrNoP+MMomzqUm9iPIJvWm1nseiq9IS1j3oIFmFp4lynTeQZtV28nzd5RrFx+4BnWOS6hKU3EYZ4T2l/VMJugd2LWomxLKHYo7a/1jLd56UfsyibejCXpTmmwTeKBDyNMQ229cYVrwpEK0Xdi7ZXGtOoDoYR38q4xowIvcCbMZ1mv4bVmevgnlXZlM+YhtLqe/q1niSCvXqWWamsRBleCkO6CQMr7rgwIqMjuQYhlYWhRV9TJHdI8mCGIaShMEAwgzCxEL53kQ7jBoePFQwoDLBLeD4YOHCgNVrD7RgGbMIQwSBLqFsMNTBiwosCjJQwA+SNJwYYdbwnwBRD1AWCWUNnmXJgTGGice2DSzNUHpA6S53w9Yp+HO1D6gtzhpQY5pkP3tPedo+ER7aZe/9QvcBTgBCqEmDp/9DK/WqPSecXtR4w+rwA6H7FoVtuucXw56cePXpYn7owHJUICbzom2XVfuookv9K9U3rflpth9mNyvAmXQfe36h58n7hTSUJYoFKEJy4hFQYn89RmKlyeRNhsh5jmo9vOeYvKvbl2lLLvbTKZ16J0kcwSUnXIc6YrgU7eRbm9cgjj7RCG7lWy5E5nh3NOITdDB6e6jGmK9Ur6f6sVJ7/flrlp6UKwm41u+qijijtgV+C34HvQfAZ5Rshz6Z1TIThHTp0qJVaiorBcp5upBBSSiS1QngeYBXFABeGF4ZECAMx8nFddvEbhhC1B47kH7ZaAVRWp26eMKlIdyDXS4PrvSGKlwZhgsVLA5JbcbrMFgyWpTDy6PhCkh7GGi8NpId5w6DE76UBf8SyjYOXBvRKaXdSFqNYgIKL22ZbyTr9Q3+WycxvLEjxf//7361ebVBViKqD1w0/xZHS8QGh/ewORPmI+cuq9TfvAJMYYz9OvWstV55n7IOBvJ9yPalj1DaJyk9S5YpxadQxzaLUVU2oVA8W6hhPslgLIuYvFtBxwoVHxSqoPPcajBYfGOoQNhe66as9R9+dceN3NwkjzAIuqfkpbv0Y0/SPfEPiPl8pfdR+gjEOmtMq5R92H0NDvmEIPOpFqNXxxxzBO0XZldrEu0RQpyDCMHvkyJFBt0KvgTfSZfqV8VZJwhyaUQ03GM/M1czTWZAwjGl5Uoo6puO0HcxQXyWCI8aKLjE+UB2FSNdiGF5xpyXuu1ypG4yGdKQLhnvu/xDzsrmdwzmrRoBlMLrMLPm4Vvf8hqlxPwIwm3wgIBiPoEmqWi8NvJjUH8YMn8D8hlmnzvLS4igbgumj/DAvDdJm9yjnNoME/iWdX9wqCSbuc0gDwAhcXELXO0iv2k0T5VzazFHO5TkWQJM8f5GomRBAJE0KKj/N8iRvabMc5XoWx6TrEBfTOOVjIBvG7IIdcxs2B0E7EGljK+2I2/449UJNi90y5mNCvgcZ2ko94uSbZNqsy6ctSdZB+jPJPKPizdxMuRyD5mk3n3L3JQ83fZRzt83uuf9ZvrUw5kHEzoeo+gTdL3dNypRjubRp3su6/Dhtg4lFdRSBoX9MsFuOwCBPlIiEtx4NQgKKJBDLY7+OCxOzKxH218dluNPw0sCHj5UMK2O2DTCgQ0dY6B//+IdVqcD/Ls7kUX0I+nhI+tZ2ZGX44osvWr1mJANIbXA5F2YFnAQ+9Fnfvn2tGgv5Mcnsv//+Vrc8DyvRJNqoedSGQK9evUKNStmlEp+ltZWS36dxw8bcyR+MPWpaSooACDBHuzu3Lip+SZ97L4lz1B9F9cGfH0In5nWl+iCAYBFVUP78hNpm3vqi7gwveh0QUj3ZOuDjAbFFdffdd1tpLBMsxmhI3thqQQqHHjA6s3xoCCDAC3fbbbdZ3VpWfTBNiM6R5uJ4HfUImE1chnENphQ/rKwAOYexoUw6jdVJkDQxSS8N6LGolwbb1c3+4e3CNeJrliDhC+iRuy8pYxCJHpM1RpJKigCqUGHEeGGBW8+t57C6pHEduwTZmSL/a6+91rp7RG1LSRHwB/dRRFovAgj4hOCn4NFYeGSl7iR1CTomxvDSSBhUGgzzKQSzKoRUDSktxBYZ2xI8AxOIo2o+Ilh4Im1jYu3evbs1YoNJRhUA/7rowcLkIo2DUUVaynOsOFG8xzgMi1MkwejrEfUFnVg8OBDFBSY3j14a8HBBPSF0ldlGbGleGlh0+KXztsEJ/2MMuP6XGWcSkQomhkUReudBhAcRVqblCHdyjM2oJOo0tD2L7SreQd6RtLDnPYyiG009BIuo2JVLR5vIM612VSqb+0m2p1x5/nvMkyzY+vTp0ywwjj8tahkY6kYlcMWPL0chcEbiLYEq+M1cX2k3hKiTaQTpoOyWOqbrNU9K38pR+hsVQTmXe/U4YjzHwgobmXJ6+RiMhhFeouAVyhF577HHHs2SgHuaY6pZgb4LjGkorfksyhztq1Kkn35jOFROmStYFDFfpFVupMr5EtXE8MJswt0fcMABdkJlGx9JKdJZIV4e1BEgpLd0Jl4OxNgAvUm8KzCIxXfknnvuaX318gy6u3hKwKsCXhvE4wFb3nhWQNKLmx/AhZlATxaG9oEHHuBxq+qAlSBBK6hHXr008AGTxQD15qWEMav08pI2DiWdX5yymVDSZhCYNFkYoebiJ1ei678nv8Hn8MMPl5+BR6LmIa1nrMUh+jNLSqvvYXqiTGowSUnXIY084/QR85vfhiDO89WkxbAHQQG7VTAHfhsIN0/04pkb8VFeK+EusdK74S8Duw7mbzHg9d+v9XfS40nq05q8NEib5ZjFmKYf2fXFHijKPC119R8RZIQJMyQtRmG8QywWgyitMRVUVtC1tMr3M6ZBZSd1DZ7rhBNOiPRdSKrMKPnUxPC6BeAXF50vPxGwQQjmFEb3iiuukEuWAWZlM3HixKK/VdcoLcxrAy8lTCH+cHE/9rTn6os8cBKPRIMPYRChRwtj3LVr15JVJFIIJNP4InQJoyYxxpNJu1YvDeAkUeKkLJTtYcYhHNvjxYEPWVLbAkjXWYyUWzlLXdI44r2CF841aEyjHKyNWdzIapkyGA/0GX1PUBTGBmMMVRc/saiqJA2LKtGUvFu6lwZpZ6UjjHFS45mykNzTF1mMaSRBqFZFsWivhEvc+3h5EcMc2l4OU+YVfIBHXWiir8sz4n3GX7flPA85YsjHAq5c2TzLQog+SprS9tIQtb7sxPGXFGXhpUHqzpzJO5XFmGYcIdTCBSn6uQgVwojv8fjx4wNvoxbn8hxBifj+B/UZ47kle2kIwiKJawgx2Z3m+77FFltYAQC8xq677ppE9onmkRjDG6VWTFLo7frd+LBF7Cq6+yUWDE53K9g9R+pAtDa2qnH/BWODbi9hfIMI3V4+Vv4Bz4cDBgjdNfEhzPOstmC6XWJCoEPlL46XBsqAqfZv87kvoWwTcs1tq1uHas+Tzi9uPepRvr9v5TdHWeWi8oIfZZfoT7ZyJY17L4nzNPozSr0EczlGeSaNNGmUnxWmLj5ptMvN3z1HAsa2LxIUKEr7YTqjSODJD1/FYcwu97GlIGgQcy7zaFrvCmVFoXpiH1SfpMuX/kw636C6+69JmVIH//2kf7NwQpVPSAzRiNiKYCxMtYx5Ooz4dlY7Jt32h+Vfj+tSj3qUlUQZGJcjbIRXYkcJNVI8LLGAIfJtnqiuDC8eEtiKAwxh6r766ivztCedJdJaNYRLIKR1GKfJi8A5k7GQO4BEIsHLJVvSrFB4+ZBsiFGdPEudYYLJT/LBzy6rYQzqYH7jeGkQBW+ph5Sjx/ohQB9LIBIplcn1hhtuyN0LKvXToyIAAjCa7FC4OupJIoMxJyoQ5SjtXZpyZeu9loMAW95it+K2CndWuMML2jEmHcItV0DmPlsts+vmoefREYD3Qej4yCOPWN6O3ScESQiUCPLTqhletiuQvF511VVm7733tgwkjs3ZSsCXm7sNHRVy1AzQ82UrjlUhzCj6u25eTNCsQNDrhIGFcYW5wZk6L8/NN99st924R15CbAPyLKtQosGhs0xACIzmCFmMlAXPELxkMPH8hglGdQPiiJoE1zCko3x0dSEkiXiAIF+l+iLQt29f+4K6paIKg0pJ3l5Qt4563roRYF7zMwiuhCwJdPCWEoWy1kePUkdN0zIRKBfWuGW2OL+tQsgI34UAE54IlRh28jfZZBMbYAvVUxFu5qEVdZXwopfDlhmh6O677z4LxDrrrGOlFiI9jQvKbrvtZj03YMAGc4qkjpUj5cDE8Bt9WSLDsHLcd9997fYekmU8PdBhiy66qL3uMrvUA/dUY8eOteHxkMyiW8sfkb8wzMMYA9c9Bx10kPWvi3cFGFp0SFFbQMSP1SjSZHRbYI4R+SOhQUcX/WMhpI746IXYtmT7HX0i/pIgJNQsApLKr5o60e56lY8eruh58QLiwUMMFsPUXZAq+NVppJ2EhBW9SbkW9Ui7Idpe7TiPWlZQOsqn/9PCnncoypY59RAsguoZ9xptIr+02lWuPpQNJV0+8yKLdz+x+D711FP9l+085A/YIolQFcNLQhok/ZgF9rSHuUzHdLI9K2M6qr53nNIJ/sQ87FK5XQr0zhFKuQT/IPYz7vWkzhnTaY6pSvUUIV1a71QauvSogi7n6fW/8MILtm/wrsUuPg4IaE+U70IlXJK83+R18B97/0nmXCEvGE6Yi6S2x1D2h5kIY1h4iRlIvHjowMFQStOFCWHA8zxu0pAIwxzD2IqBGR8WxPVHHHGEVchmW5zVCzrJMK98+GGqCYIxbNgwc+CBB1prfiz60Qvmg8VqCPG/XBOY8HAB8y6ErvHw4cMNHy2leAjwwiHFZQGSFCH5pb9lrCSVb0vIh3cgSjhM3j//R68ltD+pNjC+jjvuOGsMl0SezB34lkZVSykeAnyXUFerRKjDpWVVX6nsRrl/11132SBCSP9qIRhexrMrKKolv9b2LDvRn3/+eeLNRrgIT8NONu5fYYIphyhr7GSnSTDbcaiuEl63YmE6OG6aoHP0fZGEnn766SW3MYZjuw8papAkxDXcoEPw1eonVpRIWCD8/ULkx2pTiInwjjvusAwvvuZgVNF746NCGTDFfNRRj8DYTaRfSHpRb0CvhcHhJ1ZGKOtDtBFvAzDfSen6thYvDeDHi4aEV1bKLGRoP0wZHye8egRtyYonB/LwE2Om2hUy9aBcxmgWDDPjURZz/nbV8zeMcVLjmXq3NC8NuHfEP6hIeqRv8HHOThhjOIgYt9zfdNNN7e6SpGFRn5RAQfKUI4we71CS/Sl5RzkypsEJAUKWBMb8JUUt0UsDO6C4GvWPa3Zln/a+dUHE9/3YY48tucX8kaaOLuOZuVoCYpUUXocfsrMTRXhQh+pELoJ5CUEg/QPB8xBJFuO1vFFmDG+1QKAy8Pzzzwc+DrOJgVkSBINCx6ES4TI6fFRQc4AwmINJhrlFPxe1BF5qpMnoKcPA8sFhqxwGGmOQsG1xypLJW15qmKOkGaSk84uLdb3KB0PBEWaPcsEX40ik9gMGDCipOi/r+eefn+pkRx3q1X63cVKmHN179TxPo3zyTCPfOLgkWb7MAW75GKqFMbukYzuRwCrsCGEXIB8eN4+0zpNsezV1bGnl0x75qwaPWp4RLNMoX8Y1izexcSk3ppm76814uu2vBcdan5V61JpPvZ7Hf3IQ0b/skhBjAT5IxkBQ2npdaziGt17AEBAD5hUxPTq7Quh/8lFBwkH4PByxY7AG0cH4ACREMXrBQniNIC+khEG+XyWdHtNHgMUIixMmFVFpwVAHfew40dPSr6mWoAjMQoAdK/8CjXGMlBNpEAtpdr1gJC677DK7fazYKQJ5RIDdTXYyggihj0g309qZCCpXr9WGALvRQYRDABbhRFtj11WiNAalrde1Fs/wIvm4/fbbrTEZTKxrhQzDc//991vDMpggmFK2NfiYoCdEWnzJEVRj1KhR9jfSXYITEIIWaSF585socniIgOn168mgzjBixAjL7IoRCcwyblckuly9Orw1lkO/sl3EgmPQoEHWhZ2LA35H0e3GME1JEcgbAkSS9BNbryyeUd0RY1fSoE+H7YHfd7j/ef2tCOQNAaz8CQ6E56W8GTvlDas81WfkyJGB1RFJ9ZAhQ0p2yQMT1+liQzK8bFGjD+snf5Q0mFSkI0hk99prLwaTazgAAEAASURBVLvaYIUpOld4WOAPC36YYTqGEJzo9+IujOeuueYay9Sy+sTFBqsZjNpQcyCELQwURmzcZxWDsRuMr+jjUFf0dnG7Rn7o/0LU/8orr7SuO/iNsre0CW8CbEvyUeMvCYK5h/FLKr9q6gQW9Sqftoqv3Umes3zCNvfv399KwILqjiEjgUtkbEga8f4hv6s50m6ItsskUE0+1T5D+fR/Wtgz9qN8oKiHYFFtW9znaBP5pdUutyz/OWVDaZdPGPYgY5+33nrLLsb9thDogLLjhMFsGGH8Q7SqWkj6MQvsqTfvd5pjmvndVWULwwocBIuwNHGu52FMI/BJkjDAFl1d1G/CCEER32EMnRif3bp1C0uaynX6Mc0xVanSjGkorXcqyniuVMeg+2GqCuhg45ErivFnUL5pXMvMS0O1jYFBFS8L/jwYsOJlgXuoF6Bfi69fIfQ30bUlvDEMEUYX4oWBNHhZ4GWDCWa1iYQXlQQ8MSCixy0Z15DaUl6PHj0sM4vhGh99XK1geAIzu+aaa1rPC/jvxVUaBCMMM02IW5gwYRTUS4OFJ7F/gwcPtouRWjNEf9JvIFlrni3teRZnshVZrm1M5GzDtxaivf4FVJy2Y0DLR4PFcJKk3hsqo8mWepQPtXppqIwlBmss3OIQ322ETagHKiWDAHrR/t3nJHKGqUW4KEI+yRM+C56J+AR4pUrDtmC5RvHSIKBUc4ziZYGPDX54YYAxGhNCaV4kwXhZ4ByJLWnff/99+xtGFUL/BCLQBFIsiG1EVsAwvRIJBvUHYVyxToRQjUDawgBzfQfCGJA/6eUZ0hNqGI8QEFuUxKFm5ZSU4j6DEeki2GVBYMFHpF66WSxskLxDLCawGEVtAQV7+tpPSMrwlyz9LPdRW6lVKstYZDwwFmvNS+oV54gBJouzsJV4nLyC0kZtExNeUuOZeqA+hNQiCmMSVO9ariEJQvLKRySMqT300EPtAtp9/+OUyeKbHQb6zk8wENwPI1SnmNeCiDpHWaAEPSvXYPT4S7I/Je8oRxZO9EGt7QgrK+qYpu+T/JAzpsiPPqo3IWEUQ6Mk24Ta4HfffWebw7cUIUIQweTyPcbwHDeezJf1JMYzc3W9y5U25mVMS32iHPmuodZ5xhlnNIt+d+KJJ5qzzjrLZkO6JMdUlLoFpWlIlYaghvivMXiZEFldIJ0VknPuDR061HpZwKUYf/jBdbcPYYyY+Fy9X14IrrlGaS4T506UIs1ileNS0AeSD7ekgzGhfjBffgbMzSfuOXVLMr9ay2cSRN1ApOdx86uUXvpIcEVfGk8MSOD9xKII3bE0SMYE2Mt5GuWE5SllZtn31I16SF3C6hr3Ovll0S7eTyisfEKV4zOUIx/watsdFnJd1HXC8KJMdOvEoDYsXbXXpT1ZYE+dKV/myGrbkMRz1EOwSDK/rHClDbQnyfL53slcHIWZDAqkArOEylmaJP2YZNvj1jdp7OOWHzc9/dLX83lPBFq/ygS75Kh55olaLMPLi4X0gQ5Bf1cIV2FsEcK4+r0skAb9IdGl4TeD312RotKA4drBBx8cKHnhGSEMR2BeCYCABBeCEef5Hp4qRGslJFas/lArQS+aFwU8UUMIWgwkiVPv3r2t9ISVJwEqMAg66aSTApngJMvVvFoXAlgkM4+gz0/ocj4KSRKLcyS4GMti7BNEWUlfg+qi1xQBEGBXM0x6LbrDbgAmQY2dU4JVKeULAb7d7KTiKhahIXwXO6oIDtmdyhu1WIYXoDEUw0isa9euVmeW0MGnnHKKdZLMi4f0BJ1bXjT+brrpJquyAFMah9C18+sa4YcXi2mkidddd5259957rf4iUgmY7dZMGPqh9yMEA3z11VdbdQcM/JImJLyor4jlOi9iHl/GpNut+WWDAJGlnnnmmWLhuBRj/CWpUkKgCfLkGOTFoVi4nigCOUIADyL8BRECEKSCSo2FAEJCgn4Rk4B5j91SbKOIWYAwKU/UohlejNZQK8BojT86YZtttrGqC0huWZmgTI0RGdKYbbfd1n5EJOKZdJS7xcIqE6YVKbFsz6B7iyWqMFQ8h8I2Fqd8jNBf4TfESpVnXdUJrrO9L0EzWCEhmYYxToo5ps60sZb80HHGi0U1RLmsBsH94osvDswCQz8MBml7LYQ0Hf/JQqik4IED1ZNa2i/5xT2KDiZly7ZZ3DxqSU/5aS606FNXHz2srow/keKEpYlznTbRtqT6lOAweEiJSpRLu2m/ENf8EaJwe7f33nubPn36SLKqj3iJoUzmFcY00rKk2h+nUu6YjvNcUmkZS2mOaeYq/ioROAgWldJGuZ/0mI5SpqShbCjJd1TyjnIkyFMYpdnXUib9WI9ypDz/kTENpfU+RxnP/jpV+g1e7HojWESwxOKeyHoIlP7yl79YY313fqyUX9r3G85LQzWA8AIT7QOGNIjhgFlF2uv/aPf1tiGRCgcRBiO4/uElQZLMNoyoTnAdlyqE20MK07Fjx5KY0ttvv701tEHqK5R3Lw3ffPON/WDj1zjvhK4X3jzat2+f96q2iPqxQIliPMRELnrteWs4xqi8v6gg5JlwrYc6UBihIlTrgjEs79Z0ncVxFGNIdgMxClWqHQEYJ76XQcT8QpRVpeoRYHGctJcGBIDwSQ8//LAV+iE8xL6AiLJbbLGFlfymOR8t1xq8NMTtchjZcgZJrmTWnzcSYayt/STeDlg1IRnGd++bb75pVzlvv/22VWfgA49XCCxf8cGLOyAkuUh3kfq6RPCKZ5991l7iCAPNFmhSeni1emmgHkxIYm3r1j3KOW1GP5cPCRbkqJL4iUUH+s21rkSpq8uAsSih/fSZf1Hjr0MavxkH9Dc7AkELrjTKdPNULw0uGsHnjBlcDUYd30g2eK8Zz6J3TvAatvH877aUyFyCIVsthBEuY4hFPIsHxrm8L4xxGIZhw4alPs5h9PhLan6Kiwltpw/c9zxuHuXSR31P6fskP+jMk+QXpudars613kPCmIaXhqj1kvcoKD3vWdpjjfHMXC07t0H1SPNaXsZ0nDbi3Yj5jhgC7IzDgCKpR80BrxtJvhtx6hWWtkWrNIQ1Os51XjS/c3f/80hsCU2LFJQPJlvqbKOL2gKSAlZCSJlRq0CtASabCVsmVgaGMN6k4R5bAUluB1BWLfnBgFerh4gaB5M4f+edd571kezHEVdv4kXDf6+W32AJoZoCc4DEvZ4kfQz2cp5V+fUs118WbU+6/eRXy5h26xhnfDOmeNd5t5kjICSvYcwu91kUsxjefPPN+VkTSZs5yjnvFYtS/JemrQsp/Shl19SYKh6mfJkjq3g8sUeoh2CRRKaSX1a40gbqkEX55cpEaIG6ES5A0yLpx3L1SKtsyTcr7KX8ao7sOuGilUW4uCFD0EecgbyRMrwJ9AhMGtJDVjbjx48vuhdjpciqFMMSfPrC0HXv3t3quJBeXrAEqtBQWaDPCJOAUd+7775b9JQQZsxQa+NYeaKCQnQqCKv2mzwDxby5TKm1nfp8tgjA8EqAmbCaiJ/usPvVXp/kufcTQ9CBAwda9Yy0yqq2jvqcIlAOgXLfQxaXzNdIYJXyhQCqC5tssom1ERI1oIsuuihflfy9NsrwVugWJLN+IzZ5BEkuEkucZbMdQVhhCKaX1Q7b5+j34pgZQoqLizKYvJVXXtlea63/UNkIcj+TNB7oZuIj0N2qpn/YXubIFrGSIpAEAmlKn/z1Q1JM2HQMQDFgO/7444vSZQxkCbyCEaiSItAoCOCrF5Ug18NJo9S9tddTdrnyjoMyvBV6CP9y/AURhlFIddGZQ0kb5piVDpHS0GkhfDCeIogyhoEaYn+CH6Ai4XclhPrDyy+/bIth6wZmGQMU/pIgtv/Q0Uoqv2rqVO/yUaNA4uYyu1JvmAIkcqxEJTCF3Ev6yHYcBPblpBhJlyv5gTv9n1bfswUoeqRSZtCReggWQferuVbvMSV1BE+I9qSFq5TlPzLfEBWQCFbsGLHYdg1gSY9aA55KXMt3FudIY5IgcIfq3Xape17GNP0vWEjdajkyrrIYU9Q5yzHN7hvjuRyzC86kgdZff/1iMAt7IYF/4J7mPFmpioJ/Wu9Ulqoaldper/utwktDmmDykiDFxQXHnXfeaSUuBLSAxKcsRkNIGZG6EOIWJgwjOnf7M+9eGtLEMK28DzjgABtOuFz+uE9BvUGpegTYuYhiPMR2JMyZUvUI4NObhbN8HOPkhI7yBRdckIh7tDjlNmJaJFayPVuu/uqloRw60e7hCQmVM4y+oxILt0rRBqPm1VrSseBN2ktD1tgtp14a6tsFrEgxRsOHLvq6GEYhxcURM6oOWCoKIUkMk/Ch4oDBCYR0eM8997RMRFIWozAalB1lEpf6JnlEgs1HpJwlbpLlkRcSeLZ8w5TnTz/9dCvlTXs7BkaPDyMMR1j/J912Nz8MqViYRWFK3eeinkdtE4xxUuOZuuGVAMkyE3m9CYbT76WhHnU47rjjrCcGfH/jRg0PMkhzw4gAOCy2IXaN8A2eBMmYTrI/49QLIQJ9IN5y4jwbJW3UMc18lqTnF8YU+WU5pimbd7VetPbaa9td0X//+9824mZYueAidhgIjJL+luXBSwNtT2tMq4TXmwPDBpdej4YA4T0h9HKx/me7EYKJeuCBB0y/fv3s70r/mDjlY4SxCds3fMyjbBVXylvuM4knmZ/kG/VY7/KZEAkCgFSMxYdLnTp1snqPaU0ublky0YB91A+p+3yt51Jmln1PG9Lo/zTyjIK3SFijqnNEyTNqGtShJBiO2AeEPYvKzv7771/0JBGWLu71rMcU5dMHLW1M064sxhT9L6oZWZTPN6+SPQXYMG+nRbSbMrIaU5QNZVV+WrjmKV9leGvoDfRAx4wZY10AodbACyPEh+bRRx+1HhnkWtwjzHJSEhT0gnihkpRGxGkPEiFe5CxeZqTsMLwyoTGx4hM5La8QflyQrvKH1EQmNX+aNH9jQAlzkJbUBizPP//8ik0gfPSIESMqpouaIMsxDZ6Un9WYxhgTggGoNEfgmzfpvpcxXc8dG3dcpD2m11xzTTNw4EC3yMBzdpDYkUuKGFNZMJzUX8Y03wj3W5ZU2yrlQ0TCckSf77rrruWS1HSPMQ3Tn/S7ErVStA9K6xuN3vMee+wRtTotMp0yvDV0K67G+ODhh9c/QaDTi4R35MiR1ngtTjHoJ1122WVWQT/I4CpOXpKWDyR1JbhDFkSgCVQ+Kn2c06gb7caABytgUV+AAeevHsTWO4sjDBuzYHgpm7aWC75SCw5sw0YhtqGTGs+Uh69HGK60jQ6D2gZzwJim7LRURYLKlWuUz5imbMZ3OUpDbxoVJYxxGdNZED7PYVB4p9OgqGMabJMc0+hYolKAWly9CWaP4Cl8I+qx8+VvH99QxjQCiTCmL0ms/eUznun3rCJ0YvQOVfL776931N+MVdeINepzLSqdN3EqVYmA50+24OmHhj7tSRALhxxySMEbaAXPe0PBC1tq03p6eAXPeCT0uTRu9OnTp3D00UenkXWkPD0XbgUvylSktEkn8lzBFbyJtIh/0vlXys8zZrTlezpilZKmcv+UU04p9OzZM5W8s8yUNtG2LIi+ZEzRt1kQcwnlM7azIN5lz591FkXbMpnLPGlVZuWnVbAXlKRwzjnnpJV92Xy9RYQdU16Y2LLp0rr59NNP2/I9dZ20iiib7+WXX17wpKBl06R588ADDyx4htZpFtHq81YJbw3Ll6FDh5Z9+uabby7el7DBXMirU+ZiZfVEEVAEFAFFQBFQBBSBFoSAMrwtqDPLNaVr166ZbJNJnXDHtlxMFyLybK1HvCNQfhZb39QdY0TK96u91NquqM8TWS6Lbfeo9as23cYbb5xKKOoo9aEv6VMxNI3yTJJpGMuUz9jOgniXKT8rQsfW9YCTVT2SLhc/7iuttFLS2UbKD/Ug+jQt1adKlUA9hfKT9r5QqVy5jz48vvOzIiKyeiLYrIpvFeWqH95W0c3aSEVAEVAEFAFFQBFQBFovAn+4FWi9GGjLFQFFQBFQBBQBRUARUARaMALK8LbgztWmKQKKgCKgCCgCioAioAh4Po49X4MDFYiWhQDRaO6++27z2GOPGaJsrbDCCmUbiIsfXKwRmey1116zbmGq1XfFj+SoUaNsXmPHjrV6s1HdrBAq8u233zarrLJK2foG3YzbZqLjESTkjjvuMB988IHFqFqforW0ecKECdYF3WabbRbUrEjXqimfQClENrrvvvusj2LcAWXlfzJKI+P2r45pHdN5HtPVvLM6prMb07V8m2R+q+ab02jztLQ1r0eV8Oa1Z6qs17hx44znCs3gxJuITPjzHTJkSGhu+F3EmTdeJFZffXXL/O23336x4pq7mRPm1HOrYw0f8P1K8Iw33njDTRJ4jr9izy1MMYJUYKKQi3HbTAjJI444wvpJ9tylmRdeeMH87W9/s4uDkCLKXq62zfiR7d+/v11klC2gws245ROdi36ZMmWKDVwA48+YAZc8Utz+1TGtYzrvYzruO6tjOrsxXcu3SebTar45jTZPS1tzfWz1jtlaEACe4/AC/na9ULrFVnlRgAqe9LCAj8UguuSSSwoe81Nyq2/fvoWTTjqp5FqUH550ttC9e/eCtyotJj/rrLMKxxxzTPG3/8Rz9m39Tvbo0aOw3XbbFQYPHuxPUvZ3NW0Gnz333LPgOa63eXtS8IIXPKTghYkuW1bQzWraTD5eUJLC1ltvbf3j7rTTTkFZR7oWt3xPslTYZZddCp50t5i/J+G2fqK9HYHitbycVNO/OqYLBR3T+R3Tcd9Z3kUd0/Uf07V+m9w5NO43p9HmabeteT5XCW+ulyPxKseWFyF0PeflxQc32GADG43qxRdfLF5zT1AfOPjgg91LpmPHjlVFD0ISh6sgV4WCqHGek3xDlK0gQv1h4sSJhrCz1cRJr6bNqFqgQiBuwoi+1q1bN/PMM88EVbHstWrajJoJUvdDDz3UeIuLsvlXuhm3fCJkEV7SdSlFZCNcARFpKG9UTf/qmDY2oqCO6XyO6bjvLO+kjun6j+lav03uXBr3m9No87Tb1jyfqx/ePPdOzLpJLHLXj6KEE5awhf4st91225JLvGgwfr179y65HuUH5S+22GIlSSX0J4xLUBhS/B56kt2SZ+L8qKbNhO/015Pf6NPGpWrajK4wOtaEEEWPthaKWz662bvvvntJkWzZsSBZe+21S67n4Uc1/atjelbP6ZjO75j2zz+V5kkd0/Uf07V+m9z5M+43p9HmabeteT5XCW+eeydm3dAJRWrpDzIw//zzm2+//bZibugZDRgwwMan32effSqm9yeg/AUWWKDkMmVDMLxBVGvM9rhtnjFjhoH599cTB/5RMPK3oZo200cwu0lQNeW75cLke2okplevXqZz587urVycx+1ff6V1TFd+7/2YVTOmdEz7UQz/XQ2+bm46puszpmv9NkmfJfHNyfs8LW3N+1ElvHnvoZD6vfTSS4ZtEiEiqcFEeTqPhhfMtbhnghTGU9L7jxgwnXzyyZYxxdCtEkOGQv13331XzAapIc/89NNPxWucUDbkZzDtxQT+xW1z27ZtLTYY1LnE70oYuenlPIs2S9kcaymf8cMCZ8MNN7RGbG6+WZzrmJ6Fuo7p6ueRvI3ppOdJnacbb56u9ZuTtzGdxbchqTKV4U0KyTrn88knn5hXXnmlWGr79u2LISnRxWzXrl3xHmoKSy65ZPG3/wSJg2dYZljRYj288MIL+5M0+/3666+byZMnF6/37NnT4H5MtqDlhuiFoieaBonLszht5hmpl9SJ3+UwknT+YxZtdutQbfmorZx++ulWsnv44YebpqYmN9tMznVMz4Jdx3R180gex3SS86TO08bO2406T1fzzcnjmM7k45BQocrwJgRkvbPBlRh/LiFdxQCLSdbzemBvoTv05ZdfmqWXXtpNWjxnEsVFFwZjgwYNqijZlQdxPeYn8kAnFaMspFQQdUE/jXqlQbQrbpupJ4YjbOMLUU/cssWlLNrs1rGa8p988knjec8wRx55pPE8NrjZZXquY3oW/Dqm488jeR3TSc2TOk/PejcaeZ6O+83J65jO9CNRY+Gqw1sjgHl6HN1dz72Wufnmm61qAuoEV111lVlvvfWKzNyHH35obrzxxqJO7cUXX2xVIGB83nzzTSs1RnLMeVzC8wF1uP76622eeIzAOMv1RMCKtVZDLbde1bR5t912M567NvPyyy8bz4WKefzxx82kSZOaGXO55YSdZ9Fmty5xy8f5+QUXXGA23nhj06FDh2J/0+f4+swbRelf/5jSMd3yxzRz3JgxY+xwbbQxHfedpZE6pht7TEf55jTymM7bdyOsPirhDUOmQa8fdNBB5swzz7SSOySfBFY48cQTi60hqtgNN9xgPH+5Vr+WoAvQCSecUEzDCe7FbrvttpJrlX6gq4Tk8LTTTrMRvJDybrXVVgZ1B6Gnn37aEDXLL52W+9Uc47QZdY0uXbqY/fff3+KCsQ3qFugvu94totYjqzZL/eKWT/S9n3/+2XrigFF0ad999zVgmTeq1L/umEKlRsd0yx/TLNr32msvs84669iIko00puO+szqmT7TG2I08T0f55jTymM7bNyOsPk04CQ67qdcbFwH0dqFqDLGSaDVbcOgRM7nXi+K2GeM+vEf4XQRVW98s2uzWNevy3bqkcR63f5OuQxb4xm2zjumkez3d/LIYU26Lsii/NY/ppN9Pty/1vDICyvBWxkhTKAKKgCKgCCgCioAioAg0MAKqw9vAnadVVwQUAUVAEVAEFAFFQBGojIAyvJUx0hSKgCKgCCgCioAioAgoAg2MgDK8Ddx5WnVFQBFQBBQBRUARUAQUgcoIKMNbGSNNoQgoAoqAIqAIKAKKgCLQwAjUz4S+gUHSqisCikDrQ+DVV181b7zxRsWG77zzzma++eaz6fBl/PDDD5u3337brLHGGkUf1CNGjDDPPvus9fuMS7yOHTtWzDerBMOHD7chwvMUlCQrLLRcRUARaDkIqJeGltOX2hJFQBFIEIFTTjnFBEXK8hcxYcIEG6nwu+++M+uuu6557733zNxzz239T995553myiuvNP369bPhmwmkgZ/gaqL6+ct1f996661m9OjR5rLLLnMvV3W+6aabGvx186ekCCgCikBLQUAlvC2lJ7UdioAikAoCV199tQ1wEJa5hO2GkYXZPfroo82FF15Y9EE9dOhQywC/8847oSG+w/KOev3AAw80O+64Y9TkZdPtvffeZsqUKWXT6E1FQBFQBBoNAWV4G63HtL6KgCJQVwRWXXVVs/7661csEyf+0LbbbltkdvnN9bXWWis1ZpcykqSDDz44yew0L0VAEVAEcoGAMry56AathCKgCDQyAsccc4zV0aUNZ599trn22mvNAQccYP75z3+aL7/80vz44482nPaf//xnc9RRR9mmDhs2zIb5Rk94ySWXNJtvvrk56aSTzBxzzFECBaFlCQc+cuRImw+S3P/7v/8zK620kvnoo49sfr/++qt57rnnbBmEyQ5j0FF7uOiii8zrr79u5plnHht6/LjjjrMMuRR66qmnmsmTJ9s2vPbaa2bQoEFyq9nRbQ83o7apWUZ6QRFQBBSBlBFQLw0pA6zZKwKKQMtHYKGFFiqG8f7Tn/5kw1Wjx0vY6tlmm83MPvvs9nyBBRawYJx++ulmhx12sEzydtttZ43YzjrrLLPhhhuaX375pQjYt99+a7baaitz3nnnmc6dO9v7//jHP6yuMAZybdq0sfk2NTWZOeec055zDKKxY8eaHj16GJhY8qSsp556yjLHqGMIPfPMM+axxx6zP6n7XHPNVfJHG8aNG2fuu+8+8/3338tjJmqbig/oiSKgCCgC9USgoKQIKAKKgCLQDIEBAwYUvLm40K1bt4InVQ38u/TSS4vP3XjjjTa9J4ktXuNk2WWXLXiS0OI1j+EseIxqYZtttilMmzateP3RRx+1z3uMY/HaTjvtVPCY5YLHYBavTZo0qeAxtYW//vWvxWseU1rYfffdi7+DTo499tiCx8AWPIlx8fb48eMLHmNe6N+/f/Fa9+7dCx06dCj+9p94zHChbdu2hZ49exZ+++03eztOm/z56W9FQBFQBOqBgKo01HN1oWUpAopAwyGAIdonn3wSWO9OnToFXi93EfUEj1E0qEG46gvo/uKu7JZbbjEDBw60LsxQY0B9ARdnQh4DbVUH3GflXrnjwgsvbGbOnGlVFY444gizyCKLmFVWWcW6IENCHIWQDu+22262PrfffruVXvNc1DZFKUPTKAKKgCKQBgLK8KaBquapCCgCLQYBXIvhqispevfdd21W6MaeccYZJdmiIoCHBHRy0c/lN0ZzfkLfNy6hU0xbYKYpt2vXrmb77bc3ffr0MSuuuGLF7D7++GOrhoFKw0MPPWRwsSYUtU1xmXTJX4+KgCKgCNSKgDK8tSKozysCioAiEAMB/PWi07vOOus0e6pLly722tSpU63hGD/QBU6CllhiCesD+K677jIPPvig1d/FiA0ju6uuusrg2iyMYLzROYYZJ4DGUkstVZI0apuU4S2BTX8oAopAHRFQhreOYGtRioAioAigBoFHBfzdYjjm0g8//GCjtqFisPzyy9tbSHr9hIT1ww8/NERti8MQIzneb7/97N/06dMt04uKgqfDa/PCSM1PM2bMML169bJR5+6//34jTLmbLmqb3Gf0XBFQBBSBeiLQfHarZ+laliKgCCgCrQwBXHlB6Oq6BGNLEAtRV8DDA6oG99xzj/GM24pJYVoPOeQQc+6551rvCdzAW4ObppjYOYFpRf/366+/tleRMnuGc8YzyrN6vOQbRJRFaORLLrnERo8LShO1TUHP6jVFQBFQBOqBgEp464GylqEIKAINi8DgwYPN3XffHVr/XXbZpcikhiZybvTt29eGG77++uutGzH86hKFDYYSVQbcjgnhM5f76NriHxepLJHf8M3LUYzN2rVrZ0aNGmXzIO3KK68sWRSPp512mnUl1rt3b0NwiWWWWcYMHz7cPP7444ZruB/zE23HII2Qybhbu+aaa6wxnaTDBRpS5jhtkmf1qAgoAopAXRGohysILUMRUAQUgUZDQNySeROydRcWdrz44ott06K6JSOx51/XuhXD5Zjk2759+8K//vWvZjB5agQFT2e2mM4zGiu47tB4YMiQIYX55pvPpvEkv83ykAvUdcEFFyzm5QWfKBx00EEFj9GWJAXXLdk+++xTTCv1dI+e/+Hic3HaVHxITxQBRUARqBMCTZTjTWBKioAioAgoAnVGAInthAkTbNQz1BlQTQgjAk2g44u+rOcHt1kyXI599dVX1t1YkC6uPECZqE9wJJhFubTyTJxjnDbFyVfTKgKKgCJQCwLK8NaCnj6rCCgCioAioAgoAoqAIpB7BNRoLfddpBVUBBQBRUARUAQUAUVAEagFAWV4a0FPn1UEFAFFQBFQBBQBRUARyD0CyvDmvou0goqAIqAIKAKKgCKgCCgCtSCgDG8t6OmzioAioAgoAoqAIqAIKAK5R0AZ3tx3kVZQEVAEFAFFQBFQBBQBRaAWBJThrQU9fVYRUAQUAUVAEVAEFAFFIPcIKMOb+y7SCioCioAioAgoAoqAIqAI1IKAMry1oKfPKgKKgCKgCCgCioAioAjkHgFleHPfRVpBRUARUAQUAUVAEVAEFIFaEFCGtxb09FlFQBFQBBQBRUARUAQUgdwjoAxv7rtIK6gIKAKKgCKgCCgCioAiUAsCyvDWgp4+qwgoAoqAIqAIKAKKgCKQewSU4c19F2kFFQFFQBFQBBQBRUARUARqQUAZ3lrQ02cVAUVAEVAEFAFFQBFQBHKPgDK8ue8iraAioAgoAoqAIqAIKAKKQC0ItI3z8JgxY8yrr75q3nrrLbPEEkuYVVdd1Wy55ZambdtY2cQpUtMqAoqAIqAIKAKKgCKgCCgCNSEQiVP9/PPPzQEHHGCGDRvWrLC11lrLXHfddWb99ddvdk8vVI/ATz/9ZKZOnVp9BvqkIlAnBFjwLrjgghVL+/HHH820adMqptMEikDWCMw+++xmgQUWqFgNHdMVIdIEOUFgjjnmMPPPP39OapNNNZoKHpUr+osvvjBrrLGGmTx5sk226KKLmuWXX94g7Z0+fXrx2oQJEyJNEOXK0nt/IPDNN9+Y77///o8LeqYI5BQBJtL27dtXrN1XX31lYBCUFIG8IzDXXHPZXcxK9fzyyy/Nzz//XCmZ3lcEMkdgnnnmMYsttljm9ciyAhV1ePv3719kds8++2wDA/ziiy/a43777WfrDjN87rnnZtkOLVsRUAQUAUVAEVAEFAFFQBEIRKCsSsO7775rbrrpJvsguroDBgwoZrLQQguZwYMHm1deecV07tzZLLfccsV7eqIIKAKKgCKgCCgCioAioAjkBYGyDC8GaqLxsO+++zarMzpOr732WrPrekERUAQUAUVAEVAEFAFFQBHICwL/396dgN9XzfsD3/7c66Ln0Y2eeJTqlkIhDVJJM0UlGlQqKUKlSEKhXyQKqdwmaU4qNBhLg6QBhahQl0Qyz8PlMdzv/7zWvZ9j//bvnPM989nnfNfneb7fvc8e1l7rsz97rff6rM/Q0aSBhjfo8Y9/fOzmbeZA5kDmQOZA5kDmQOZA5kDmwNRwoCPg/clPftJsyEL37msyIu9kDmQOZA5kDmQOZA5kDmQOTBUHOgLeFVdcsdmYMvhtHsw7mQMzzIHf/OY3hWgZmTIHMgcyBzIHMgcyB6abAx1teFdfffVm6+68887i+c9/fvO3Hfa9L3jBC4qVV165eNGLXlRssskmi53PPxYmB97xjncUzGHOO++8gRjwP//zP8Wll15abLfddsVDH/rQgcrq9ubf/va3xSmnnJLC7v3gBz9Itz360Y8uRCTZZZdd0u+PfexjxbHHHlucffbZxZprrtmy6J122qkwYXzf+95XvPrVry5uu+22xa4T51Mor1VWWaU47LDDikc96lGLnc8/poMDm266abHPPvsUrXwcemkBubvxxhuLbbfdtpfber42y27PLFtQN9xyyy3FQQcdVFxxxRVdhRpsx5xx99299LG+ta222mqJqi+11FLF4x73uGLjjTcu9ttvv+JBD3rQEtfkA9PNgY6A95nPfGYKVPyHP/yh+M///M/i4IMPLsQnDLrsssuKT37yk+nnV77ylcLHkilzYFgcuPrqqxOwrE60hlV+tRwxYg888MAUK/Z5z3tesdFGGxVix37uc58r3vOe96QB4FnPelbxnOc8pzj++OPT8VaA99vf/nYBLB9wwAHNR5gUHnLIIc3fEovceuutqQyA6eMf/3jOWNjkzsLbOeaYY1JSjlED3iy7C0+2JtHicffd2thrH0tJt9lmmzXZYxX7pptuKj70oQ8V+udyf928KO9MNQc6Al5JJhYtWlS8/vWvL370ox8VBnsdM83V5ZdfXpx44onNxpsVZsocGCYHIkLIMMvsVNYb3/jG4oc//GHx4Q9/uFhhhRWal1q5AHzFmr744otTghVagGuuuaZ43etet4Qm4LOf/WzKPOZ7CWIDbwJZpi222CJ9S8DzXXfdVchamGlhcoBGbBwksk6W3XFwemE/Y9x9N2732sfCMdU++YUvfGHKKguwZ8A7ezLc0YZXcwHZHXbYIbVczN3nPve5xROf+MTiTW96UxF2vTRUu+222+xxJ7doIA5IRb3rrrsWgJ3OI7L1RaGy89GCbr755oWO5uSTTy7+/ve/p9Nf+MIXilNPPTXtv/zlLy8+/elPp31Zjd797ncnMwexoU3GQg5dIJQeeZQchZbW0tTvfve74h//+Edx4YUXFkwNnv3sZxcvfelLi2uvvTaV6R/trlWKV7ziFYuB3bhADGrlRv1onbXH88oEuNAI+06YLcxHq622WrqERiHTdHLgr3/9a3HccccVW2+9deorTzrppGY4x2iRJeKXvOQlTdkj30Hu1bcK8bjnnnumyZVznb4P56V0P+200wrPs0Rri371q18lRcU222xTxDdCYRGUZTc4kbftOGCFav/99y+Y7Ogry/Lqnk79cLu+232dvoN++27ldqJe+1jXS4EefX2nsvO56eLAvID3IQ95SMF0gZp/1VVXXUybxd5FJ3v66adPV6tzbUfOATa85IKNN9D7zW9+M9mqxoO///3vF3vvvXfBXIaW1EBPzo488sh0ifTVMfveeeediyc96UkJRLDVuu6665Kt4xFHHJHuZz8J1CLAlbZUVsCnP/3pSRv7yEc+MtWFrFrSdY59uknbJz7xiXTfd77znbR96lOfmrbVf7QBO+64Y7H00kunUxtuuGEh+QpwWyZmCgAHu+P5yKDBDliZz3jGM+a7PJ+vKQfOOuustAJm8kZLf/7556fJVVSXLbvVAUuu7Ntdc+ihhzYnXECFvvSxj31s+lYe8YhHFPN9H8r+8Y9/nAAEGQx5NEgDKlK/qw8Ztzqxxx57NCeccW2W3XhDeVvlAL8Cii1mjCbxMq7ef//96TLa2079cKu+243zfQf99t3Vupd/99rHGges3Jkowj6ZZosDXb/RfffdN6n6AZTvfe97yZ5xoedlni1RGG5rDLxMXiID39/+9rfi3HPPTbNmHQntLXtwE6l//dd/TQ8HTA3QwK/OFmDltEZb+rCHPay46qqrim9961spwx/7WrTBBhsk7dYFF1zQtJnVQdMY08iin/70p6mz1UnTViDLug888ECyTacJUy7qNt60NqiXOgEvD37wg9P9V155ZXJEU/8y0dZFfRz//e9/n55vX5tz54oT00kGeGYp3qGJjsmdPxpd/SVAbLXhbW97W2pgmMjwi7D6YbLzmMc8pvCNhL16N9+HwoBZjmgmZIjJzX333Vd89KMfbcryeuutlyZ6Z5xxRsqWmWU3sSr/68AByghjPnryk5+cJkx8E5h6mSjN1w9X++5uvgPP6qfvjpW0XvvYiy66KIHbeK4Vu5///Odp4rn77rs7nGnGODCvhrfaXnYya621VpHBbpUz+XeZAyIbBNh1nFZLZ/azn/0sXUYD5ZhO1FKuP16yPGPvuOOOclHNfUtetF/+4p677767sAQFYJRpnXXWaf685557kkmDJecyAc1Cj1nuBagRjUC3BJy4PyIwWAb7/Oc/39LTHqgHSgwY2mjJkKaNLTwteKbp5cDaa6+92ITFYE/7isgecxXp10NmbckBuSM/rajb70N0jwC7ygFEAJTyxM1Eco011ljsG8my24rr+VhwYN11143d1L/qHykOUC/9cBTSy3fQa98dz+i1j7VC59uxumJVDuBlenfJJZcsNnZF+Xk7/RzoWsM7/U3NLRgnBzg8lim0uGxp//KXvyQThBtuuKHwV6WyTW75HBABPLCzrZLl4DKVJ2QBPoDwMgEqSEcXdl60YzrAVqTDp4kLosUVVoyDw/rrr5/CSmlbFVi7HgAJcw2/zznnnGSzTEPNrjjT9HKgKlc0TiZ3KGSZBrgVkU0Db5l6+T6WW2658q1p1aAs+3GSrFstCcqyG5zI21YcKMu0CTqZDpvWXvrhKLub7yCuLctvN303UyHUax9rhS40ub45JkDs4jkbl52Wo155O/0cyIB3+t/h1LVATF1/gB4bsSq1i3/I1hXg5PjQ7poo6//9v38uXgSgsKwWNriuC0ex5ZdfPkVVcIz9b5hL+B3EEYPpAls24WyCaMqAV2YJojOwOy4PFnFddWvJkKOSOL3sk2ngMs0eB0RFQGy1W73jVnLcy/dRlnPPIethz+53kJWL6iCeZTe4k7dVDrSSy7iml3447unmOxCHGpVlupu+O55R3fbSxzKvE4HqxS9+cfI10aePK/Z7td759+g48E9UMLpn5JIzBxbjgM7UrJxzAPLbn2V+drZhnuAYCm0ZW0laVklQ4h4OFG95y1uSo1C6uMU/96Evf/nLi50VlYF5hAQQtmx5dXTMLMrkGRzw2OmK8FAm93C2uPnmm1NkiG6c1eJ+kR/YUx599NHJ5CKO5+3scCBkj91jyKytuMsmScxgkGMh5/a7+T5accnzyC8b8SDlknVOx2XKslvmRt7vlgPd9MNkGIVMd/sdVOsQ93Xqu6v3lH/30sdaLRGHnS0wf5NMs8eBDHhn751ORYs4cLHnPeqoo4p777234B3Lg10WHDaI6OEPf3ja0pyyd6RZZY8ojBONK1MEjj+Ac6coB+wnLVOdcMIJSav65z//OTleCFNmRh9Ee8ucQagzznRsiTkEybIm6oTOsKq99ZtWl4MejQBnuG6JGYaQazpYzkaZZo8DVg9EBhENRPQGtoLXX399kkWgNrRIZN13IHkP+ezm+2jFrZBnDnKWkdkIv/e9700TxQgvGfdl2Q1O5G0vHOimH6723d1+B9V6dNt3V++L3732sdpm/AF4y6H8ory8nW4OZMA73e9vamsvw43Z9xe/+MUEOgE/DpHsXGk9EedItrXS+EoG4bxYvYh5Ac93misJI6pREdJFpX/K5UwkZBMtLRAgXBqNchDHDMc5k4n7C3R4NmAODAvt1IrUQ8IKwCZslVtd1+oYT/6VVlopaZAB+EyzxwGyQ97JLvtucaRpVy25BjEvYGIj7jnHs26+j7i3vF1mmWXSJJDt4/bbb59kkumMTIFPecpTypem/Sy7S7AkH5iHA930w9W+W5HdfAetHt1N393qvjjWSx/LnMLKi4gpvplMs8WBBzWWa+dmq0mz0Zpf//rXiy1LzkarWreCmQK7sHLa6vKVbG+B0QDCzjlmObiqcS3f12rfPUI5tXNMK98DgFqa6/UZ5TIWwj6QzyxkPsJ35h8LlQyiwh7hVSz5lnmhK2aKYBWjTPN9H+Vry/tWS5QZdpDlc3m/Mwf0RWUH1XZXe5+9RHZpV860Hp+vH27Vd8/3HbTjRS99d7syFvJxWveyQ+BC5EUGvDV96wsJ8Nb0FeRqdcmBDHi7ZFS+bGo4kAHv1LyqXNEuOZABb8Mhskte5csyBzIHMgcyBzIHMgcyBzIHMgemkgMZ8E7la8uVzhzIHMgcyBzIHMgcyBzIHOiWAxnwdsupfF3mQOZA5kDmQOZA5kDmQObAVHJgIoknhOgRfidIFhdB0WWreupTnxqH591K0Sme5dvf/vZ5r+3nAvFg5aQXLitT5kDmQOZA5kDmQOZA5kDmwHRyYCKAV7xJYFUYJyTu5Ne//vXiggsuSKGqpPzrhnjI3nTTTd1c2tc16hUpEfsqoM+beGqLSyvUVrvIBX0W3fVtUgALGm4ykilzoBMHypmROl0n21LE5+x03ajP8ap/4IEHUpi7bjzxR12fXH79OCDJTDckosZSSy3VzaUjuUY/7a8V+S7LkW1aXZOPLRwOkGkRMmaJesUnEwG8GC5Uzmtf+9rFeC8JwUc+8pGiW8C72M0j+EHj7G/cJLbsVlttlUDv6quv3vfjAVbgGcjoNj6s+J177bVXce2116bnCmNy0kknLZagoe8KDXijjl2YG1nRehX0AR89ktvlphemS1zLbgfYkVRkTIWSwW7lsF2VBpWBv/71rynOskQiSBxoSUkmRSEDQNMsgBMDqnjCJjfdToQmxfthPDcShwxS1iAyYMxctGhRy8eLsSye+LgpZGBW+jV9hknyLMj0fffdN25xGOnzxLDvhSYGeFtVUmzUu+++e7FTwB8QLOuJbC3AmAQCZXLNeeedl7IYCa4ueHvMugE+WbMEc5dj3jN23333lJmLVtl973rXuxbTpDJhkLlr2WWXTQkP3ve+96XH0fjKliSNrI9gjTXWKF75ylc247SedtppKZ6shAZBMoGJuymDy6233lpcd911KZmCDF403C996Uvj0olvAWSpcb/2ta8160KLjl8y1lTT6jYvyjuZA1PCAZO3ALuqfPDBB6fvciGAsyl5RbmamQOZA5kDI+HAxAAvwBjgVkDpH/zgByn9JgAZdOONNxZvectbihe+8IWFbCny0R9yyCHF+9///pSFy3XKkdZV1qK//OUvxZlnnpmyZcWslxaZWYD0sOjyyy9PGV/Y/tKe3nnnnUnLQ6OKvve976XnAKIGRqA4iK0wUwPpOyUjAMRlCGOKAWBLESsfd5nuueee5pKTtKIAr2duuummCUTGtYD5bbfdln5KaUuDaaY8yBJE5DGnFSuX47d0vVVSrzLYjfPKkX3mDW94Qxwa2lau9EglPF+h5fbMd+00nPceEA1PtG0a6l2toyQK3WgntTHaXC2j29/BJzyrEu2/lNPtSCIGKXfLRN6ljI7vv3xuFPt8FB7/+Mc3iw5+aM8s5ACK96K/meZJhLp3s+ri/YVMNl9qjzudZAA/r7zyyrYlfvvb3257TsKSyy67rO35YZ6g1Fl77bVTkSEDtoPyZph17Les8vtplTCm33LHfd80f4/D4tXEAK+PEVgs06qrrlo885nPbB46/fTT0/IjLQxad911m6D2Ax/4QPM6g1gs/bPN+/znP5/OGQCf9rSnJe0qpzhkCzzff//9KdWmFJ5XXXVVc8CzT3NrUCprgoBQAPyd73xnU9O5zjrrFC94wQuS41y3mlpLI0B81DdVqvHPUkM5za1lexply4ODkgmFvyApE4855pj42dWWkyEt9bAJ2D3nnHMKOdO7JRObWSLveZrJ5KwbwGsAHFZWqqpM499rXvOa4sILL+yZlaeeemrhbxykf7vooouWyKaWZXoc3O/+GUwVZHecj8j0sL7fVjIgtblU1P2Qydwo+uxWdbG0/MEPfrBYb731mqeHxZdmgRPeGVbfNalmTMofaFLtbfXcoQHeH/7wh2nQMIuQ+7pqq+djpiF1nnYU8ARoEW2ApfOzzjqr2HPPPYuLL7640OHQ+rIDYnIQ9LOf/SyB1fitvFVWWSV+poEXmEbuZd5w++23J+c2IJdpAwqNJzsnWuDf/OY3aRCiReYsdumlly42KHG0M6gD3UHskIFjmt1uSX1pNavkmQHUAWtmEbTG7Ib6JbNrNqI67rLNq/fwile8Yoligfrtt99+ieMO7LTTTiPJLa590gp3Q2baJgBskrsBWN2UOclrAgCa3HSjTZpkXYfxbDI4iDyrQycZ0H+YkLaiu+66K5nrtNOi+h4OP/zwVrcO9dgyyyzTNLdS8KzJgH4V0NF36etmnYxz5b61n/Z2kumjjz46mc21K/eEE05IK5ytzm/aWEU8++yzW50a+jF92KMe9ahU7qzKNDwxzRreaa77sAR2aIDXkjyghtiqbrTRRovVkd0rBxFAhVYU8wlQkIHAsrlzIi+43+DEi7q8BBj7MXAprwx+1COWUYBsJhC0p5Zb2PdyUDnssMPisck0wjM4aZml0gq7j2lDLNG4WCcOhFeBPPBV7tjj2fGAANbxWxmtOkjlWhZCeKF9yi2XHWX0usXrajnaWiXHdthhh2T2UT6nM9PxtrqnfN2o9+Odt2rPqJ89ivLjnQzrPY+ijsMs03vzNwh1kgHfVjsZZfsf97Z6/rnnnptMdnpZaWhVTq/HQgayTPfKuXpcPwyZNmZQ5Bh3ymNZtJBCgO9JK6J0aUfGpnbfQ7t7hnE8vvFZ6deiPbPyjQ7jHU9rGUMDvMEAS/U0lVXAe8011yStbqcwXwEOCRYwTBsEHDJBCPrqV79asMULIYzjrbYAOLtUsXTDtpamB5WB6TbbbJNsa2mdzYo5t1XJOdpF5a211lrptGVV2uN99tkn/QZaq8seoh7Q3k4LsUs+4ogjkvY9zCm005IvU4hWYH1a2pbruTA5wM4/HE9p0/bYY480ocUNcs00CYWja/qR/2UOjJEDNLWcnluR8Y+fSKbMgcyBwTgwdMC7xRZbFOc0bDINMqENBZy+/OUvJ+AqygEym6X1vfrqqwsgFKiyFCKuoQgJaOWVV07naWiVR/vLy5qWlrG+e91Dc7zxxhune+KfyA0f/vCHk1aHHZRlfFpaDm9I8gsRFJTLJIKpA62uJVE2dmViWsHJy7LzG9/4xgJA3m233VIn5JioEQZOJgE0zJdcckly/gLamUpwpGGqUabjjz8+2a2KilAnYudD4x1gV93wmGOgGbt6Z8ocmCYO6IfCttB36jsP8put40IwKYk25+3scID54Fvf+taWDepGKdTyxnwwc6AHDjC/ZAoJF3VaceihyJFdOnTAyymD3VDZrAEgpfkV0zWIzS7wiHyYls0tOVrSCS1iOA0At65ho8f+l+aWuQGwaOa7aNGiFJmhHK4sIjcAtqIfeCE0OKJAOAeAi9wQ0RaAOedpe8qAV504kwHfnGKAaBEe2Piu1DABOO6441Id2PhaNqVNDoc6DnMmAAAvk4kw4dB20SIY+QextY2IEMA3PtB4h9Y7rutlG1psWq1yObTtca5Vee0c2kwQTDYsHU+C1DkmUrEUHPWwokCGpom8F2RC0el91L1N3kU3gFEbo839tin4hGdl8u384he/KB9aYt83Fn1OnJRNkVOsznoS1Emmq/Xh1NvK/r963SR/x3vR31S/0UnWq9dnj1Om5/smOGK3i6crXCRTvTpRyMC092vB03g/2jPNk4hRfY+bb755UvJRRm6yySYJa8GB3YwJweNxbYcOeGlTaFuF3wqzBgBryy23bLaJkxgguP/++y8W9YA2tBz1wAsCksXKdc6+5Xb7HNkAY5EeXve61yWQDfD6+IViicgNwg2dcsopqT5i39LC0vS2irYAFJcFWh1pnz/5yU8mzbC27bjjjsmpDvhj10oTCtDTHHNo23nnnRMgtkRl5gMgOif6g3jAW2+9dQLNK6644mKmDgBzOWoF8K2eZU1rk4E97pQ92oVtM+jHR9xLUQYx7a8j0aD74KaRvOdpJt9hN5MNA0bV5Kffdpdl2krRq171qsWcWXsp9+STTy781Z2YUlkhE4+87jTtMm1SHwqXTrzWJw6jrUJWtqPrr7++8NeKTIDOOOOMxfxNWl03iWPD4Msk6t3umcPqu9qVP+rjo4rSsN9++xX+KCIPPfTQpGD0/Tz/+c9P4Jc5aF1o6ICX/a4kEUJ6AVaEnvYFUOWMRmP65je/OWkwaTHni3rA3MBMwQAXYJR2N7TAGMkEIkwl/HYdza8EEogjCo0s6ibagjoyiRB9wfXsf2lkd9lll1SGUGYAr/Oc3HSMtLlBZjy0yEEiQXi+SBac7sRVdKxM7HxFiED4BMABEaEVLl/b7T7tEcBM0INfJhs049rYjkxOvMMqqY/wZGGqUj0/6t9AOlMYH1PVucMEIto46noMq3wA0PfBuaSOs+Fu2xnf5XzXez+DyLPyQ6Z9cyEDMjOazFqZYL7UipgxmNC2m+hZnRlHlIZq3UKmfaPzyQAv+LovGYZM6ytGpVGq8nAUv7uVaX1hyGG/9SADEeGgVRn6a/4TrYifS3nltNU14z42KzIQfDOpMe5QQnUrF3Fvnbajrvtqq62WeMT884YbbkirEkw/hR61sh6+EpPkydABbzn0F80Lm1aaieioDVhmApgDuFTBUzXqASFjowQo0uKgaufALMFHFiDOwPGlL32pJV8BjPmeqxyhzTjHuZ6g2A8y2OrM/Zn1qWMZbOkAy8LFnANwp+WVrQzwlWWtTPgAtCGTBXzSjvkGwXIZ1f2og3qWy8H7MjGxQNFxAg4AQJVozSe5fGZgMMEwmJb5Xa3ntPwuy2v5/UxL/XutJ3kcVjurMs22/IorrkirPZbVqsRsoR3YdS1nTRPxcTuY6reYWuhDBgVO1TZP4rd+C3nP3tGs0zBkOvqBTrzCS5k/uw3h2KmsUZ8LGah+o6N+7qjKj35DexaCTA/KR2Oz1fENN9wwKfGssMN8dQC8I+mRgFMdHq0m04bSelUnAAA+xklEQVQwZ2B/6eOm4gZsaR9FPQiixRX1oArIfv3rX8claRsZyeIg7Y17AuDF8VbbcrSFON/uuc67Hmgvx61ln+yj9kxLSr/85S8Xsx9Uv/jo4xk0umx5/dFIV0F7XDfOLd6rCw22P449TD5ol2nII0ST98lcoBzObZz1zM/KHOjEAZ2p2N3ICkb12wMqTZpd1+7PypSJVKbMgbpxgEmdseachjN4psyBunNAJK1FixYlpZ6Vd75SEclq0nUfuoZXg2hQpdDkEALtRzB4mkuglKG9pU1gkiOaMEE+akDS8gEb3Ri0OKyJUcghBbBEQpvJVgYIi6BAQ0JzamArE7snTmbso5xTNoDnuexp2cIBfco1i3vSk55Uvj3tS2tMMyuBBZtdz+OUpo0GV85syqEd8lI/85nPJNDoZra7BmLaUvxgBqE+Zj5A9KBB+JeobA8H8MQsLLS7bgXUHeM0JxSOP+8hz2p7YGy+dKwcIJ+RidGDTZh922zKgmhOw58gjuVt5kCdOMD8jtkC8xwro5kyB6aJA1bEOfML6XrAAQcUzD75MdVBq1vm40gArwdIach2d4MNNmhqTiLagvNAFMczIPL1r3+9Q0nLSBvMljWWESRjMKjRpLLBE0FB2Cw2IohWhrMacMszMEwk2NwAmFL+fuMb30iglX0foOy5Ii84rh5hGyd0WTXeoUxqgDDnNQ51CGhXZ8CZFht4ZI6gLUB1LFGxvQIemQLQoLqGYADZQDQHtyCgPbTd99xzT1reVJa/fikmDXipHOYmEZKJzWMZ7MYzTCzYMpbrFud62TIzWXPNNXu5Zd5ry+2Z9+IpuCBk3GQs2jYF1V6iir6hbkwVtDHavEQhXR4IPuFZ2IkJK1YmKxHMc3rV2ALGnEzHScEP7Yl+Y5zPH/aztAPpb6Z5ojxOmSYDZM+fcaAd3X333SkUZ7vzFC8ieUyaQgZs43uddJ0GeX75G+1mFXmQZ43y3m766H6eL3qVqFcwDlwFJ9VhBbtVW4YGeIEb0QoiJBctJrMFnnqI7SXABSBG2CsA0HXf//73C5mOIpuMWcGiRYvStRjHdhfwpSUFeNkx0eCwDXZe5wTYAnM0sQYOM2bRG2h2AE+G02Yc7qNdVR/gd/3110/llSNE0EiXbWwNnsCte6noAVLRH2h5ndPRvPjFL04aW2DReVppgyfAL/qCOkhCEU56HNPKoJJznDYEseljPzysKA3shmlsLTfMR7x+/Q1CzCAEUtf+YZPJzCyR9zzNZBWnG3BpAByWpzO7MMC21YBKy2BlplcyWTYp3nvvvXu9deDrs0wPzMKhFqBvH2eUBpUX0pKCpB3pT9slp3AP+3MhQcdth96uvtPer1XbNay+q1ruuH6PKkoD/CWxD6xV90nu0ABv9aUZPIQnu/DCC5O3Hs2mDgRjgDvpc4FMZg7MGwTQNgNZqRHblk2tgQwQbjVTAJR91BzTmBgoD5gGiIMMwqFOZ1oRESJcC4yaTasfUI441dHmirxQ1fJGmWYw2267bTJloG0GXj3T9coxyBq4aDcAZFpp4BqpjzoA9mLyVrO5cWyLWIsiIRh0gYhBvNrxMKI0mPnznvQeECcdmu5WJMXz7rvv3upU18eYa5gIDJPMtHU6ZQ/9YZY/7rIAQIMC2RnV7HscbepW6+EbGESetSVk+sADD0wrIue0sWv0fVtiY3JUJjLPc1g/UyWd9bjBQsj0tMtA8DJkWt9V98Ev6txq24tMk7VBKGSA0kR0n3ZKiTe84Q1LJDAqP9dYaYyaNM2KDAQfjefGdUqobuUi7q3TdlR1jxX6VpGdov3wQB0cLgf7UqM1bbbsQYUj0/HJoCZSAU9Ts1jAU6gKGlLMAHKBZJpBxGHKsniVDJr3NTKvcawSMcAfTSmNbZnMZmJGA1REnEPaX7/N4MP8Ie4z6Khrp1m2ARHABVqp8U8//fQEWMQaZjuo8wKedVzlDt89NNXRvnhmbNUnlqO0z8AOBA0ChELA1UM5eO8Pr9kbm4yYMJRJ/dgjT9K+uFyfVvvRnlbnpulYLGEP+p6npc3kcRB5LrfTag75bUcGXd9mTCJdZ+IrC6JJ6Kc+9al2t471eMjArMi0fgt5z+X+b6xMHePDhtHGsgx00iobz/TL+nBjVV0pZGBWZNqYjrRnGO+7ru+t33p1E5ufn1bZ16LfZw1630gBL69/g5wZMI0mbQwNj5lAaDzNStmN0pSaQVl250R2fcMmlxYYBXCzz2RA8gZJKyIuruMM/uND8xsApsk142W2QJvMVIHm1XURIYJZBIpIDYBgNQpEuuD//vHyBk7Nxv0plzYUuN9hhx2SHa926JTKpH4c5iZJZqmRpEN7TTa0P2yHOUywe64z2J0k//Kz68MBk1XxrOcjwDc0cBGaDAhmQ0/eM2UOTAsHmOn5M9GzSpEpc6AOHOjGybIuE4WRAl4DDY0hbQxb3fJyphkqQEuLS6PKfIGGU257mdKQwUq2MoOb6AdAJFDpPukWaVXZDYv6ADSL8gCEug/R8LDzMzAqlz2pZXaATl0sEdE8c0oze6aVUM8q4LWM/p3vfKdZNjDNVpkWmobUeW1QH45g2sE+WF3CjsmySJnUV2QKtr3jIrbEokQEqas/S79MNAIYxPm8zRyoKweYNcWKSDd1JOMm0UEmfjS+WeaDI3lbBw6YiBm/OGJbBc2UOVB3DoiaNR/BXDNv0oAJbOXYxUYs3mCMkGL+aHY5gB111FHJqctMQMxBg5l0n8AsB7LjjjsumQmI/iDsBfMB2c9of4FGwFlyCA5xZhyc40RW4DQn/q/rLNUjdnzALdBNC+zP73e/+91L2PwxhRBtgtYWQGZzDDTSMCPlAtFAPaczYciAXwCXJpptLgKiIzQS7TKNd8S5dd4xoBopS310fAHe04ke/4XG25aZRBnslosSo7RqKy2bXafltfL949qPpSXbstZ/XM8f9nOiPd5xLGsO+xnjKM+7IK/zETkMmZzv2nbn4/52MmAiyWm0TFYz2PyWScQYoHennXYqH+56n73/MDrwkIHYdl2Bml4Y7SDTddHq9MOqccp08MyWckdfQGbbkfFFPPcyWcWsy+St3J5p7teCv+X2RP8T56ZpO6rvcUGbNAhRUSbRFZgAMMT3R3gMSjS6EWrMeR8sYMzsIeyThBsyMLF9FYpM3FuzCal72dLSoPJKBY5pb5gWcCTzB2wC1AYm58TEZVsrsQKzBOYNYvfS1rqOTbCwL0KbGbzDKU3CBR0JDZHOyD3A9Xvf+94EwNVd/ZTvuRJM0ChH7GFaaSDdc4PUY6XGRKDsIAPklh3FaKBpjjuFqYny5tsyZTBhaEc60E033XSx0/ih7XUk7ZklilWAaW0T86RxR2loJQO+FStCnEO7IeY7/vohk3T2wMOiScgAMDKqiaO+a5pJv97NhJ9iY1jvLsqhmLGC2Y6Ev/RXJpM34S/rRNMuA1VeUkpNM4VP07DbsGBMGmg7JY449NBDl9AQlpmqY42PWQfLlhVYZUNXnpXS7AbYdT9ADIwiEReA56uvvjppUoFKpgixrMlxTSgtpg9AK22q55jVlMu0TOSZwCkCLP1FhAbHdDbxsbres4HdoAi1RaMMMOsYn/a0p8XpBN7LAwkQTOMsNBgTD3V3rEzaTmONRJ+Q2U29aZb7JbNR7SDoIi8w62hF6k5jXia81enXiUyWyJH2lOWmTnXspS7RHvLTjYa0l7LHeW23mgPvbBB51qayTFdlQNnMocqOmCbXJpytALLynIuVH7+7JQ64g7bFsyYpA5QLJt8RX7zbtne6jmYXr/Vd3cpFp/Imda7bupvsDfrtVmVAmmx25iIPtSJgt2p/znl70Hq0elY/x2ZFBqLtJjU07ib15XE9zk/LtluZ7rU9voFpoYFseDls0ZZWlwurjQcoFy1aVD28xO/qDKQqXEAiGyemA7ZAc5BsZoCpYPSiJxj4ANOqZhNgAuTKAFYZ5Q4aiI3Ow/Wcu8oEoBAefwClga/80g3E5bozazBAqjMNMeBbjvOrbG0HMhHArRNUTnVQTxd0+S+WX9QT0OaQZ4JSJvVk4hETgPK5uu0HT72bQfgyX7t0cLSFwMA4SFtC3sbxvEk9I76ZQZ7vu0DtZMCKStlhVL/QDuwq54orrigWNfomgGGS1K49o6qTPo4pk0FcqMVhTW5jCZtMe9+zTsOQ6eBRyADeVcfCuMaWImic/TXFTiisyvWwT36qTs4hA9rjb9opxlFtWQgyPe3vq1P9p6pHYqYgwgPgSHNjWREZ0JgcMDWQ6YPmgtOYeLbVpSFRGiJCQzDG7E1KUlrWKrkegI6P2HnOcT4C1+t8RJVQnyD2uvGRxDFgnd2VP5rnqs1sXDfKLQ2vmHkmIAgIF+x8m222GeVjp6ZsckGzrgPnGOn91iV81dQwsUYV9c0Kh2NFpd0fTdqwwF6Nmj5vVWgJxeRmSiUhT6bMgXYcoNCyutrqT/z8TJkD08KBgTS81Uay0ZUWeJNNNkmJDcwMRUEw8ABbbD2YIAgnRpsJFLLLlcGMrS7AwQ7pkEMOSQAUCDWrMihxPhNizJ/kFbKYBXCzHC9ag1TB7HtpncNpgm0TjU8AVmBTRjcdPq2sOsTMTYSGKkkmAWCzaQWMaYbNdtWfQ50/we1FQHBOmaFRsrQKOMmOox6yQ4nMoH2WrTitjTNKg/qxPab1xuvgX7XNC/U3G+pPfOITzeYz2REV5PqGDfi4U842K5F3+uaAFYHqSkrfhc3QjVWQy9/Aqhk/iUz14AAlS6xmVGtU1ahWz+ffmQOZA605MFTAy972uuuuS85fOlBL98AoQAn8cu5iasAZCgAGQkVAYEMHGJpBiqxgRmmpxG+AVPQEQBVwdZxmoqyV5JUNgHqGaA/ArmUh3oNSCgN50XkApECzkEQBimMJqbwUGuwyCFheollWX4OoP3avto65T9pg5g20RfEs0SEAfM54tL4AdWiSzYw/+MEPxmPSva5DgDKeqZ+/fim0zLbVcphlVI/1+5xR3cdBsZy9RTti0oE/wyS22mWwG2V7l+yfxVEeNkV7yMwol8pEQhmlFtN30M370F5/g1Dc77147jQTmbMyRKZHLQNlPr3xjW9MkWviGPOGfffdN62KxbF+t96Lfk+fOan341uKLJv9tqNbU4VhyHSMF7FV5/mUEa36bnJkXBs2lVcvq2Ub96yklqkOMlCuz6D7eA0HwB7R/wxa5iTuH+UY06o9odhrdW5Sx4YKeDWCdhOAjXBcnMgISTiG+JABWmG5wumD9hYYRrShwmcBg2HTSstGO8y5QieqMwWAy8TeclHDFs/z2KRyYAuHNgD5Yx/7WLpctATRH2g1xDr0cQKtymYWAaiLngAUowjlRXPNZtlzpOgFol/2spelkGZm47TOUaeLLroohVTTPlpVphe0xD4azgY0hpEeOT2k8Q/Y5XgXhE8A9LCiNES507JlikKrbkI0aeL130r7P+l6dft8cmpFYVRkEmkwmI8MHGR6GBSrKMMoaxJlfOUrX0krW2UHu0nUI56p3/Q3C0SBQUkCxPdLJiCUAvPRKKI0zPfMducBDFGLxkkilQwzWsk4697LsyTRkvCD2eS0Uie78EHaZGyxqm7VuEzwErwD91B8GicmTUMBvOeff36yd4xZ6UqNkFtBZvs0tUGWYwBMWoUAvOV89xzElFMO2eU3AMTswVb57TRKmGomUy4TSI2BthyloRy9oZsoDQGCI0oDkBpBl8XQXXPNNROQZ+OL4nrAWpQG15933nkp3XI1SgNwHh2HwZAt8jCjNBgEpolMmEStKHsqmziNUsPLwakVed/TrOG1GjGqzg6/utXkkcH45lvxuZtjZMC3rD3TJtPl9jG9Ylc8bA2vQUdSHKsSVTK533vvvVNIyOo5v/VfFA6DUB20e/p/AGUQ6lYbZrxpNxZ1+3w8YyY3aLQWK6eDgPx29bVqKjpSK2KeeNBBBy12qg4ysFiFBvxhos50kZKq275uwEeO5PZuZbqXh1M8fOQjHyne/va3LzEZkPQrwue5bmYALzMEFOG7ysunNJRV5F9laADlOE4rWhYs++zOMBZgLoNZ95hll8lAWO6EgM1YAtKxqJ9nlKnfKA2ESP1pAc2u/QbW1TkE7IILLkiPksbX81tFafD8CG1mENRpaMcgg3osv6jHIOWU+TTOfZEl/AXhiYkSbWIvHw+Q7H1U33mUayuMnQlH1UmNHB177LHNpCHlewbdJ5O+D/JTltdBy63r/eQwvol+60gGEH5No0yX262fMNElAyYCw2iP5DhkWDz0qv2yRD7in7cjKcZpnMvxwNtd2+64vlh/r58e9F23e0adjg9DpqM9g8o0GYrkRlFmeasfbDcWk712tsHVKEXlMimzqs8MGZiVfo3SziTb+1kIMl1+v/PtG4f3bkyi9TXVMcxEaNCJ53zP7/X81Kj92CEKo8PhyodbJnZEZY1w+Zz98kdejtIAgCLlsa0VtqtK5SgNAcLLURrMyg1YZjL2aZ440JVtqQS3Z1JBozPJKA3Vts36b05nsvJde+21CfBaXuE4aKbeikyoDj744GTGQiZEsZBQZOONN251eT6WOVArDpD3SBbDSZYTbxlEC70mDFsn6gRuOt2Xz9WfA0JSRobQam0BEyuLmTIHeuEAkMscNFa6y/fKh1A3GjvgZdeBdMgxo2TigGgkhcmi+RKtgTOa0FC0Ovc1UuOyA5YG+Pe//31K8Wt2ya5GmTSsEjZQnbv/5z//eYqfC2zSmDoGlHLYmFSUBnYsk4zSkJi8QP6RiU0b2eNMhoLYKG622WYp610rm1MakjPPPDOBYpOYSYSOi7rmbeZArxwQcpA2CsmeaHJXXm4WAzxT5kCvHGAiY5WrFQ1qotSqzHxsujggklUrolgK36lW5ydxbGiAFxAFUIHWcmrPWILUOCCClha95CUvScvT7gECLYsAvrzyaVLZk9GssSkDXpgCsGtlBwvk0saaXXAUc58PT+fOOcySHk0wx7Itt9yyuOaaa1IEB+mCqeDrGKUBUAfqETtlSyd4V+ZfOtnDvzBpsB2knB4e2fZSExDOg4OQdpADWvTq8km1XLbSZbAb5y3pHnPMMcVOO+0Uh9pumZ6MkrwT793KwLCXytitW50YB5XNdzo9z/uLyW2n6zqdq5NMl+vpPerLeqWQ6UFlgKe8frRMYpHT6i699NLlwyPdH6VM91NxK4P9UJ1lWh9Im98rlf0hqvdaBe3HQVi/XqVRyQBF1SSUEOU+Z9D+q8qrcf6OFephP9OKdhBewYLMO4WkrRsNBHiBTY1lKC/ygWV8A3c5pzd7nuh0aG8tFYtyEDPD5Zdfvli0aFHxq1/9qtkxsyFjDI/Y7oqUIKqCqA0R8UCWNap0H77kD6985SsTUGamANDG0h1TB16CklaoR12jNBgsy1EaaL91QmxWByWDsb9JkY6WQxizkToQwOtvlsk3YUWBw+SoySSylca8+lyrLOE8Wj3X62+Dfp1IlJmLL764TlVKfQfnuIVMojUYG3qlSURp6PbbkBxn2JozKwJrr712r2wa6/XwBrMzIUwnQRR200yjclyulsvHikLJd8dEsGxWNWn+DQR4y5UXF7eVZ7DQUkHAKaBriSQIADYroHmLwbnslNYuagMgHY5eHD+E+lIGpwvAqp1Gkx2tmY7UjO4PEqWBZro6CxbVIZzxIurCoFEa8CmyxMXz2YtGCDQxezmdqF9MDOK6Xrb4WgePdhMVjoOhwe6lDeVrtYe8cD6bT8NrFQDga0VWAoS6mzRFewyuw9bwktVWmQNH0eZuNQc6vkHkWd3bybQoGvogK0OToNNPP70vYDUMGfBtMd1qReRKiMZyn9rqumEd0+9OOg5vuS392t93K9Mme/P1ReX6tNrHMxM4Wv5uyvI+29nitio/jlECnXjiifFzsa2oOGLjD4NGJQPGedESxk2whnHHpL5buRh3Hbt53rDHmHgm3lhFB3y32GKLFETAGM2Btk5gV32HBnij8Z22wBfNZdnz3vUyjpXj23UTtSGec/fdd6dsbZZwAWZglG1vqyQC7rEEY1mi6rEPXLL1FVM3Ygi7XkdUdeSI5UcC5K+XKA2eAVQD2GXyMUV+dAk0aMMIyyACYzBF6qgcA1G13eU6jHLfgDvooKsj7TZKA/4Kaybtc5nIhhAqdVhu8Y5pDWbFm7nM51b78b20OtftMTKAAIP4Npiu0NhzTtTxToLIE/vwXilkwEQg2tNLGVbGJNhpR/oAk79x8SU89PXz3ves0zBk2kpeaA+rMmBcqvKRiUo/siZ0Zjvyvvops1V5IQOz0q/FOKrPqb6LVu1faMcOPfTQpGyElZgBMiMV+92qfN1orICXTaGle+YKZsYIIKGd7XeZgq0mIEXDEcJov2xrU56VBdARgqcapYG9WzjVxYsadpSGsHeJesRzRrllTyMZB+03Oyjx8crOLKN89qTKNkkBgCz/hfex9MC0vuPk/aTav5Cea8UEYPC+TXLqoL0fF/9NAMsZG9s9l7KhvKLV7rp8fPwcoLVsZ6Ijqk92Nhz/O8lP7I4D+l1Kx09/+tMJ2x1xxBEJ7PKhEuGobiZVYwW8BiKaV97De+yxR1oeoJlhX0pLETOp7lj9v1dZumXna5ZMSyJkmKWbclmWjJk70AQBsACwEC3MBmiGzz333KQxck5ZQbQv7pUyWTY4NkTizdGYSFnMNlhkCKp8IN5vy8jhUGDLTMIxjnSeH6lyLcHyblTuKMmEgB1NEIc4v2l7zcxmmdhs09jTgpkMedeZZosDJjMR51rLyDTTBt/tQqCVGkl4/GXKHJiPA8ZcviytaKF8L63ano/1zwHjKtxFgQkTmYCbXFMuAb+0/aHc7P8pw7tzrICXneo73vGOlOb0sssuS4xgKP/a1762b9sYjl4iN3Bg89EKhcF2xHNoNP1mLyv1r7TBnKcsG9Esi/TghS277LLpeBnsYjGHB56r4rfSzNIg+RNfjmMem1uDLS2imbhMcACtToXZgln7ddddV9Ams20Bjqn8LcvKtBaJJjyLHUyAYdEM1Mt1sYTrml4J6G8XTgZ/AP7qElqvzxjn9cEL7Yr9bp4fXuq93NNNuYNeE/WJ7aDlTep+E0jyOh9ZdSlPROe7vtX5uN/WhFTklvJqjm9Y+EJOZPqbulO5PdMuB3gd7dGW8nup+3uo1q9bmdbeUbYTH4clFzT8VXPCcruH9ZyQgdiWnzGN+9GOUb/rUfOmvNI9rGeRKRNuUWKYk/KhsIpvzMWvuuGLBzU+1v8NgjssDnRZDsDJxmdYM0uxeb1QZbYigyOtJgcPDiZsWaPpIQg+ePcL70MjDBwDtuFgBqyyTZHM4EUvelHylDV70YkArwZ9oFpkCDFfX/7yl6cldMvo7IKFCTIbov6PY1FXES7KURrYGn/uc59L9s1xTa9bWm9RMNoRuz4OeZkyBwbhgG+gmygNvj+z/2GRpXxxtVuR78wkN6K9tLomH8scaMcB4xJfjfmIoqKdOcJ898Z5fgXtyrAyWM1kFvflbeZALxywEj1oaNBWz6M8g2msZPM/AoI9RzITK9mjJGC7FxqrhrdcsbKTWvn4fPvsfWlTjzzyyMUuZXQPwNGiApZVMtOI2YYXIl5vlZg50DwjWmOkPFETgnSEF110UQK8Qm8AqgceeGCKd6l8oNigzjyCHSkQ7DhNL/MGdi2Eo0rCSLG1RUK8Scyhw+0GSFTLit/uF7fQkn6VnKOpnpQTW7U+3fw2YzQw+HC78WbupsxJXmOCxZzHu+hGQzrJunZ6drd19x0MIs/qEDJgAnvCCSe0rRbZtrJiglpnChmYFZn2XoDAhSLTJnvdyn8/coiPg34z/Tx3kHtmRQaCB9EeuCGUY3FumrajGjPZ8epnw3TB9wB3cF6rG00M8PbLCMv+N998c8vbgU02m8MgwMqLYxJRFhQOBBGgm30skAzcss/l8GZA9oGwU/7qV7+aNNgcxgBocek4jbUiGqkNN9wwnQKYlUGAQoha3TPfMXUBxo866qglLmXHO20daSy5eR+D8GUJZkzoQHSegGBZxiZUnZE/1vc0KDgIGaC9bZXOMhph8uvPqkm/oamirFFuyzJADmaBAF7f56Dvehp4MQyZjpXGdu2dxr6ODMxKvxbvR3sWgky3k8N2xynnWhEgbMJG4UZRCONMmmajhx0BF5kCAIzU9OWc0JzuOJv5oGlkxUOMZVMvWNxhwJVdcJCoEcoyQxSWbJwE2Ho2zbL6GWB9wJKAaFvdNWDj5FV+1vRwQAjCiFtdrTVzIHb8Jo3k3wpNHqiqXMq/68IB/XM72nbbbYdqBtTuOfl45kC/HNhuu+1a3spJnFmmPph5g+2kaeYBLy9u2VlohoHYcuBqwE8YI+YGNLQ6HkvMBko2ua4VS47X94033ph+0+5KpHDSSSelUGrK9huAFCECqKzaySj/6quvTmB3/fXXT+8cWBZOKRyqRiUIAC4TD0sMnANjtsq5h9MeL8r99ttvVI/P5WYOjIQDJp3tNF8ivviGke/VKgx7+kyZA3XkAJ+SdoqHhbDyU8d3kuvUPQc+//nPt7w4Vq8kSqmLHE8l4LWsyR62StUsaUAqsGdwfMlLXpJmG+xnw25VhAV/YtICw17MN77xjWTfK1yY+0477bQEmGmIgEOzGU5tXiAPceYCnNicN4vh7Ab4chhD6kq7ahBWHvtfpP4nn3xyCt3hN4ceOagRhz7luTeWcNOJHv+F5gDYl2yhFeHPpptuOjbbJKHbaLr7oeCFdsV+P+XU5Z5oQ2zrUq9e66Fj60aDarIVMtnrM+L6uN+3eN9998Xh5pbNPcfUMnFs45xZhyW1cr3se/cm2cO04eUEpbxJULwf7YrJ9STqMegzxynT3lc7wsMIc9numnbHJyUHIQOxbVe/aTke7bCddpkeBc/b9auHHHJIisjFrKEuNJWAlzmB6AlV0smWozSYOTMvCFW6TGYGl0gu4UWI2xlRGJgeiLIQ4cmo5Am54yIx+C0sGcCLZKYBeEVsYCvLxkec21133TWVITzZmWeemeL3CpOEDLzA9CabbLKYh7ksOOUoDZzwgGZx7QYh4dSETRNLuBUxsShnlmt1zTCPAde04YPYK7bzaB5mPcdZ1jAjF4yz3vEsmtZu7MGB1GG11epIOUV51KXVluwLf7hQSL9j5WmSFBP+SdZhkGd3G6WBomLQ/sh41o7wkelZP2RlUsz4SdG0y0CVb1Zvp5lGNQk+/vjj0wp49X3DWUK6UnKJStVuRW6cPJ1KwNtNlAUdkTi8ADCnsSBa1NAEi7JgH4B17b333pt+S1iA2J8gnUZosABEy6VMHQy6iPlDADimAwiwFiqNeYP4dEGAgfJdH/c4J1tcZEy6/fbbExAf1EMXWOewQ6ss7m+rjhkv2UKOa8mB7WW/mc60RxuGqQ2L9zKJbWj3vOeQr0nUY9Bndlt38t4NMO5Un5ABKxYxUT3//PPTCooILlZkWhH5tqLSSZvW6r5RH9MefZVVp275OF+drDQNyuf5ntHuvL4RgFsoMm0QH/S9xYpjK57i48UXX9zq1LzHJEqahBzMigwEg6M9xspYpo9z07QdVE5btRXOMbnWH1cjb8noevTRR6fbXJcBbysODumYTtfyg9lFOdh27DtnoGTfJ6WwPxrWskYVCCbgZbtfg5NjZac02oCg8gcR2qxqhq9WHRxgTuuLaMJ8ZARkECEJcEDLzZNSVrsq0XBPSypWABEBL4PwpcqDSf0OWQEExzXhmFRbPVeHO2inGzJgIGdexEb34x//eAoB+LGPfaxt89wnpmmszrS9cMwnfOc0R5YFyxPgMVdjqI/T9/o+B33XQ63UiAobhkx3+vbJRDunoBE1aSjFkoFZ6dfCjEF7FoJM9yIAvvO99947ZaCtyjFTUZPvOtFUani7YSAAySzAC2G/GyRUmODIgGs1yoJrBEoGFIMIOGevICYNBlmOXjH4xrnqVhxeA5kMbGussUY6rSNwv6X9cRDQ/da3vjVpCXyw6uwDpiVl6tHKNGQc9crPyBwYBgfIsO+V+ZIA6Ca4ncgKAa1ZpsyBSXNA+tVWviiTrld+fuZAtxwAcvkwCRVLaQh3yU1AcSj7bd1oZgEvRnMUs5zPdpf2lG2u/M48YoG/ZZZZJtnc0rL4O+ecc1IkBaC0F5KsImLoxn3i8BqA2fMyVaCFAj6BTWB7XCR/etWL0kSAc4/oEpkyB6aVA5Z62agj3y8nVVrcTJkD084BYfWEuKxqzaa9Xbn+s8cBSkIxz62W33DDDcmElNminAXtMmFOigszDXh1GECmpXx/XgIjaqYLNLdmJoypOZHREnH2YNdLC1wmCS2CRFAAWmmJzWYQ8Cj2J41uEINtkR9kUCs7yrHxdW/ZdMI9zBgiI5p7LXerU1nbHGV3u5WRrgp23cu2+LjjjlssIYWOtWy60e0zxnld8AL/Y3+czx/2s6INtmHeMOxnjKu8bpb6vDd/g1DwjIMEL+AyicDCTClMg+Ic3rZL+BLXTGob7ZlFmZ4UT4fxXDLTzTc5iEx3+hasVMSqYMjIMNo1jjKiXbPQr+FXtMd22t5F+X13I8/l67vZxxOr3hSLTDcPP/zw5CRPuyuGtIlbN2NDN88axjVTB3hpLP21InFl/QUxZxA9QYQEYBIgLb/0zTffvPAHrNL20vqWCaB1rmwSEefZC4rtyVGGJlmcXdex5xX1QP5zzzrxxBMLsXfLOaU5kEUkiCiP2UM5SgNwzrYPOO2XpChuR7TZ/oLYRKrjbrvtFodquw3b6NpWsMeKVb1be7x94pf7zrpxjhlmlAadqdjZVdprr72qh5KW7Nhjjy323XffJc7V5cC0y0CVj9Pu0T6OKA2dTOKsMg7S91ffxyR+z5pMV5VUk+DpIM8cRZQGuApuAnZhpW9961vJgY0pJyUaGc+Ad5C31se9Xshyyy3X9s6yZrZ6EY1whBQrn4tYsl4qzfBVV11V3HXXXenF33333cmcgemCqBA6fzF4n/CEJxS/+93vknaX1rdMK620UvIid0ws4De/+c3J1rAbIFEup7xPu9yOaMFe85rXNE/7GJ7znOc0f9dxx+w6R2mo35spTyI71c53OIg8K5sMSNPdaTLHZMmqTZBv1O9bbrklRU3xLdaFDAj6glmJPMK0BFhbKFEaBpHpTkCAY/Og38qkZHxWZCD4F+3JURqCI//cisyg/2KLbmUcjllxxRWT8myFFVaonXP54irNf7Yj7/0fB8z0q+E2qsyhsRXaS7xPgNZSFPOAmBHq/JlCmA3R3DJrALItBwRYYFe75ZZbNoumDaM589cvqReQXQ1crpOmza2bB+V87QyNCAAzCF/me864zse79z60adbJAN9pkO+m/WTgyCOPbBliL+5nU89kp/zd+tZe//rXp29w++23n1hihqhjbMsyQA5mgQBe3+eg73oaeOG77ffb7cSfae/jyMCs9Gv6DqQ9nd7ZNMjrKOpIOSdEq4lBhCGj6OMsXzeajR52wlwV6szsz8yGSj/CkEWkCEHvxfQVu1dcXIkgxjFbNOgwvaChpt1CbMM41AXY/cxnPlMsWrQoRZLQDqCgbBYyYdbmx2cOLMYBqya827fYYouOQMPyWhnwntMw37nttttSWQKlszXLlDlQVw7oh8UxLZNEK6985SvLh/J+5sDEOaAv1idbfYsIOO9973snXq9WFciAtxVXSsdoZqtObHGaJtdSpOQV7EqlFUZAr9mOGSH73sh6BICy1aVxfeITnxjFjHQLxLIzNuOicWZfHBqJT3ziEykGb8xg1ZudI6e6qkPQSCuZC88c6JIDlnr3bsR9tNzbrZaf3JcBLgdWZdQtCUWXLMiXzQgHZOFk7masiHB5lA36YeDBX5lihat8LO9nDtSBA+VcBHWoT7s6ZMDbjjP/d1x8OX+t6IILLkhaXVpSXuDAsZmO6AhsWtjJihTB+UBYMmr/pZdeOmmeqva1Oj3mEMgAbamzVafXqh7tjkWHCdCWvdTjuPoE2C2XYckYUHdPnZZwot7qHPvlek/bfrTBNpa2p60N6qvu3dTfe2slb720OXjWjQy4RmZE4QFlPAziTCN+b6QmBpxjVSauGde2l/aMq06DPCfaE9tByprkveOQaeZmCJAlk1b9/LUjPJ0GvsY3rq7d9Avt2luX49GebvqcutS5VT1m4V20alcvxzLg7cAty6Dzkc7qs5/9bArBIS6oSAtm7ZdffnkCvAZTy1AArxjA7HSF6qgKn1n9sKM0RN1pD/yV6Z577kna5vKx2Fd/xuf7779/MsOI43XZ5igNdXkT/1sPMt6Ng80wozR0IwNSirPlbUVMfSIzG9OHE044YTFHt1b3jPLYrHm060OmmcYRpaHKHzLdSYvLR2SaIjfMmkyHT071vU3L71FEaZiWtkc9M+ANTvS5FWiZM9qXv/zllNlNzF5aXIGYmTrwVAyiRaoC3TjH/vf9739/+nnHHXckg28C2mnGH/e225qRArqWgasOMWuttVbSNNNKV4lWV+YqADxscqrXTOI3jQGPUINRmGVMoh7DeqbBjXOH91wnTXqv7eu27mRwEHlWr15kQBZBMSLbmSStssoqKRa177LsMNpr+we5flZkIHgwK+3ptn8Ztkx3+paMK4N+P/GeRrmdFRkIHll95TtgLGw3fse1dd52K9N1bsOgdcuAd0AOfvKTn0wlsMtdeeWVC+YNCCi74oorUhzgdGCef6I3iKqAAFTaMFt//RJwAPDqlFuVwzGCh2WVXvrSl6YYw9Xjk/6tIwV4tadb+81J17nT83Wk4dG+EDojg3krOezEo+q5XmTAZLQd2FWuWNi+kfjuqs8ax++QATLtb9pJv0WmvedO4G3a2xn1990O+u2SAaRP68Szdv141KUu25AB7RmUN3VpE8A73/upS11zPdpzYPp72PZtG/kZGdHEAz3ooIOSzWu5s+KlyNRBRIZ+SUriZZddtt/b02AegKrVYGqwl/5PSlb76OlPf3oyZ3j3u9/d93NHdaM6xmA6Cx0p8KYjNTkqy86o+DeqcoXgM0maj6688sri9ttvn++yjud7kYHvfOc7xXbbbdexPHV64IEHOl4zypMhAwBilulRcrq3spl0SQs/H1FqcEQehMoy8JOf/KRtUWK9T4NJQ7Rn2vu1eBEmJED8tLdntdVWK0SMWsiUAe8Ab1+oMYMUDVEVsMgEpTOU2rea6nS+R6677ropaxuzgkGXUMT81Un6YNtR2WyBprqcFa7dPZM4jseWnzn3hVZkEvUY1jPJjmVKE6eYcAyr7HGWs84663QFeKX+PacLu/hOdfc9yIo4TBkQrWRSFDIgMgqgMO1ECyamOHvTcPaZxjbps7sBvJdeemnhbxAqy4B+rZ09PPOc6667bpBHjeVeyhUrltMuA8Esk1EhRqe9PTCJlL8LmhqdUqY+OdBIcTzXCK7c9u5GmtO5hsPaXMMZYa4RvWHu1ltvTdc2TAnmGs40be8b1olGLNK5xqxurqHFGlaREy2n4WiX2vOlL31povUY1sMbdt+pPY3MfMMqcubLaYT+Szz74he/OBNtbawQpfY07PZnoj0NQJba86Mf/Wgm2jOORtx5552JZ2RhFsi3adzxrc4CNcwWU3saiolZaM6CbkPW8A4w3Tn//PM73n3uuec2zzc6geZ+XYMyNyuYdzIHMgcyBzIHMgcyBzIHZogD/2+G2pKbUuGApTIpjGclHImlMu0Z1PGpwqaJ/dQO7WllXz2xStX8wSHT7OlmgUIGmALMAnkvWaZ7e5Pe/Sz1ayEDvtVZIOOn91M1W5yFti20NjyIfnuhNTq3N3MgcyBzIHMgcyBzIHMgc2DhcCBreBfOu84tzRzIHMgcyBzIHMgcyBxYkBzIgHdBvvbc6MyBzIHMgcyBzIHMgcyBhcOB7LQ25e9arFGhz2RMe8YznlE897nP7dgi4cluvvnmghOdUCtbb711sfrqq3e8Z5wne23PRRddlDLdleso1uAGG2xQPjTxfWls2bS98IUv7FgXWfu8z29+85vFf/zHfxQ77bRTsdRSS3W8Z9ZO9ioDdedZr+3JMj1rEl0UvcpAlunxy4CY6OLP77nnnimJVKca1P39dKr7Qj6XNbxT/PaBokbYs0KwcuDoxBNPLM4444yOLTrttNOKY445plhuueVS0oMDDzywaITF6XjPuE722h5xS7XXfffdd1/zT7zEOhEAe9JJJxXSTnciSTUOOOCAFL95jTXWKG655ZbiNa95Tcou1+m+WTrXqwzUnWe9tifL9CxJ8/+2pVcZyDI9fhkQ/7gRKrS4+uqriz/+8Y8dK1D399Ox8gv95IIOyjbFjW8kKpjbbbfd5hrZ2JqtuOmmm+Y222yzuQbgax4r74j3uvHGG881kks0Dx999NFzr3vd65q/J7XTT3vEeRTf+Kc//emkqt3xuY2EH3ONycXcpptuOrfNNtvMnXDCCR2v9y533333uQboSdc10ijPNZKazIkDuRCoHxmoM8/6aU+W6dmS9H5kIMv0eGWgoX2fa2SKnGtkZUzjSWOC0rECdX4/HSueT85lDe+UznhoMe+///5i8803b7Zg/fXXTyG7GokZmsfKOzQNK6ywQrHqqqs2D2+xxRZFIyFG8ec//7l5bBI7/bRHVjiZykJbPYl6d3qmlM0NAFOceuqpxSqrrNLp0nTu61//etGYsDTD3wjvs9FGGxU33HDDvPfOwgX9yECdedZPe7JMz4Ik/7MN/chAlul/8m8ce7I/Mus7+eSTu3pcnd9PVw1YwBdlG94pffmRcx3YC2IjKu0qe95W5B7xBMu07LLLpp865sc97nHlU2Pd76c9wIEYiYcffnjRyFqWwP6OO+5Y7L333rWIbSs9aUOz2zUfG5rqJd6P9/Xd73636zKm+cJ+ZKDOPOunPVmmp1mCl6x7PzKQZXpJPo7ySCNbavHwhz+8kN67G6rz++mm/gv5mqzhndK3/7Of/SxpAqt51zmiNVIgtmyVe+S5L5PrEcA7SeqnPffee2/BCa+x7F986EMfKl70ohcVF1xwQXHhhRdOsinNZ+tEuyU2ZCYq1ffDYa3d++y27Gm5rlcZqDvPem2P95Rlelqktbt69ioDWaa74+swr4p+urHiP2+xdX8/8zZggV+QNbxTKgA0mw37sMIHWM7SxKA+QGy1ae7505/+tNhh16Mq0FrsojH86Kc9nAx41roXveIVryhoyC6//PJir732GkOth/cI2da8R+0pk9/t3mf5ulnY71UG6s6zXtvjHWaZngVJ/mcbepWBLNP/5F0d9+r+furIszrVKWt46/Q2eqjLox/96HR1wzFqsbv+8Ic/FI997GMXOxY/3FO9Pn4/5jGPicsmsu2nPVI9BtiNSq+77ropTJnJwLRRu/fT7n1OW/vmq28/MlBnnvXTnizT80nJdJ3vRwayTNf7Hdf5/dSbc5OvXQa8k38HfdVg+eWXLzg13XHHHc372Rb9/Oc/L5xrRRynaEAb3v/N0+5nx6usSVI/7TnkkEOKs846a7Fqf/WrX022yNOY99z74VhYJu+n3fssXzcL+/3IQJ151k97skzPgiT/sw39yECW6X/yr457dX4/deRXneqUAW+d3kYPdWG7y3b13HPPTfa3TBNOOeWUYr311ivWXHPNVJK4r2effXbTPlcEAPexd2UKIcrDRz/60eTk1cOjR3JpP+1phCQrLrnkkgQStb8Rvis5r+26664jqeOwC62+n5133rlohJYrbrvttoI92VVXXZViC++yyy7DfnQty+tHBurMs37ak2W6lqLZd6X6kYEs032zeyQ3ipJz6aWXNsuu8/tpVjLvtORAtuFtyZbpOMhm9R3veEfK3kVDK1nBYYcd1qz8D37wg6QBbcTeTdEb2B814u4Wb3vb24rLLrssmQNstdVWRSP+YPOeSe702p5tt902abglawjbqv3337/YfvvtJ9mMrp9dfT9Pf/rTi3322Se9QxpqZiZvetObUti1rgud8gt7lYG686zX9mSZnnIBblH9XmUgy3QLJk7w0PXXX58y5XGKRnV/PxNkVe0f/SCxiGtfy1zBjhxgt4t6cW7iPfyoRz2qFuG7qo3rtT3//d//nULKAIjTaMpQbT/tu6gZ1RBy1etm+XevMlB3nvXanizTsyfdvcpAlul6y0Dd30+9uTeZ2mXAOxm+56dmDmQOZA5kDmQOZA5kDmQOjIkD2YZ3TIzOj8kcyBzIHMgcyBzIHMgcyByYDAcy4J0M3/NTMwcyBzIHMgcyBzIHMgcyB8bEgQx4x8To/JjMgcyBzIHMgcyBzIHMgcyByXAgA97J8D0/NXMgcyBzIHMgcyBzIHMgc2BMHMiAd0yMzo/JHMgcWHgckAzmggsuKL73ve/VvvFf+MIXile/+tWFOrej++67L6XuPv/881PM62nMaNiubfl45kDmwGxzIMfhne33m1uXOZA5MEEOfOtb3yr23HPPFA9bhqY60+GHH17cfPPNhdSp4nuX6R//+Ech3vUZZ5xRALnC/9mus846xYc//OFi9dVXL1+e9zMHMgcyB2rHgazhrd0ryRXKHMgcmBUOrLTSSsWb3/zm4qlPfWqtm/SVr3wlgd3111+/OO2004o///nPi9X3mGOOKU4//fRijz32KKTvFsf7pJNOKu68885ip512Kv76178udn3+kTmQOZA5UDcO5Di8dXsjuT6ZA5kDmQNj5sDuu++eTBQ+97nPFauttlpx6qmnFvvtt1+zFiussEIhcQJzh3/7t39rHt97771TenOa4Q022KB5PO9kDmQOZA7UjQNZw1u3N5LrkzmQOTA1HKAZ3WWXXYonPelJaXl/r732Kr7xjW8063/HHXcUUpJed911zWN27rrrrqT5ZRKwxRZbJFMB17iW9hQdccQRxbve9a6U1nTfffct1lxzzWLHHXcsrrrqqnQeOHX92muvnVKG//CHP0zHy/+YVLz85S8vNtpoo+IJT3hC8ZznPCeVWdbI/uhHPyo++tGPFlLgMrt47nOfW5xwwglFJOH8y1/+UgC2xx577GJg13PUH3Wy+00X5H+ZA5kDmQMT5kDW8E74BeTHZw5kDkwnB77+9a8nIEn7CSSyc73iiiuKX/ziF8X111+fNJ5ALEB71llnFS972ctSQ7///e8X6623XvEv//IvBc0q8wFOYFJjf/e73y3uvffeYuWVVy6e9axnpRTTv/rVrxLYdf7SSy9N9rPsbd/+9rcXm222WfHIRz6y+PjHP1487nGPK+6+++4mKP30pz+dALLjW265ZUoj/qlPfaoAjJkmeCZ605veVBx//PEF4CudtWu222674jOf+UyxzTbbpGva/XvGM55R3HrrrQVnthVXXLHdZfl45kDmQObA5DnQmMVnyhzIHMgcyBzokQOHHHLIXMN5a+4nP/lJ886GRnXuYQ972FzDbjcdu/baa+cavfxcA/A2r1l33XXnGo5hcz/+8Y+bx26//fa5BgBO1zYiOqTjDa1s+r1o0aLmdQ3Am465tgG4m8c9z3NuvPHG5rEGqJ5beuml5xoAvHms4Wg29/jHP37uIQ95yJz9P/3pT3P//u//Prfzzjs3r2kA97mG7fHcVltt1TzWaueSSy5Jz2xotVudzscyBzIHMgdqxYFs0jD5OUeuQeZA5sAUcmCZZZZJkQo4c/3yl79MLWDa0ACRBSevVkT7e9tttxWvetWrisc+9rHNS572tKcVL37xi5u/yzsHH3xw8+fGG2+c9p/97GcXa621VvM4swb0wAMPNI/R4H7xi19MURfi4IMe9KCklf773/9e/PGPf0za2Wc+85nFa1/72rgkaZCPPPLIpIGOdjVP/t8O7a/oE6uuumpx4oknVk/n35kDmQOZA7XjQAa8tXsluUKZA5kD08ABdrVPecpTioYGtlhuueUKEQ6OOuqo4r/+67/aVv9rX/taOvfkJz95iWvY6FapoX0tGlra5uHYZ6ZQpqWWWir9FCosSKgw13NAO/DAA4uGxjbV8yMf+Ui6BOjdZJNNkunChhtuGLelLZtdJhFClFXpQx/6UPGCF7wgmV0w3Yg6Va/LvzMHMgcyB+rEgQx46/Q2cl0yBzIHpoYDbGpvueWWZJ+7/fbbJ+cy4BdwBQpb0e9+97t0mKa1Sg0zg+qhtmBSHNz5CND9j//4jwR2aXqXX375BM45uvVLRx99dHJuoxW+6aabkt1wv2Xl+zIHMgcyB8bJgfl7zXHWJj8rcyBzIHNgijgg2gFntMsuuyyZNVx55ZXJaUzs3bK2NZokUgLinFalTprh6rXz/RbpgVZX/F/7IkecffbZKZNaxNhtVb9O5R522GHFW9/61mK33XYrrrnmmoJJR6bMgcyBzIFp4UAGvNPypnI9MwcyB2rFAQkXRCYQRQGJuiBagxBg7HjLob+i4gDoE5/4xOKDH/xgsqGN4w3Ht+LCCy+MnwNvv/nNbybAzYyhbJbQcJRLdrsewKShWzrllFOK97znPSk2r8xqD33oQ7u9NV+XOZA5kDlQCw4suYZWi2rlSmQOZA5kDtSbA29729uSZpezmSQNwpOJjStOrmPlBA3Rkgc/+MHF+973vmKHHXYohPRiBwwYn3zyyU2NcCtzh7i/2y2zCqD0nHPOSbFyObUJH8bGGBhHzCvYHs9HHO3EBGZyoW4HHXTQErfQ+lbtgJe4KB/IHMgcyByYIAcy4J0g8/OjMwcyB6aXA7S1tJ7i4TbCj6WGPPzhD0+JHj7wgQ+0bdjznve8dD1733e+850p9u0BBxyQ4ve+//3vL8IBrW0BXZwQAeLiiy9OCSR23XXXFCOYc5n4veq99dZbp2QYsqrNR8wXfvvb36bLRKRoRcrMgLcVZ/KxzIHMgbpwICeeqMubyPXIHMgcmEoO/O1vfyvuv//+wpaNbieHMnazQodxIKtqcvfZZ5/ivPPOS4komEcMi4QWE4KM+UX1mcN6Ri4ncyBzIHOg7hzIgLfubyjXL3Mgc2CmOCAzmri7N9xwQ7Ndsq8JcUZTevPNNzeP553MgcyBzIHMgeFwIJs0DIePuZTMgcyBzIGuOMBul+kCEwBph6X0Fd3hEY94RHHmmWd2VUa+KHMgcyBzIHOgNw5kDW9v/MpXZw5kDmQODMSBRure4rOf/WxybmMfK4rCBhtsUDBpEMEhU+ZA5kDmQObA8DmQAe/weZpLzBzIHMgcyBzIHMgcyBzIHKgRB3Ic3hq9jFyVzIHMgcyBzIHMgcyBzIHMgeFzIAPe4fM0l5g5kDmQOZA5kDmQOZA5kDlQIw5kwFujl5GrkjmQOZA5kDmQOZA5kDmQOTB8DmTAO3ye5hIzBzIHMgcyBzIHMgcyBzIHasSBDHhr9DJyVTIHMgcyBzIHMgcyBzIHMgeGz4EMeIfP01xi5kDmQOZA5kDmQOZA5kDmQI048P8B05Gb9eWPnfkAAAAASUVORK5CYII=" /></p> <!-- rnb-plot-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> </div> <div id="figure-s2" class="section level2"> <h2>Figure S2</h2> <p>Plot FigS2 all significant results (CI not overlapping zero) for males ### FELIX: “ALL” missing. Felix added 11/2/2020: done</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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--> <pre class="r"><code>meta.plot2.sig.bS <- meta.plot2.sig[, c("lnCVR", "lnVR", "lnRR", "lnCVRsig", "lnVRsig", "lnRRsig", "GroupingTerm")] meta.plot2.sig.cS <- gather(meta.plot2.sig.bS, trait, value, lnCVR:lnRR) meta.plot2.sig.cS$sig <- "placeholder" meta.plot2.sig.cS$trait <- factor(meta.plot2.sig.cS$trait, levels = c("lnCVR", "lnVR", "lnRR")) meta.plot2.sig.cS$sig <- ifelse(meta.plot2.sig.cS$trait == "lnCVR", meta.plot2.sig.cS$lnCVRsig, ifelse(meta.plot2.sig.cS$trait == "lnVR", meta.plot2.sig.cS$lnVRsig, meta.plot2.sig.cS$lnRRsig) ) # choosing sex biased ln-ratios significantly larger than 0 meta.plotS2.sig.malebias <- meta.plot2.sig.cS %>% group_by_at(vars(trait, GroupingTerm)) %>% filter(sig == 1) %>% summarise(male_sig = sum(value > 0), female_sig = sum(value < 0), total = male_sig + female_sig) meta.plotS2.sig.malebias <- ungroup(meta.plotS2.sig.malebias) %>% add_row(trait = "lnCVR", GroupingTerm = "Hearing", male_sig = 0, female_sig = 0, .before = 4) %>% # add "Hearing" for lnCVR (not filtered as only zeros) mutate(malepercent = male_sig * 100 / total, femalepercent = female_sig * 100 / total) meta.plotS2.sig.malebias$label <- "CI not overlapping zero" # restructure to create stacked bar plots meta.plotS2.sig.bothsexes <- as.data.frame(meta.plotS2.sig.malebias) meta.plotS2.sig.bothsexes.b <- gather(meta.plotS2.sig.bothsexes, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE) # create new sample size variable meta.plotS2.sig.bothsexes.b$samplesize <- with(meta.plotS2.sig.bothsexes.b, ifelse(sex == "malepercent", male_sig, female_sig)) # Add summary row ('All') and re-arrange rows into correct order for plotting (warnings about coercing 'id' into character vector are ok) meta.plotS2.sig.bothsexes.c <- meta.plotS2.sig.bothsexes.b %>% group_by(trait, sex) %>% summarise(GroupingTerm = "All", male_sig = sum(male_sig), female_sig = sum(female_sig), total = male_sig + female_sig, label = "CI not overlapping zero", samplesize = sum(samplesize)) %>% mutate(percent = ifelse(sex == "femalepercent", female_sig*100/(male_sig + female_sig), male_sig*100/(male_sig + female_sig))) %>% bind_rows(meta.plotS2.sig.bothsexes.b, .) %>% mutate(rownumber = row_number()) %>% .[c(55, 1:9, 57, 10:18, 59, 19:27, 56, 28:36, 58, 37:45, 60, 46:54), ] meta.plotS2.sig.bothsexes.c$GroupingTerm <- factor(meta.plotS2.sig.bothsexes.c$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) meta.plotS2.sig.bothsexes.c$GroupingTerm <- factor(meta.plotS2.sig.bothsexes.c$GroupingTerm, rev(levels(meta.plotS2.sig.bothsexes.c$GroupingTerm))) # *Plot Fig2 all significant results (CI not overlapping zero): # no sig. lnCVR for 'Hearing' in either sex; no sig. male-biased lnCVR for 'Immunology' and 'Eye, and no sig. male-biased lnVR for 'Eye' malebias_FigS2_sigtraits <- ggplot(meta.plotS2.sig.bothsexes.c) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text( data = subset(meta.plotS2.sig.bothsexes.c, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5), color = "white", size = 3.5 ) + facet_grid( cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18), scales = "free", space = "free" ) + scale_fill_brewer(palette = "Set2") + theme_bw(base_size = 18) + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip() # malebias_FigS2_sigtraits # this is Figure S2 A</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <div id="prepare-data-for-traits-with-effect-size-ratios-10-larger-in-males-supplemental-figure-s2" class="section level3"> <h3>Prepare data for traits with effect size ratios > 10% larger in males, supplemental Figure S2</h3> </div> <div id="felix-all-missing.-felix-added-1122020-done" class="section level3"> <h3>FELIX: “ALL” missing. Felix added 11/2/2020: done</h3> <p>This Figure extends Figure 4, as it includes results not only for lnCVR and lnRR but also lnCVR. In addition, we compare two different assessments of sex-bias, significance (CI not overlapping zero) and sex differences in male / female ratios > 10%</p> </div> <div id="over-10-male-bias-count-data-first--order-metanalysis" class="section level3"> <h3>Over 10% male bias, count data (first- order metanalysis)</h3> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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--> <pre class="r"><code>meta.plot2.over10 <- meta_clean %>% select(lnCVR, lnVR, lnRR, GroupingTerm) %>% arrange(GroupingTerm) meta.plot2.over10.b <- gather(meta.plot2.over10, trait, value, c(lnCVR, lnVR, lnRR)) meta.plot2.over10.b$trait <- factor(meta.plot2.over10.b$trait, levels = c("lnCVR", "lnVR", "lnRR")) meta.plot2.over10.c <- meta.plot2.over10.b %>% group_by_at(vars(trait, GroupingTerm)) %>% summarise( malebias = sum(value > log(11 / 10)), femalebias = sum(value < log(9 / 10)), total = malebias + femalebias, malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total ) meta.plot2.over10.c$label <- "Sex difference in m/f ratios > 10%" # restructure to create stacked bar plots meta.plot2.over10.c <- as.data.frame(meta.plot2.over10.c) meta.plot2.over10.d <- gather(meta.plot2.over10.c, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE) # create new sample size variable meta.plot2.over10.d$samplesize <- with(meta.plot2.over10.d, ifelse(sex == "malepercent", malebias, femalebias)) # Add summary row ('All') and re-arrange rows into correct order for plotting (warnings about coercing 'id' into character vector are ok) meta.plot2.over10.e <- meta.plot2.over10.d %>% group_by(trait, sex) %>% summarise(GroupingTerm = "All", malebias = sum(malebias), femalebias = sum(femalebias), total = malebias + femalebias, label = "Sex difference in m/f ratios > 10%", samplesize = sum(samplesize)) %>% mutate(percent = ifelse(sex == "femalepercent", femalebias*100/(malebias + femalebias), malebias*100/(malebias + femalebias))) %>% bind_rows(meta.plot2.over10.d, .) %>% mutate(rownumber = row_number()) %>% .[c(55, 1:9, 57, 10:18, 59, 19:27, 56, 28:36, 58, 37:45, 60, 46:54), ] meta.plot2.over10.e$GroupingTerm <- factor(meta.plot2.over10.e$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) meta.plot2.over10.e$GroupingTerm <- factor(meta.plot2.over10.e$GroupingTerm, rev(levels(meta.plot2.over10.e$GroupingTerm))) # *Plot Fig2 Sex difference in m/f ratio > 10% malebias_Fig2_over10 <- ggplot(meta.plot2.over10.e) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text( data = subset(meta.plot2.over10.e, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5), color = "white", size = 3.5 ) + facet_grid( cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18), scales = "free", space = "free" ) + scale_fill_brewer(palette = "Set2") + theme_bw(base_size = 18) + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_blank(), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip() # malebias_Fig2_over10 (supplemental Figure S2)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <div id="fig-s2-second-order-meta-analysis-male-traits" class="section level4"> <h4>Fig S2, second-order meta-analysis, male traits</h4> </div> <div id="female-figure-significant-traits" class="section level4"> <h4>Female Figure, significant traits</h4> <p>Female FigS2 B sig</p> <p>Prepare data for traits with CI not overlapping 0 create column with 1= different from zero, 0= zero included in CI</p> <p>Restructure data for plotting</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbWzEsIlJlbW92ZWQgOCByb3dzIGNvbnRhaW5pbmcgbWlzc2luZyB2YWx1ZXMgKGdlb21fZXJyb3JiYXJoKS4iXSxbMSwiUmVtb3ZlZCA3IHJvd3MgY29udGFpbmluZyBtaXNzaW5nIHZhbHVlcyAoZ2VvbV9wb2ludCkuIl1dLCJoZWlnaHQiOjQzMi42MzI5LCJzaXplX2JlaGF2aW9yIjowLCJ3aWR0aCI6NzAwfQ== --> <p><img 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" /></p> <!-- rnb-plot-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Prepare data for traits with m/f difference > 10%</p> <p>Create column with 1= larger, 0= difference not larger than 10% between male/female ratios</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin {"data":"```r\nmeta.male.plot3.perc <- metacombo %>%\n  mutate(\n    percCVR = ifelse(lnCVR > log(11 / 10), 1, 0),\n    percVR = ifelse(lnVR > log(11 / 10), 1, 0),\n    percRR = ifelse(lnRR > log(11 / 10), 1, 0)\n  )\n\n# Significant subset for lnCVR\nmetacombo_male.plot3.CVR.perc <- meta.male.plot3.perc %>%\n  filter(percCVR == 1) %>%\n  group_by(GroupingTerm) %>%\n  nest()\n\nmetacombo_male.plot3.CVR.perc.all <- meta.male.plot3.perc %>%\n  filter(percCVR == 1) %>%\n  nest(data = everything())\n\n# Significant subset for lnVR\nmetacombo_male.plot3.VR.perc <- meta.male.plot3.perc %>%\n  filter(percVR == 1) %>%\n  group_by(GroupingTerm) %>%\n  nest()\n\nmetacombo_male.plot3.VR.perc.all <- meta.male.plot3.perc %>%\n  filter(percVR == 1) %>%\n  nest(data = everything())\n\n# Significant subset for lnRR\nmetacombo_male.plot3.RR.perc <- meta.male.plot3.perc %>%\n  filter(percRR == 1) %>%\n  group_by(GroupingTerm) %>%\n  nest()\n\nmetacombo_male.plot3.RR.perc.all <- meta.male.plot3.perc %>%\n  filter(percRR == 1) %>%\n  nest(data = everything())\n\n\n# **Final fixed effects meta-analyses within grouping terms and across grouping terms, with SE of the estimate\n\nplot3.male.meta.CVR.perc <- metacombo_male.plot3.CVR.perc %>%\n  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(\n    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),\n    control = list(optimizer = \"optim\", optmethod = \"Nelder-Mead\", maxit = 1000), verbose = F\n  )))\n\nplot3.male.meta.VR.perc <- metacombo_male.plot3.VR.perc %>%\n  mutate(model_lnVR = map(data, ~ metafor::rma.uni(\n    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),\n    control = list(optimizer = \"optim\", optmethod = \"Nelder-Mead\", maxit = 1000), verbose = F\n  )))\n\nplot3.male.meta.RR.perc <- metacombo_male.plot3.RR.perc %>%\n  mutate(model_lnRR = map(data, ~ metafor::rma.uni(\n    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),\n    control = list(optimizer = \"optim\", optmethod = \"Nelder-Mead\", maxit = 1000), verbose = F\n  )))\n\n# Across all grouping terms #\n\nplot3.male.meta.CVR.perc.all <- metacombo_male.plot3.CVR.perc.all %>%\n  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(\n    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),\n    control = list(optimizer = \"optim\", optmethod = \"Nelder-Mead\", maxit = 1000), verbose = F\n  )))\n\nplot3.male.meta.CVR.perc.all <- plot3.male.meta.CVR.perc.all %>% mutate(GroupingTerm = \"All\")\n\nplot3.male.meta.VR.perc.all <- metacombo_male.plot3.VR.perc.all %>%\n  mutate(model_lnVR = map(data, ~ metafor::rma.uni(\n    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),\n    control = list(optimizer = \"optim\", optmethod = \"Nelder-Mead\", maxit = 1000), verbose = F\n  )))\n\nplot3.male.meta.VR.perc.all <- plot3.male.meta.VR.perc.all %>% mutate(GroupingTerm = \"All\")\n\nplot3.male.meta.RR.perc.all <- metacombo_male.plot3.RR.perc.all %>%\n  mutate(model_lnRR = map(data, ~ metafor::rma.uni(\n    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),\n    control = list(optimizer = \"optim\", optmethod = \"Nelder-Mead\", maxit = 1000), verbose = F\n  )))\n\nplot3.male.meta.RR.perc.all <- plot3.male.meta.RR.perc.all %>% mutate(GroupingTerm = \"All\")\n\n# Combine with separate grouping term results\n\nplot3.male.meta.CVR.perc <- bind_rows(plot3.male.meta.CVR.perc, plot3.male.meta.CVR.perc.all)\nplot3.male.meta.VR.perc <- bind_rows(plot3.male.meta.VR.perc, plot3.male.meta.VR.perc.all)\nplot3.male.meta.RR.perc <- bind_rows(plot3.male.meta.RR.perc, plot3.male.meta.RR.perc.all)\n\n\n# **Re-structure data for each grouping term; delete un-used variables: \"Hearing missing for all 3 parameters\"\n\nplot3.male.meta.CVR.perc.b <- as.data.frame(plot3.male.meta.CVR.perc %>% group_by(GroupingTerm) %>%\n  mutate(\n    lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)),\n    lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3))\n  ))[, c(1, 4:7)]\nadd.row.hearing <- as.data.frame(t(c(\"Hearing\", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.CVR.perc.b))\nplot3.male.meta.CVR.perc.b <- rbind(plot3.male.meta.CVR.perc.b, add.row.hearing)\nplot3.male.meta.CVR.perc.b <- plot3.male.meta.CVR.perc.b[order(plot3.male.meta.CVR.perc.b$GroupingTerm), ]\n\nplot3.male.meta.VR.perc.b <- as.data.frame(plot3.male.meta.VR.perc %>% group_by(GroupingTerm) %>%\n  mutate(\n    lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)),\n    lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3))\n  ))[, c(1, 4:7)]\nadd.row.hearing <- as.data.frame(t(c(\"Hearing\", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.VR.perc.b))\nplot3.male.meta.VR.perc.b <- rbind(plot3.male.meta.VR.perc.b, add.row.hearing)\nplot3.male.meta.VR.perc.b <- plot3.male.meta.VR.perc.b[order(plot3.male.meta.VR.perc.b$GroupingTerm), ]\n\nplot3.male.meta.RR.perc.b <- as.data.frame(plot3.male.meta.RR.perc %>% group_by(GroupingTerm) %>%\n  mutate(\n    lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)),\n    lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3))\n  ))[, c(1, 4:7)]\nadd.row.hearing <- as.data.frame(t(c(\"Hearing\", NA, NA, NA, NA))) %>%\n  setNames(names(plot3.male.meta.RR.perc.b))\nplot3.male.meta.RR.perc.b <- rbind(plot3.male.meta.RR.perc.b, add.row.hearing)\n\nadd.row.eye <- as.data.frame(t(c(\"Eye\", NA, NA, NA, NA))) %>%\n  setNames(names(plot3.male.meta.RR.perc.b))\nplot3.male.meta.RR.perc.b <- rbind(plot3.male.meta.RR.perc.b, add.row.eye)\n\nplot3.male.meta.RR.perc.b <- plot3.male.meta.RR.perc.b[order(plot3.male.meta.RR.perc.b$GroupingTerm), ]\n\nplot3.male.meta.CVR.Vr.perc <- full_join(plot3.male.meta.CVR.perc.b, plot3.male.meta.VR.perc.b)\noverall.male.plot3.perc <- full_join(plot3.male.meta.CVR.Vr.perc, plot3.male.meta.RR.perc.b)\n\n\noverall.male.plot3.perc$GroupingTerm <- factor(overall.male.plot3.perc$GroupingTerm, levels = c(\"Behaviour\", \"Morphology\", \"Metabolism\", \"Physiology\", \"Immunology\", \"Hematology\", \"Heart\", \"Hearing\", \"Eye\", \"All\"))\noverall.male.plot3.perc$GroupingTerm <- factor(overall.male.plot3.perc$GroupingTerm, rev(levels(overall.male.plot3.perc$GroupingTerm)))\n\n```"} --> <pre class="r"><code>meta.male.plot3.perc <- metacombo %>% mutate( percCVR = ifelse(lnCVR > log(11 / 10), 1, 0), percVR = ifelse(lnVR > log(11 / 10), 1, 0), percRR = ifelse(lnRR > log(11 / 10), 1, 0) ) # Significant subset for lnCVR metacombo_male.plot3.CVR.perc <- meta.male.plot3.perc %>% filter(percCVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.CVR.perc.all <- meta.male.plot3.perc %>% filter(percCVR == 1) %>% nest(data = everything()) # Significant subset for lnVR metacombo_male.plot3.VR.perc <- meta.male.plot3.perc %>% filter(percVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.VR.perc.all <- meta.male.plot3.perc %>% filter(percVR == 1) %>% nest(data = everything()) # Significant subset for lnRR metacombo_male.plot3.RR.perc <- meta.male.plot3.perc %>% filter(percRR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.RR.perc.all <- meta.male.plot3.perc %>% filter(percRR == 1) %>% nest(data = everything()) # **Final fixed effects meta-analyses within grouping terms and across grouping terms, with SE of the estimate plot3.male.meta.CVR.perc <- metacombo_male.plot3.CVR.perc %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.VR.perc <- metacombo_male.plot3.VR.perc %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.RR.perc <- metacombo_male.plot3.RR.perc %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) # Across all grouping terms # plot3.male.meta.CVR.perc.all <- metacombo_male.plot3.CVR.perc.all %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.CVR.perc.all <- plot3.male.meta.CVR.perc.all %>% mutate(GroupingTerm = "All") plot3.male.meta.VR.perc.all <- metacombo_male.plot3.VR.perc.all %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.VR.perc.all <- plot3.male.meta.VR.perc.all %>% mutate(GroupingTerm = "All") plot3.male.meta.RR.perc.all <- metacombo_male.plot3.RR.perc.all %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.RR.perc.all <- plot3.male.meta.RR.perc.all %>% mutate(GroupingTerm = "All") # Combine with separate grouping term results plot3.male.meta.CVR.perc <- bind_rows(plot3.male.meta.CVR.perc, plot3.male.meta.CVR.perc.all) plot3.male.meta.VR.perc <- bind_rows(plot3.male.meta.VR.perc, plot3.male.meta.VR.perc.all) plot3.male.meta.RR.perc <- bind_rows(plot3.male.meta.RR.perc, plot3.male.meta.RR.perc.all) # **Re-structure data for each grouping term; delete un-used variables: "Hearing missing for all 3 parameters" plot3.male.meta.CVR.perc.b <- as.data.frame(plot3.male.meta.CVR.perc %>% group_by(GroupingTerm) %>% mutate( lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)), lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.CVR.perc.b)) plot3.male.meta.CVR.perc.b <- rbind(plot3.male.meta.CVR.perc.b, add.row.hearing) plot3.male.meta.CVR.perc.b <- plot3.male.meta.CVR.perc.b[order(plot3.male.meta.CVR.perc.b$GroupingTerm), ] plot3.male.meta.VR.perc.b <- as.data.frame(plot3.male.meta.VR.perc %>% group_by(GroupingTerm) %>% mutate( lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)), lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.VR.perc.b)) plot3.male.meta.VR.perc.b <- rbind(plot3.male.meta.VR.perc.b, add.row.hearing) plot3.male.meta.VR.perc.b <- plot3.male.meta.VR.perc.b[order(plot3.male.meta.VR.perc.b$GroupingTerm), ] plot3.male.meta.RR.perc.b <- as.data.frame(plot3.male.meta.RR.perc %>% group_by(GroupingTerm) %>% mutate( lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)), lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.RR.perc.b)) plot3.male.meta.RR.perc.b <- rbind(plot3.male.meta.RR.perc.b, add.row.hearing) add.row.eye <- as.data.frame(t(c("Eye", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.RR.perc.b)) plot3.male.meta.RR.perc.b <- rbind(plot3.male.meta.RR.perc.b, add.row.eye) plot3.male.meta.RR.perc.b <- plot3.male.meta.RR.perc.b[order(plot3.male.meta.RR.perc.b$GroupingTerm), ] plot3.male.meta.CVR.Vr.perc <- full_join(plot3.male.meta.CVR.perc.b, plot3.male.meta.VR.perc.b) overall.male.plot3.perc <- full_join(plot3.male.meta.CVR.Vr.perc, plot3.male.meta.RR.perc.b) overall.male.plot3.perc$GroupingTerm <- factor(overall.male.plot3.perc$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall.male.plot3.perc$GroupingTerm <- factor(overall.male.plot3.perc$GroupingTerm, rev(levels(overall.male.plot3.perc$GroupingTerm))) </code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Restructure data for plotting : Male biased, 10% difference</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxub3ZlcmFsbDMucGVyYyA8LSBnYXRoZXIob3ZlcmFsbC5tYWxlLnBsb3QzLnBlcmMsIHBhcmFtZXRlciwgdmFsdWUsIGMobG5DVlIsIGxuVlIsIGxuUlIpLCBmYWN0b3Jfa2V5ID0gVFJVRSkgXG5cbmxuQ1ZSLmNpIDwtIG92ZXJhbGwzLnBlcmMgJT4lXG4gIGZpbHRlcihwYXJhbWV0ZXIgPT0gXCJsbkNWUlwiKSAlPiVcbiAgbXV0YXRlKGNpLmxvdyA9IGxuQ1ZSX2xvd2VyLCBjaS5oaWdoID0gbG5DVlJfdXBwZXIpXG5sblZSLmNpIDwtIG92ZXJhbGwzLnBlcmMgICU+JSBmaWx0ZXIocGFyYW1ldGVyID09IFwibG5WUlwiKSAlPiUgbXV0YXRlKGNpLmxvdyA9IGxuVlJfbG93ZXIsIGNpLmhpZ2ggPSBsblZSX3VwcGVyKVxubG5SUi5jaSA8LSBvdmVyYWxsMy5wZXJjICU+JVxuICBmaWx0ZXIocGFyYW1ldGVyID09IFwibG5SUlwiKSAlPiVcbiAgbXV0YXRlKGNpLmxvdyA9IGxuUlJfbG93ZXIsIGNpLmhpZ2ggPSBsblJSX3VwcGVyKVxuXG5vdmVyYWxsNC5tYWxlLnBlcmMgPC0gYmluZF9yb3dzKGxuQ1ZSLmNpLGxuVlIuY2ksIGxuUlIuY2kpICU+JSBzZWxlY3QoR3JvdXBpbmdUZXJtLCBwYXJhbWV0ZXIsIHZhbHVlLCBjaS5sb3csIGNpLmhpZ2gpIFxuXG5vdmVyYWxsNC5tYWxlLnBlcmMkbGFiZWwgPC0gXCJTZXggZGlmZmVyZW5jZSBpbiBtL2YgcmF0aW9zID4gMTAlXCJcblxub3ZlcmFsbDQubWFsZS5wZXJjJHZhbHVlIDwtIGFzLm51bWVyaWMob3ZlcmFsbDQubWFsZS5wZXJjJHZhbHVlKVxub3ZlcmFsbDQubWFsZS5wZXJjJGNpLmxvdyA8LSBhcy5udW1lcmljKG92ZXJhbGw0Lm1hbGUucGVyYyRjaS5sb3cpXG5vdmVyYWxsNC5tYWxlLnBlcmMkY2kuaGlnaCA8LSBhcy5udW1lcmljKG92ZXJhbGw0Lm1hbGUucGVyYyRjaS5oaWdoKVxuYGBgIn0= --> <pre class="r"><code>overall3.perc <- gather(overall.male.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3.perc %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3.perc %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.perc %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.male.perc <- bind_rows(lnCVR.ci,lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.male.perc$label <- "Sex difference in m/f ratios > 10%" overall4.male.perc$value <- as.numeric(overall4.male.perc$value) overall4.male.perc$ci.low <- as.numeric(overall4.male.perc$ci.low) overall4.male.perc$ci.high <- as.numeric(overall4.male.perc$ci.high)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Plot Fig S2 all >10% difference (male bias) S2 B, bottom right</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code> Metameta_Fig3_male.perc <- overall4.male.perc %>% # filter(., GroupingTerm != "Hearing") %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes( shape = parameter, fill = parameter ), color = "mediumaquamarine", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(-0.2, 0.62), breaks = c(0, 0.3), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_blank(), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = 0.5, size = 14), axis.title.y = element_blank() ) # Metameta_Fig3_male.perc (Figure S2 right panel)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="female-fig-s2-10" class="section level4"> <h4>Female Fig S2 >10%</h4> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuXG5tZXRhLnBsb3QzLnBlcmMgPC0gbWV0YWNvbWJvICU+JVxuICBtdXRhdGUoXG4gICAgcGVyY0NWUiA9IGlmZWxzZShsbkNWUiA8IGxvZyg5IC8gMTApLCAxLCAwKSxcbiAgICBwZXJjVlIgPSBpZmVsc2UobG5WUiA8IGxvZyg5IC8gMTApLCAxLCAwKSxcbiAgICBwZXJjUlIgPSBpZmVsc2UobG5SUiA8IGxvZyg5IC8gMTApLCAxLCAwKVxuICApXG5cbiMgU2lnbmlmaWNhbnQgc3Vic2V0IGZvciBsbkNWUlxubWV0YWNvbWJvX3Bsb3QzLkNWUi5wZXJjIDwtIG1ldGEucGxvdDMucGVyYyAlPiVcbiAgZmlsdGVyKHBlcmNDVlIgPT0gMSkgJT4lXG4gIGdyb3VwX2J5KEdyb3VwaW5nVGVybSkgJT4lXG4gIG5lc3QoKVxuXG5tZXRhY29tYm9fcGxvdDMuQ1ZSLnBlcmMuYWxsIDwtIG1ldGEucGxvdDMucGVyYyAlPiVcbiAgZmlsdGVyKHBlcmNDVlIgPT0gMSkgJT4lXG4gIG5lc3QoZGF0YSA9IGV2ZXJ5dGhpbmcoKSlcblxuIyBTaWduaWZpY2FudCBzdWJzZXQgZm9yIGxuVlJcbm1ldGFjb21ib19wbG90My5WUi5wZXJjIDwtIG1ldGEucGxvdDMucGVyYyAlPiVcbiAgZmlsdGVyKHBlcmNWUiA9PSAxKSAlPiVcbiAgZ3JvdXBfYnkoR3JvdXBpbmdUZXJtKSAlPiVcbiAgbmVzdCgpXG5cbm1ldGFjb21ib19wbG90My5WUi5wZXJjLmFsbCA8LSBtZXRhLnBsb3QzLnBlcmMgJT4lXG4gIGZpbHRlcihwZXJjVlIgPT0gMSkgJT4lXG4gIG5lc3QoZGF0YSA9IGV2ZXJ5dGhpbmcoKSlcblxuIyBTaWduaWZpY2FudCBzdWJzZXQgZm9yIGxuUlJcbm1ldGFjb21ib19wbG90My5SUi5wZXJjIDwtIG1ldGEucGxvdDMucGVyYyAlPiVcbiAgZmlsdGVyKHBlcmNSUiA9PSAxKSAlPiVcbiAgZ3JvdXBfYnkoR3JvdXBpbmdUZXJtKSAlPiVcbiAgbmVzdCgpXG5cbm1ldGFjb21ib19wbG90My5SUi5wZXJjLmFsbCA8LSBtZXRhLnBsb3QzLnBlcmMgJT4lXG4gIGZpbHRlcihwZXJjUlIgPT0gMSkgJT4lXG4gIG5lc3QoZGF0YSA9IGV2ZXJ5dGhpbmcoKSlcblxuXG4jICoqRmluYWwgZml4ZWQgZWZmZWN0cyBtZXRhLWFuYWx5c2VzIHdpdGhpbiBncm91cGluZyB0ZXJtcywgd2l0aCBTRSBvZiB0aGUgZXN0aW1hdGVcblxucGxvdDMubWV0YS5DVlIucGVyYyA8LSBtZXRhY29tYm9fcGxvdDMuQ1ZSLnBlcmMgJT4lXG4gIG11dGF0ZShtb2RlbF9sbkNWUiA9IG1hcChkYXRhLCB+IG1ldGFmb3I6OnJtYS51bmkoXG4gICAgeWkgPSAueCRsbkNWUiwgc2VpID0gKC54JGxuQ1ZSX3VwcGVyIC0gLngkbG5DVlJfbG93ZXIpIC8gKDIgKiAxLjk2KSxcbiAgICBjb250cm9sID0gbGlzdChvcHRpbWl6ZXIgPSBcIm9wdGltXCIsIG9wdG1ldGhvZCA9IFwiTmVsZGVyLU1lYWRcIiwgbWF4aXQgPSAxMDAwKSwgdmVyYm9zZSA9IEZcbiAgKSkpXG5cbnBsb3QzLm1ldGEuVlIucGVyYyA8LSBtZXRhY29tYm9fcGxvdDMuVlIucGVyYyAlPiVcbiAgbXV0YXRlKG1vZGVsX2xuVlIgPSBtYXAoZGF0YSwgfiBtZXRhZm9yOjpybWEudW5pKFxuICAgIHlpID0gLngkbG5WUiwgc2VpID0gKC54JGxuVlJfdXBwZXIgLSAueCRsblZSX2xvd2VyKSAvICgyICogMS45NiksXG4gICAgY29udHJvbCA9IGxpc3Qob3B0aW1pemVyID0gXCJvcHRpbVwiLCBvcHRtZXRob2QgPSBcIk5lbGRlci1NZWFkXCIsIG1heGl0ID0gMTAwMCksIHZlcmJvc2UgPSBGXG4gICkpKVxuXG5wbG90My5tZXRhLlJSLnBlcmMgPC0gbWV0YWNvbWJvX3Bsb3QzLlJSLnBlcmMgJT4lXG4gIG11dGF0ZShtb2RlbF9sblJSID0gbWFwKGRhdGEsIH4gbWV0YWZvcjo6cm1hLnVuaShcbiAgICB5aSA9IC54JGxuUlIsIHNlaSA9ICgueCRsblJSX3VwcGVyIC0gLngkbG5SUl9sb3dlcikgLyAoMiAqIDEuOTYpLFxuICAgIGNvbnRyb2wgPSBsaXN0KG9wdGltaXplciA9IFwib3B0aW1cIiwgb3B0bWV0aG9kID0gXCJOZWxkZXItTWVhZFwiLCBtYXhpdCA9IDEwMDApLCB2ZXJib3NlID0gRlxuICApKSlcblxuIyBBY3Jvc3MgYWxsIGdyb3VwaW5nIHRlcm1zICNcblxucGxvdDMubWV0YS5DVlIucGVyYy5hbGwgPC0gbWV0YWNvbWJvX3Bsb3QzLkNWUi5wZXJjLmFsbCAlPiVcbiAgbXV0YXRlKG1vZGVsX2xuQ1ZSID0gbWFwKGRhdGEsIH4gbWV0YWZvcjo6cm1hLnVuaShcbiAgICB5aSA9IC54JGxuQ1ZSLCBzZWkgPSAoLngkbG5DVlJfdXBwZXIgLSAueCRsbkNWUl9sb3dlcikgLyAoMiAqIDEuOTYpLFxuICAgIGNvbnRyb2wgPSBsaXN0KG9wdGltaXplciA9IFwib3B0aW1cIiwgb3B0bWV0aG9kID0gXCJOZWxkZXItTWVhZFwiLCBtYXhpdCA9IDEwMDApLCB2ZXJib3NlID0gRlxuICApKSlcblxucGxvdDMubWV0YS5DVlIucGVyYy5hbGwgPC0gcGxvdDMubWV0YS5DVlIucGVyYy5hbGwgJT4lIG11dGF0ZShHcm91cGluZ1Rlcm0gPSBcIkFsbFwiKVxuXG5wbG90My5tZXRhLlZSLnBlcmMuYWxsIDwtIG1ldGFjb21ib19wbG90My5WUi5wZXJjLmFsbCAlPiVcbiAgbXV0YXRlKG1vZGVsX2xuVlIgPSBtYXAoZGF0YSwgfiBtZXRhZm9yOjpybWEudW5pKFxuICAgIHlpID0gLngkbG5WUiwgc2VpID0gKC54JGxuVlJfdXBwZXIgLSAueCRsblZSX2xvd2VyKSAvICgyICogMS45NiksXG4gICAgY29udHJvbCA9IGxpc3Qob3B0aW1pemVyID0gXCJvcHRpbVwiLCBvcHRtZXRob2QgPSBcIk5lbGRlci1NZWFkXCIsIG1heGl0ID0gMTAwMCksIHZlcmJvc2UgPSBGXG4gICkpKVxuXG5wbG90My5tZXRhLlZSLnBlcmMuYWxsIDwtIHBsb3QzLm1ldGEuVlIucGVyYy5hbGwgJT4lIG11dGF0ZShHcm91cGluZ1Rlcm0gPSBcIkFsbFwiKVxuXG5wbG90My5tZXRhLlJSLnBlcmMuYWxsIDwtIG1ldGFjb21ib19wbG90My5SUi5wZXJjLmFsbCAlPiVcbiAgbXV0YXRlKG1vZGVsX2xuUlIgPSBtYXAoZGF0YSwgfiBtZXRhZm9yOjpybWEudW5pKFxuICAgIHlpID0gLngkbG5SUiwgc2VpID0gKC54JGxuUlJfdXBwZXIgLSAueCRsblJSX2xvd2VyKSAvICgyICogMS45NiksXG4gICAgY29udHJvbCA9IGxpc3Qob3B0aW1pemVyID0gXCJvcHRpbVwiLCBvcHRtZXRob2QgPSBcIk5lbGRlci1NZWFkXCIsIG1heGl0ID0gMTAwMCksIHZlcmJvc2UgPSBGXG4gICkpKVxuXG5wbG90My5tZXRhLlJSLnBlcmMuYWxsIDwtIHBsb3QzLm1ldGEuUlIucGVyYy5hbGwgJT4lIG11dGF0ZShHcm91cGluZ1Rlcm0gPSBcIkFsbFwiKVxuXG4jIENvbWJpbmUgd2l0aCBzZXBhcmF0ZSBncm91cGluZyB0ZXJtIHJlc3VsdHNcblxucGxvdDMubWV0YS5DVlIucGVyYyA8LSBiaW5kX3Jvd3MocGxvdDMubWV0YS5DVlIucGVyYywgcGxvdDMubWV0YS5DVlIucGVyYy5hbGwpXG5wbG90My5tZXRhLlZSLnBlcmMgPC0gYmluZF9yb3dzKHBsb3QzLm1ldGEuVlIucGVyYywgcGxvdDMubWV0YS5WUi5wZXJjLmFsbClcbnBsb3QzLm1ldGEuUlIucGVyYyA8LSBiaW5kX3Jvd3MocGxvdDMubWV0YS5SUi5wZXJjLCBwbG90My5tZXRhLlJSLnBlcmMuYWxsKVxuXG5cbiMgKipSZS1zdHJ1Y3R1cmUgZGF0YSBmb3IgZWFjaCBncm91cGluZyB0ZXJtOyBkZWxldGUgdW4tdXNlZCB2YXJpYWJsZXM6IFwiSGVhcmluZyBtaXNzaW5nIGZvciBhbGwgMyBwYXJhbWV0ZXJzXCJcblxucGxvdDMubWV0YS5DVlIucGVyYy5iIDwtIGFzLmRhdGEuZnJhbWUocGxvdDMubWV0YS5DVlIucGVyYyAlPiUgZ3JvdXBfYnkoR3JvdXBpbmdUZXJtKSAlPiVcbiAgbXV0YXRlKFxuICAgIGxuQ1ZSID0gbWFwX2RibChtb2RlbF9sbkNWUiwgcGx1Y2soMikpLCBsbkNWUl9sb3dlciA9IG1hcF9kYmwobW9kZWxfbG5DVlIsIHBsdWNrKDYpKSxcbiAgICBsbkNWUl91cHBlciA9IG1hcF9kYmwobW9kZWxfbG5DVlIsIHBsdWNrKDcpKSwgbG5DVlJfc2UgPSBtYXBfZGJsKG1vZGVsX2xuQ1ZSLCBwbHVjaygzKSlcbiAgKSlbLCBjKDEsIDQ6NyldXG5hZGQucm93LmhlYXJpbmcgPC0gYXMuZGF0YS5mcmFtZSh0KGMoXCJIZWFyaW5nXCIsIE5BLCBOQSwgTkEsIE5BKSkpICU+JSBzZXROYW1lcyhuYW1lcyhwbG90My5tZXRhLkNWUi5wZXJjLmIpKVxucGxvdDMubWV0YS5DVlIucGVyYy5iIDwtIHJiaW5kKHBsb3QzLm1ldGEuQ1ZSLnBlcmMuYiwgYWRkLnJvdy5oZWFyaW5nKVxucGxvdDMubWV0YS5DVlIucGVyYy5iIDwtIHBsb3QzLm1ldGEuQ1ZSLnBlcmMuYltvcmRlcihwbG90My5tZXRhLkNWUi5wZXJjLmIkR3JvdXBpbmdUZXJtKSwgXVxuXG5wbG90My5tZXRhLlZSLnBlcmMuYiA8LSBhcy5kYXRhLmZyYW1lKHBsb3QzLm1ldGEuVlIucGVyYyAlPiUgZ3JvdXBfYnkoR3JvdXBpbmdUZXJtKSAlPiVcbiAgbXV0YXRlKFxuICAgIGxuVlIgPSBtYXBfZGJsKG1vZGVsX2xuVlIsIHBsdWNrKDIpKSwgbG5WUl9sb3dlciA9IG1hcF9kYmwobW9kZWxfbG5WUiwgcGx1Y2soNikpLFxuICAgIGxuVlJfdXBwZXIgPSBtYXBfZGJsKG1vZGVsX2xuVlIsIHBsdWNrKDcpKSwgbG5WUl9zZSA9IG1hcF9kYmwobW9kZWxfbG5WUiwgcGx1Y2soMykpXG4gICkpWywgYygxLCA0OjcpXVxuYWRkLnJvdy5oZWFyaW5nIDwtIGFzLmRhdGEuZnJhbWUodChjKFwiSGVhcmluZ1wiLCBOQSwgTkEsIE5BLCBOQSkpKSAlPiUgc2V0TmFtZXMobmFtZXMocGxvdDMubWV0YS5WUi5wZXJjLmIpKVxucGxvdDMubWV0YS5WUi5wZXJjLmIgPC0gcmJpbmQocGxvdDMubWV0YS5WUi5wZXJjLmIsIGFkZC5yb3cuaGVhcmluZylcbnBsb3QzLm1ldGEuVlIucGVyYy5iIDwtIHBsb3QzLm1ldGEuVlIucGVyYy5iW29yZGVyKHBsb3QzLm1ldGEuVlIucGVyYy5iJEdyb3VwaW5nVGVybSksIF1cblxucGxvdDMubWV0YS5SUi5wZXJjLmIgPC0gYXMuZGF0YS5mcmFtZShwbG90My5tZXRhLlJSLnBlcmMgJT4lIGdyb3VwX2J5KEdyb3VwaW5nVGVybSkgJT4lXG4gIG11dGF0ZShcbiAgICBsblJSID0gbWFwX2RibChtb2RlbF9sblJSLCBwbHVjaygyKSksIGxuUlJfbG93ZXIgPSBtYXBfZGJsKG1vZGVsX2xuUlIsIHBsdWNrKDYpKSxcbiAgICBsblJSX3VwcGVyID0gbWFwX2RibChtb2RlbF9sblJSLCBwbHVjayg3KSksIGxuUlJfc2UgPSBtYXBfZGJsKG1vZGVsX2xuUlIsIHBsdWNrKDMpKVxuICApKVssIGMoMSwgNDo3KV1cbmFkZC5yb3cuaGVhcmluZyA8LSBhcy5kYXRhLmZyYW1lKHQoYyhcIkhlYXJpbmdcIiwgTkEsIE5BLCBOQSwgTkEpKSkgJT4lIHNldE5hbWVzKG5hbWVzKHBsb3QzLm1ldGEuUlIucGVyYy5iKSlcbnBsb3QzLm1ldGEuUlIucGVyYy5iIDwtIHJiaW5kKHBsb3QzLm1ldGEuUlIucGVyYy5iLCBhZGQucm93LmhlYXJpbmcpXG5hZGQucm93LmhlbWF0b2xvZ3kgPC0gYXMuZGF0YS5mcmFtZSh0KGMoXCJIZW1hdG9sb2d5XCIsIE5BLCBOQSwgTkEsIE5BKSkpICU+JVxuICBzZXROYW1lcyhuYW1lcyhwbG90My5tZXRhLlJSLnBlcmMuYikpXG5wbG90My5tZXRhLlJSLnBlcmMuYiA8LSByYmluZChwbG90My5tZXRhLlJSLnBlcmMuYiwgYWRkLnJvdy5oZW1hdG9sb2d5KVxuXG5cbnBsb3QzLm1ldGEuUlIucGVyYy5iIDwtIHBsb3QzLm1ldGEuUlIucGVyYy5iW29yZGVyKHBsb3QzLm1ldGEuUlIucGVyYy5iJEdyb3VwaW5nVGVybSksIF1cblxucGxvdDMubWV0YS5DVlIucGVyYy5jIDwtIGZ1bGxfam9pbihwbG90My5tZXRhLkNWUi5wZXJjLmIsIHBsb3QzLm1ldGEuVlIucGVyYy5iKVxub3ZlcmFsbC5wbG90My5wZXJjIDwtIGZ1bGxfam9pbihwbG90My5tZXRhLkNWUi5wZXJjLmMsIHBsb3QzLm1ldGEuUlIucGVyYy5iKVxuXG5cbm92ZXJhbGwucGxvdDMucGVyYyRHcm91cGluZ1Rlcm0gPC0gZmFjdG9yKG92ZXJhbGwucGxvdDMucGVyYyRHcm91cGluZ1Rlcm0sIGxldmVscyA9IGMoXCJCZWhhdmlvdXJcIiwgXCJNb3JwaG9sb2d5XCIsIFwiTWV0YWJvbGlzbVwiLCBcIlBoeXNpb2xvZ3lcIiwgXCJJbW11bm9sb2d5XCIsIFwiSGVtYXRvbG9neVwiLCBcIkhlYXJ0XCIsIFwiSGVhcmluZ1wiLCBcIkV5ZVwiLCBcIkFsbFwiKSlcbm92ZXJhbGwucGxvdDMucGVyYyRHcm91cGluZ1Rlcm0gPC0gZmFjdG9yKG92ZXJhbGwucGxvdDMucGVyYyRHcm91cGluZ1Rlcm0sIHJldihsZXZlbHMob3ZlcmFsbC5wbG90My5wZXJjJEdyb3VwaW5nVGVybSkpKVxuYGBgIn0= --> <pre class="r"><code> meta.plot3.perc <- metacombo %>% mutate( percCVR = ifelse(lnCVR < log(9 / 10), 1, 0), percVR = ifelse(lnVR < log(9 / 10), 1, 0), percRR = ifelse(lnRR < log(9 / 10), 1, 0) ) # Significant subset for lnCVR metacombo_plot3.CVR.perc <- meta.plot3.perc %>% filter(percCVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_plot3.CVR.perc.all <- meta.plot3.perc %>% filter(percCVR == 1) %>% nest(data = everything()) # Significant subset for lnVR metacombo_plot3.VR.perc <- meta.plot3.perc %>% filter(percVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_plot3.VR.perc.all <- meta.plot3.perc %>% filter(percVR == 1) %>% nest(data = everything()) # Significant subset for lnRR metacombo_plot3.RR.perc <- meta.plot3.perc %>% filter(percRR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_plot3.RR.perc.all <- meta.plot3.perc %>% filter(percRR == 1) %>% nest(data = everything()) # **Final fixed effects meta-analyses within grouping terms, with SE of the estimate plot3.meta.CVR.perc <- metacombo_plot3.CVR.perc %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.meta.VR.perc <- metacombo_plot3.VR.perc %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.meta.RR.perc <- metacombo_plot3.RR.perc %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) # Across all grouping terms # plot3.meta.CVR.perc.all <- metacombo_plot3.CVR.perc.all %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.meta.CVR.perc.all <- plot3.meta.CVR.perc.all %>% mutate(GroupingTerm = "All") plot3.meta.VR.perc.all <- metacombo_plot3.VR.perc.all %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.meta.VR.perc.all <- plot3.meta.VR.perc.all %>% mutate(GroupingTerm = "All") plot3.meta.RR.perc.all <- metacombo_plot3.RR.perc.all %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.meta.RR.perc.all <- plot3.meta.RR.perc.all %>% mutate(GroupingTerm = "All") # Combine with separate grouping term results plot3.meta.CVR.perc <- bind_rows(plot3.meta.CVR.perc, plot3.meta.CVR.perc.all) plot3.meta.VR.perc <- bind_rows(plot3.meta.VR.perc, plot3.meta.VR.perc.all) plot3.meta.RR.perc <- bind_rows(plot3.meta.RR.perc, plot3.meta.RR.perc.all) # **Re-structure data for each grouping term; delete un-used variables: "Hearing missing for all 3 parameters" plot3.meta.CVR.perc.b <- as.data.frame(plot3.meta.CVR.perc %>% group_by(GroupingTerm) %>% mutate( lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)), lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.CVR.perc.b)) plot3.meta.CVR.perc.b <- rbind(plot3.meta.CVR.perc.b, add.row.hearing) plot3.meta.CVR.perc.b <- plot3.meta.CVR.perc.b[order(plot3.meta.CVR.perc.b$GroupingTerm), ] plot3.meta.VR.perc.b <- as.data.frame(plot3.meta.VR.perc %>% group_by(GroupingTerm) %>% mutate( lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)), lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.VR.perc.b)) plot3.meta.VR.perc.b <- rbind(plot3.meta.VR.perc.b, add.row.hearing) plot3.meta.VR.perc.b <- plot3.meta.VR.perc.b[order(plot3.meta.VR.perc.b$GroupingTerm), ] plot3.meta.RR.perc.b <- as.data.frame(plot3.meta.RR.perc %>% group_by(GroupingTerm) %>% mutate( lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)), lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.RR.perc.b)) plot3.meta.RR.perc.b <- rbind(plot3.meta.RR.perc.b, add.row.hearing) add.row.hematology <- as.data.frame(t(c("Hematology", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.RR.perc.b)) plot3.meta.RR.perc.b <- rbind(plot3.meta.RR.perc.b, add.row.hematology) plot3.meta.RR.perc.b <- plot3.meta.RR.perc.b[order(plot3.meta.RR.perc.b$GroupingTerm), ] plot3.meta.CVR.perc.c <- full_join(plot3.meta.CVR.perc.b, plot3.meta.VR.perc.b) overall.plot3.perc <- full_join(plot3.meta.CVR.perc.c, plot3.meta.RR.perc.b) overall.plot3.perc$GroupingTerm <- factor(overall.plot3.perc$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall.plot3.perc$GroupingTerm <- factor(overall.plot3.perc$GroupingTerm, rev(levels(overall.plot3.perc$GroupingTerm)))</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Restructure data for plotting Female bias, 10 percent difference</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>overall3.perc <- gather(overall.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3.perc %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3.perc %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.perc %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.perc <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.perc$label <- "Sex difference in m/f ratios > 10%" overall4.perc$value <- as.numeric(overall4.perc$value) overall4.perc$ci.low <- as.numeric(overall4.perc$ci.low) overall4.perc$ci.high <- as.numeric(overall4.perc$ci.high)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Plot FigS2 all >10% difference (female) Figure S2B, bottom left</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>Metameta_Fig3_female.perc <- overall4.perc %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "salmon1", color = "salmon1", size = 2.2, show.legend = FALSE ) + # scale_shape_manual(values = scale_x_continuous( limits = c(-0.53, 0.2), breaks = c(-0.3, 0), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), # rows = vars(label), # labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_blank(), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = 0.5, size = 14), axis.title.y = element_blank() ) # Metameta_Fig3_female.perc (Figure 5D left panel)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>MISSING Metameta_Fig3_female.sig Metameta_Fig3_female.sig VR!!! # ADDED TO TEST #Metameta_FigS2_male.sig (Figure S2B top right panel)</p> <p>Restructure MALE data for plotting</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>overall3.male.sigS <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3.male.sigS %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3.male.sigS %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.male.sigS %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.male.sigS <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.male.sigS$label <- "CI not overlapping zero"</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Plot FigS2 all significant results (CI not overlapping zero, male )</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuTWV0YW1ldGFfRmlnUzJfbWFsZS5zaWcgPC0gb3ZlcmFsbDQubWFsZS5zaWdTICU+JVxuICBnZ3Bsb3QoYWVzKHkgPSBHcm91cGluZ1Rlcm0sIHggPSB2YWx1ZSkpICtcbiAgZ2VvbV9lcnJvcmJhcmgoYWVzKFxuICAgIHhtaW4gPSBjaS5sb3csXG4gICAgeG1heCA9IGNpLmhpZ2hcbiAgKSxcbiAgaGVpZ2h0ID0gMC4xLCBzaG93LmxlZ2VuZCA9IEZBTFNFXG4gICkgK1xuICBnZW9tX3BvaW50KGFlcyhzaGFwZSA9IHBhcmFtZXRlciksXG4gICAgZmlsbCA9IFwibWVkaXVtYXF1YW1hcmluZVwiLCBjb2xvciA9IFwibWVkaXVtYXF1YW1hcmluZVwiLCBzaXplID0gMi4yLFxuICAgIHNob3cubGVnZW5kID0gRkFMU0VcbiAgKSArXG4gIHNjYWxlX3hfY29udGludW91cyhcbiAgICBsaW1pdHMgPSBjKDAsIDAuNCksXG4gICAgYnJlYWtzID0gYygwLCAwLjMpLFxuICAgIG5hbWUgPSBcIkVmZmVjdCBzaXplXCJcbiAgKSArXG4gIGdlb21fdmxpbmUoXG4gICAgeGludGVyY2VwdCA9IDAsXG4gICAgY29sb3IgPSBcImJsYWNrXCIsXG4gICAgbGluZXR5cGUgPSBcImRhc2hlZFwiXG4gICkgK1xuICBmYWNldF9ncmlkKFxuICAgIGNvbHMgPSB2YXJzKHBhcmFtZXRlciksIHJvd3MgPSB2YXJzKGxhYmVsKSxcbiAgICBsYWJlbGxlciA9IGxhYmVsX3dyYXBfZ2VuKHdpZHRoID0gMjMpLFxuICAgIHNjYWxlcyA9IFwiZnJlZVwiLFxuICAgIHNwYWNlID0gXCJmcmVlXCJcbiAgKSArXG4gIHRoZW1lX2J3KCkgK1xuICB0aGVtZShcbiAgICBzdHJpcC50ZXh0LnkgPSBlbGVtZW50X3RleHQoYW5nbGUgPSAyNzAsIHNpemUgPSAxMCwgbWFyZ2luID0gbWFyZ2luKHQgPSAxNSwgciA9IDE1LCBiID0gMTUsIGwgPSAxNSkpLFxuICAgIHN0cmlwLnRleHQueCA9IGVsZW1lbnRfdGV4dChzaXplID0gMTIpLFxuICAgIHN0cmlwLmJhY2tncm91bmQgPSBlbGVtZW50X3JlY3QoY29sb3VyID0gTlVMTCwgbGluZXR5cGUgPSBcImJsYW5rXCIsIGZpbGwgPSBcImdyYXk5MFwiKSxcbiAgICB0ZXh0ID0gZWxlbWVudF90ZXh0KHNpemUgPSAxNCksXG4gICAgcGFuZWwuc3BhY2luZyA9IHVuaXQoMC41LCBcImxpbmVzXCIpLFxuICAgIHBhbmVsLmJvcmRlciA9IGVsZW1lbnRfYmxhbmsoKSxcbiAgICBheGlzLmxpbmUgPSBlbGVtZW50X2xpbmUoKSxcbiAgICBwYW5lbC5ncmlkLm1ham9yLnggPSBlbGVtZW50X2xpbmUobGluZXR5cGUgPSBcInNvbGlkXCIsIGNvbG91ciA9IFwiZ3JheTk1XCIpLFxuICAgIHBhbmVsLmdyaWQubWFqb3IueSA9IGVsZW1lbnRfbGluZShsaW5ldHlwZSA9IFwic29saWRcIiwgY29sb3IgPSBcImdyYXk5NVwiKSxcbiAgICBwYW5lbC5ncmlkLm1pbm9yLnkgPSBlbGVtZW50X2JsYW5rKCksXG4gICAgcGFuZWwuZ3JpZC5taW5vci54ID0gZWxlbWVudF9ibGFuaygpLFxuICAgIGxlZ2VuZC50aXRsZSA9IGVsZW1lbnRfYmxhbmsoKSxcbiAgICBheGlzLnRpdGxlLnggPSBlbGVtZW50X2JsYW5rKCksXG4gICAgYXhpcy50aXRsZS55ID0gZWxlbWVudF9ibGFuaygpXG4gIClcblxuIyBNZXRhbWV0YV9GaWdTMl9tYWxlLnNpZ1xuYGBgIn0= --> <pre class="r"><code>Metameta_FigS2_male.sig <- overall4.male.sigS %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "mediumaquamarine", color = "mediumaquamarine", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(0, 0.4), breaks = c(0, 0.3), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank() ) # Metameta_FigS2_male.sig</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="plot-fig-s2-plots-combined" class="section level4"> <h4>Plot Fig S2: plots combined</h4> <p>Metameta_FigS2_female.sig</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuRmlnUzIgPC0gZ2dhcnJhbmdlKG1hbGViaWFzX0ZpZ1MyX3NpZ3RyYWl0cywgbWFsZWJpYXNfRmlnMl9vdmVyMTAsIEZpZ1MyYiwgRmlnUzJkLCBuY29sID0gMSwgbnJvdyA9IDQsIGhlaWdodHMgPSBjKDIuMywgMiwgMi4xLCAyKSwgbGFiZWxzID0gYyhcIkFcIiwgXCIgXCIsIFwiQlwiLCBcIiBcIikpXG5cbmBgYCJ9 --> <pre class="r"><code>FigS2 <- ggarrange(malebias_FigS2_sigtraits, malebias_Fig2_over10, FigS2b, FigS2d, ncol = 1, nrow = 4, heights = c(2.3, 2, 2.1, 2), labels = c("A", " ", "B", " ")) </code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiUmVtb3ZlZCAyIHJvd3MgY29udGFpbmluZyBtaXNzaW5nIHZhbHVlcyAocG9zaXRpb25fc3RhY2spLlJlbW92ZWQgOCByb3dzIGNvbnRhaW5pbmcgbWlzc2luZyB2YWx1ZXMgKHBvc2l0aW9uX3N0YWNrKS5cbiJ9 --> <pre><code>Removed 2 rows containing missing values (position_stack).Removed 8 rows containing missing values (position_stack).</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuRmlnUzJcbmBgYCJ9 --> <pre class="r"><code>FigS2</code></pre> <!-- rnb-source-end --> <!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbXSwiaGVpZ2h0Ijo0MzIuNjMyOSwic2l6ZV9iZWhhdmlvciI6MCwid2lkdGgiOjcwMH0= --> <p><img src="data:image/png;base64,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" /></p> <!-- rnb-plot-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> </div> <div id="not-sure-what-this-below-is-felix-added-1122020-ich-glaube-dass-das-von-vorher-war.-wenn-wir-alles-oben-haben-dann-unten-loeschen" class="section level2"> <h2>NOT SURE WHAT THIS BELOW IS?? Felix added 11/2/2020: Ich glaube, dass das von vorher war. wenn wir alles oben haben, dann unten loeschen</h2> </div> <div id="figure-s2-sex-bias-including-vr" class="section level2"> <h2>Figure S2: sex-bias, including VR</h2> <p>Prepare data for traits with effect size ratios > 10% larger in males</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>meta.plotS2.over10 <- meta_clean %>% select(lnCVR, lnVR, lnRR, GroupingTerm) %>% arrange(GroupingTerm) meta.plotS2.over10.b <- gather(meta.plotS2.over10, trait, value, c(lnCVR, lnVR, lnRR)) meta.plotS2.over10.b$trait <- factor(meta.plotS2.over10.b$trait, levels = c("lnCVR", "lnVR", "lnRR")) meta.plotS2.over10.c <- meta.plotS2.over10.b %>% group_by_at(vars(trait, GroupingTerm)) %>% summarise( malebias = sum(value > log(11 / 10)), femalebias = sum(value < log(9 / 10)), total = malebias + femalebias, malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total ) meta.plotS2.over10.c$label <- "Sex difference in m/f ratios > 10%" # restructure to create stacked bar plots meta.plotS2.over10.c <- as.data.frame(meta.plotS2.over10.c) meta.plotS2.over10.d <- gather(meta.plotS2.over10.c, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE) # create new sample size variable meta.plotS2.over10.d$samplesize <- with(meta.plotS2.over10.d, ifelse(sex == "malepercent", malebias, femalebias)) # *Plot FigS2 Sex difference in m/f ratio > 10% malebias_FigS2_over10 <- ggplot(meta.plotS2.over10.d) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text( data = subset(meta.plot2.over10.d, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5), color = "white", size = 3.5 ) + facet_grid( cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18), scales = "free", space = "free" ) + scale_fill_brewer(palette = "Set2") + theme_bw(base_size = 18) + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_blank(), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip() # malebias_FigS2_over10 #(Panel B in Fig S2 in ms)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>#Metameta_FigS2_male.sig (Figure S2B top right panel)</p> <p>Restructure MALE data for plotting</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>overall3.male.sigS <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3.male.sigS %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3.male.sigS %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.male.sigS %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.male.sigS <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.male.sigS$label <- "CI not overlapping zero"</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Plot FigS2 all significant results (CI not overlapping zero, male )</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbWzEsIlJlbW92ZWQgNSByb3dzIGNvbnRhaW5pbmcgbWlzc2luZyB2YWx1ZXMgKGdlb21fZXJyb3JiYXJoKS4iXSxbMSwiUmVtb3ZlZCA1IHJvd3MgY29udGFpbmluZyBtaXNzaW5nIHZhbHVlcyAoZ2VvbV9wb2ludCkuIl1dLCJoZWlnaHQiOjQzMi42MzI5LCJzaXplX2JlaGF2aW9yIjowLCJ3aWR0aCI6NzAwfQ== --> <p><img 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" /></p> <!-- rnb-plot-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <div id="perc-sex-difference-male-bias" class="section level3"> <h3>10 % Perc sex difference, male bias</h3> <p>Restructure data for plotting : Male biased, 10% difference</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>overall3S.perc <- gather(overall.male.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) # lnVR, lnCVR.ci <- overall3S.perc %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3S.perc %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3S.perc %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4S.male.perc <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # lnVR.ci, overall4S.male.perc$label <- "Sex difference in m/f ratios > 10%" overall4S.male.perc$value <- as.numeric(overall4S.male.perc$value) overall4S.male.perc$ci.low <- as.numeric(overall4S.male.perc$ci.low) overall4S.male.perc$ci.high <- as.numeric(overall4S.male.perc$ci.high)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Plot FigS2 all >10% difference (male bias)</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>Metameta_FigS2_male.perc <- overall4S.male.perc %>% # filter(., GroupingTerm != "Hearing") %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes( shape = parameter, fill = parameter ), color = "mediumaquamarine", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(-0.2, 0.62), breaks = c(0, 0.3), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_blank(), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = 0.5, size = 14), axis.title.y = element_blank() ) # Metameta_FigS2_male.perc (Figure 5D right panel)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Restructure data for plotting: Female bias, 10 percent difference, including VR</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>overall3S.perc <- gather(overall.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) # lnVR, lnCVR.ci <- overall3S.perc %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3S.perc %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3S.perc %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4S.perc <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4S.perc$label <- "Sex difference in m/f ratios > 10%" overall4S.perc$value <- as.numeric(overall4S.perc$value) overall4S.perc$ci.low <- as.numeric(overall4S.perc$ci.low) overall4S.perc$ci.high <- as.numeric(overall4S.perc$ci.high)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Plot Fig5D all >10% difference (female)</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>Metameta_Fig3S_female.perc <- overall4S.perc %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "salmon1", color = "salmon1", size = 2.2, show.legend = FALSE ) + # scale_shape_manual(values = scale_x_continuous( limits = c(-0.53, 0.2), breaks = c(-0.3, 0), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), # rows = vars(label), # labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_blank(), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = 0.5, size = 14), axis.title.y = element_blank() ) # Metameta_Fig3S_female.perc (Figure 5D left panel)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Figure S2</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbXSwiaGVpZ2h0Ijo0MzIuNjMyOSwic2l6ZV9iZWhhdmlvciI6MCwid2lkdGgiOjcwMH0= --> <p><img src="data:image/png;base64,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" /></p> <!-- rnb-plot-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> <div id="acknowledgements" class="section level2"> <h2>Acknowledgements</h2> <p>tbd</p> </div> <div id="r-session-information" class="section level2"> <h2>R Session Information</h2> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuc2Vzc2lvbkluZm8oKVxuYGBgIn0= --> <pre class="r"><code>sessionInfo()</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <!-- rnb-text-end --> </div> </div> <div id="rmd-source-code">---
title: "IMPC Mouse data - Variance in sex differences"
author: "Susanne Zajitschek,  Felix Zajitschek, Russell Bonduriansky,Robert  Brooks, Will Cornwell, Daniel Falster, Malgortaza Lagisz, Jeremy Mason, Daniel Noble, Alistair Senior & Shinichi Nakagawa"
date: "August 2019"
output:
  html_document:
    code_download: true
    code_folding: hide
    depth: 4
    number_sections: no
    theme:  flatly
    toc: yes
    toc_depth: 4
    toc_float: yes
  html_notebook:
    toc: yes
  pdf_document:
    toc: yes
    toc_depth: '4'
subtitle: Electronic Supplementary Material
---

# Set-up

## Loading packages & custom functions

```{r, include=FALSE}
knitr::opts_chunk$set(
  echo = TRUE,
  warning = FALSE,
  message = FALSE,
  cache = TRUE,
  tidy = TRUE
)
```

```{r}
library(readr)
library(dplyr)
library(metafor)
library(devtools)
library(purrr)
library(tidyverse)
library(tidyr)
library(tibble)
library(kableExtra)
library(robumeta)
library(ggpubr)
library(ggplot2)
library(here)
```

## Functions 
for preparing the data for meta analyses

### 1) Subsetting data
Create function for sub-setting the data to choose only one data point per individual per trait: "data_subset_parameterid_individual_by_age"

```{r}
data_subset_parameterid_individual_by_age <- function(mydata, parameter, age_min=0, age_center=100) {
  tmp <- mydata %>%
    filter(
      age_in_days >= age_min,
      id == parameter
    ) %>%
    # take results for single individual closest to age_center
    mutate(age_diff = abs(age_center - age_in_days)) %>%
    group_by(biological_sample_id) %>%
    filter(age_diff == min(age_diff)) %>%
    select(-age_diff)# %>% 
#    filter(!duplicated(biological_sample_id))  #Felix 6/2/2020: this line can be deleted
    
  # still some individuals with multiple records (because same individual appear under different procedures, so filter to one record)
  j <- match(unique(tmp$biological_sample_id), tmp$biological_sample_id)
  tmp[j, ] 
  }
```

### 2) "Population statistics"
Create function called: "calculate_population_stats"
This function groups animals from the same strain and same insitiution together. This is done for each trait seoarately, and only for traits that have been measured in both sexes. Any group containing fewer than 5 individuals is excluded.

```{r}
calculate_population_stats <- function(mydata, min_individuals = 5) {
  mydata %>%
    group_by(population, strain_name, production_center, sex) %>%
    summarise(
      trait = parameter_name[1],
      x_bar = mean(data_point),
      x_sd = sd(data_point),
      n_ind = n()
    ) %>%
    ungroup() %>%
    filter(n_ind > min_individuals) %>%
    # Check both sexes present & filter those missing
    group_by(population) %>%
    mutate(
      n_sex = n_distinct(sex)
    ) %>%
    ungroup() %>%
    filter(n_sex == 2) %>%
    select(-n_sex) %>%
    arrange(production_center, strain_name, population, sex)
}
```

### 3) Extraction of effect sizes and sample variances
Function: "create_meta_analysis_effect_sizes"

```{r}
create_meta_analysis_effect_sizes <- function(mydata) {
  i <- seq(1, nrow(mydata), by = 2)
  input <- data.frame(
    n1i = mydata$n_ind[i],
    n2i = mydata$n_ind[i + 1],
    x1i = mydata$x_bar[i],
    x2i = mydata$x_bar[i + 1],
    sd1i = mydata$x_sd[i],
    sd2i = mydata$x_sd[i + 1]
  )

  mydata[i, ] %>%
    select(strain_name, production_center, trait) %>%
    mutate(
      effect_size_CVR = calculate_lnCVR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i),
      sample_variance_CVR = calculate_var_lnCVR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i),
      effect_size_VR = calculate_lnVR(CSD = input$sd1i, CN = input$n1i, ESD = input$sd2i, EN = input$n2i),
      sample_variance_VR = calculate_var_lnVR(CN = input$n1i, EN = input$n2i),
      effect_size_RR = calculate_lnRR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i),
      sample_variance_RR = calculate_var_lnRR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i),
      err = as.factor(seq_len(n()))
    )
}
```

 
### 4) Calculate meta-analysis statistics

Based on a function created by A M Senior @ the University of Otago NZ 03/01/2014: 

* Calculates effect sizes for meta-analysis of variance.  All functions take the mean, sd and n from the control and experimental groups.
* The first function, calculate_lnCVR, calculates the the log response-ratio of the coefficient of variance (lnCVR) - see Nakagawa et al 2015.
* The second function calculates the measurement error variance for lnCVR. As well as the aforementioned parameters, this function also takes Equal_E_C_Corr (default = T), which must be True or False. If true, the function assumes that the correlation between mean and sd (Taylor's Law)  is equal for the mean and control groups, and, thus these data are pooled. If False the mean-SD correlation for the experimental and control groups are calculated separately from one another.
* Similar functions are then implemented for lnVR (for comparison of standard deviations) and ln RR  (for comparison of means) 
 
```{r}

calculate_lnCVR <- function(CMean, CSD, CN, EMean, ESD, EN) {
  log(ESD) - log(EMean) + 1 / (2 * (EN - 1)) - (log(CSD) - log(CMean) + 1 / (2 * (CN - 1)))
}

calculate_var_lnCVR <- function(CMean, CSD, CN, EMean, ESD, EN, Equal_E_C_Corr = T) {
  if (Equal_E_C_Corr == T) {
    mvcorr <- 0 # cor.test(log(c(CMean, EMean)), log(c(CSD, ESD)))$estimate   old, slightly incorrect
    S2 <- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * mvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * mvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1))))
  }
  else {
    Cmvcorr <- cor.test(log(CMean), log(CSD))$estimate
    Emvcorr <- cor.test(log(EMean), (ESD))$estimate
    S2 <- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * Cmvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * Emvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1))))
  }
  S2
}

calculate_lnVR <- function(CSD, CN, ESD, EN) {
  log(ESD) - log(CSD) + 1 / (2 * (EN - 1)) - 1 / (2 * (CN - 1))
}

calculate_var_lnVR <- function(CN, EN) {
  1 / (2 * (EN - 1)) + 1 / (2 * (CN - 1))
}

calculate_lnRR <- function(CMean, CSD, CN, EMean, ESD, EN) {
  log(EMean) - log(CMean)
}

calculate_var_lnRR <- function(CMean, CSD, CN, EMean, ESD, EN) {
  CSD^2 / (CN * CMean^2) + ESD^2 / (EN * EMean^2)
}
```

## Load & clean data

### 1) Data loading and cleaning of the csv file

This step we have already done and provide a cleaned up file which is less computing intensive and which we have saved in a folder called `export`. However, the cvs is provided in case this is preferred to be attempted, following the steps below:

```{r clean, eval=FALSE, include=TRUE}
# loads the raw data, setting some default types for various columns

load_raw <- function(filename) {
  read_csv(filename,
    col_types = cols(
      .default = col_character(),
      project_id = col_character(),
      id = col_character(),
      parameter_id = col_character(),
      age_in_days = col_integer(),
      date_of_experiment = col_datetime(format = ""),
      weight = col_double(),
      phenotyping_center_id = col_character(),
      production_center_id = col_character(),
      weight_date = col_datetime(format = ""),
      date_of_birth = col_datetime(format = ""),
      procedure_id = col_character(),
      pipeline_id = col_character(),
      biological_sample_id = col_character(),
      biological_model_id = col_character(),
      weight_days_old = col_integer(),
      datasource_id = col_character(),
      experiment_id = col_character(),
      data_point = col_double(),
      age_in_weeks = col_integer(),
      `_version_` = col_character()
    )
  )
}

# Apply some standard cleaning to the data
clean_raw_data <- function(mydata) {
  
  group <- read_csv(here("data", "ParameterGrouping.csv"))
  
  tmp <- 
    mydata %>%

    # Filter to IMPC source (recommend by Jeremey in email to Susi on 20 Aug 2018)
    filter(datasource_name == "IMPC") %>%

    # standardise trait names
    mutate(parameter_name = tolower(parameter_name)) %>%

    # remove extreme ages
    filter(age_in_days > 0 & age_in_days < 500) %>%

    # remove NAs
    filter(!is.na(data_point)) %>%

    # subset to reasonable set of variables, date_of_experiment used as an indicator of batch-level effects
    select(production_center, strain_name, strain_accession_id, biological_sample_id, pipeline_stable_id, procedure_group, procedure_name, sex, date_of_experiment, age_in_days, weight, parameter_name, data_point) %>% 

    # sort
    arrange(production_center, biological_sample_id, age_in_days)
      
    # filter to groups with > 1 centre  
    merge(tmp, 
          tmp %>% group_by(parameter_name) %>%
    summarise(center_per_trait = length(unique(production_center, na.rm = TRUE)))
        )%>%
    filter(center_per_trait >= 2) %>% 

    # Define population variable
    mutate(population = sprintf("%s-%s", production_center, strain_name)) %>% 

    # add grouping variable: these were decided based on functional groups and procedures 
    mutate(parameter_group = group$parameter[match(parameter_name, group$parameter_name)] ) %>%
    
    # Assign unique IDs (per trait)
    # each unique parameter_name (=trait,use trait variable) gets a unique number ('id')

    # We add a new variable, where redundant traits are combined
    #[note however, at this stage the dataset still contains nonsensical traits, i.e. traits that may not contain any information on variance]
    mutate(id = match(parameter_name, unique(parameter_name))) %>% 
    as_tibble()
}

# Load raw data - save cleaned dataset as RDS for reuse
data_raw <- load_raw(here("data","dr7.0_all_control_data.csv.gz"))
dir.create("export", F, F)

data <- data_raw %>% 
  clean_raw_data() 
saveRDS(data, "export/data_clean.rds")
```

For analysis we load the RDS created above and other datasets:

```{r load}
data <- readRDS(here("export", "data_clean.rds")) 

procedures <- read_csv(here("data", "procedures.csv"))

```


Checking length of different variables and sample sizes.

### Table 1:  "Strains and Center Sample Sizes"
This table summarises the available numbers of male and female mice from each strain and originating institution.

```{r echo = FALSE, results = 'hold'}
#length(unique(data$parameter_name)) # 232 traits
#length(unique(data$parameter_group)) # 161 parameter groups
#length(unique(data$procedure_name)) # 26 procedure groups
#length(unique(data$biological_sample_id)) # 27147 individial mice   

#number of males and females per strain per production center 
kable(cbind(data %>% group_by(production_center, strain_name) %>% count(biological_sample_id, sex) %>% count(sex) %>% print(n = Inf))) %>%
  kable_styling() %>%
  scroll_box(width = "70%", height = "200px")

## SZ: I don't understand why the tibble is showing in the knitted html
```


# Meta-analyses
## 1. Population as analysis unit 
(Step C, Figure 3 in main document)

### Loop: Meta-analyses on all traits

* The loop combines the functions mentioned above and fills the data matrix with results from our meta analysis. 
* Error messages indicate traits that either did not reach convergence, or that did not return meaningful results in the meta-analysis, due to absence of variance. Those traits will be removed in later steps, outlined below.

```{r}

n <- length(unique(data$id))

# Create dataframe to store results
results_alltraits_grouping <- 
    tibble(id = 1:n, lnCVR=0, lnCVR_lower=0, lnCVR_upper=0, 
           lnCVR_se=0, lnVR=0, lnVR_lower=0, lnVR_upper=0, 
           lnVR_se=0, lnRR=0, lnRR_lower=0, lnRR_upper=0, lnRR_se=0, sampleSize=0, trait=0)

for (t in 1:n) {
  tryCatch(
    {
      results <- data %>% 
        data_subset_parameterid_individual_by_age(t) %>%
        calculate_population_stats() %>%
        create_meta_analysis_effect_sizes()

      # lnCVR,  log repsonse-ratio of the coefficient of variance
      cvr <- metafor::rma.mv(yi = effect_size_CVR, V = sample_variance_CVR, 
                             random = list(~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err), 
                             control = list(optimizer = "optim", optmethod = "Nelder-Mead", 
                                            maxit = 1000), verbose = F, data = results)

      # lnVR, comparison of standard deviations
      cv <- metafor::rma.mv(yi = effect_size_VR, V = sample_variance_VR,
                            random = list(~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err), 
                            control = list(optimizer = "optim", optmethod = "Nelder-Mead", 
                                           maxit = 1000), verbose = F, data = results)

      # for means, lnRR
      means <- metafor::rma.mv(yi = effect_size_RR, V = sample_variance_RR, 
                               random = list(~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err), 
                               control = list(optimizer = "optim", optmethod = "Nelder-Mead", 
                                              maxit = 1000), verbose = F, data = results)
      
      f <- function(x) unlist(x[c("b", "ci.lb", "ci.ub", "se")])

      results_alltraits_grouping[t, 2:14] <- c(f(cvr), f(cv), f(means), means$k)
      results_alltraits_grouping[t, 15] <- unique(results$trait)
    },
    error = function(e) {
      cat("ERROR :", t, conditionMessage(e), "\n")
    }
  )
}
```

In the above function, we use 'tryCatch' and 'conditionMessage' to prevent the loop from aborting when the first error at row 84 is produced.
As convergence in the two listed non-converging cases can't be achieved by sensibly tweaking (other optim etc.), and we only learn about non-convergence in the loop, it is not possible to exclude the traits (N=2) beforehand.
Similarly, there are 8 traits with very low variation, which can not be excluded prior to running the loop.

The produced "Warnings" indicate cases where variance components are set to zero during likelihood optimization.


### Merging datasets & removal of non-converged traits

Procedure names, grouping variables and trait names ("parameter_names") are merged back together with the results from the metafor analysis above.
 
```{r}
results_alltraits_grouping2 <- 
  results_alltraits_grouping %>% 
  left_join(by="id",
             data %>% select(id, parameter_group, procedure = procedure_name, procedure_name, parameter_name) %>%   # We filter duplicated id's to get only one unique row per id (and there is one id per parameter_name)
              filter(!duplicated(id))
            ) %>%
  # Below we add 'procedure' (from the previously loaded 'procedures.csv') as a variable
  left_join(by="procedure", 
            procedures %>% distinct()
            )

#(n <- length(unique(results_alltraits_grouping2$parameter_name))) # 232
```

### Removal of traits 

14 traits from the originally 232 that had been included are removed because they either did not achieve convergence or are nonsensical for analysis of variance (such as traits that show no variation, see list below). 

Not converged: "dp t cells", "mzb (cd21/35 high)"

Not enough variation: "number of caudal vertebrae", "number of cervical vertebrae", "number of digits", "number of lumbar vertebrae", "number of pelvic vertebrae", "number of ribs left","number of ribs right", "number of signals", "number of thoracic vertebrae", "total number of acquired events in panel a","total number of acquired events in panel b", "whole arena permanence".


```{r}
# We exclude 14 parameter names for which metafor models didn't converge ("dp t cells", "mzb (cd21/35 high)"), and of parameters that don't harbour enough variation
meta_clean <- results_alltraits_grouping2 %>% 
	  filter(!parameter_name %in% c("dp t cells", "mzb (cd21/35 high)", "number of caudal vertebrae", 
	  "number of cervical vertebrae", "number of digits", "number of lumbar vertebrae", "number of pelvic vertebrae", "number of ribs left",                       
        "number of ribs right", "number of signals", "number of thoracic vertebrae", "total number of acquired events in panel a",
        "total number of acquired events in panel b", "whole arena permanence"))

```
 

## 2. Meta-analysis: condensing non-independent traits 
(Step F in Figure 3 in main article)

### Dealing with Correlated Parameters, preparation

This dataset contained a number of highly correlated traits, such as different kinds of cell counts (for example hierarchical parameterization within immunological assays). As those data-points are not independent of each other,  we conducted meta analyses on these correlated parameters to collapse the number of levels.

#### Collapsing and merging correlated parameters

Here we double check numbers of trait parameters in the dataset

```{r}

meta1 <- meta_clean 
length(unique(meta1$procedure)) #18
length(unique(meta1$GroupingTerm)) #9
length(unique(meta1$parameter_group)) # 148 levels. To be used as grouping factor for meta-meta-analysis / collapsing down based on things that are classified identically in "parameter_group" but have different "parameter_name"
length(unique(meta1$parameter_name)) #218
```

#### Count of number of parameter names (correlated sub-traits) in each parameter group (par_group_size) 

### Table 2: Numbers of correlated and uncorrelated traits
This serves to identify and separate the traits that are correlated from the full dataset that can be processed as is. If the sample size (n) for a given "parameter group" equals 1, the trait is unique and uncorrelated. All instances, where there are 2 or more traits associated with the same parameter group (90 cases), are selected for a "mini-meta analysis", which removes the issue of correlation.

```{r}
kable(cbind(meta1 %>% count(parameter_group))) %>%
  kable_styling() %>%
  scroll_box(width = "60%", height = "200px")
```

```{r}
meta1_sub <- meta1 %>%
  # Add summary of number of parameter names in each parameter group
  group_by(parameter_group) %>%
  mutate(par_group_size = length(unique(parameter_name)), 
         sampleSize = as.numeric(sampleSize)) %>% 
  ungroup() %>% 
  # Create subsets with > 1 count (par_group_size > 1)
  filter(par_group_size > 1) # 90 observations
```

#### Meta-analyses on correlated (sub-)traits, using robumeta` 
Here we pepare the subset of the data (using nest()), and in this first step the model of the meta-analysis effect sizes are calculated

```{r}

# Create summary of number of parameter names in each parameter group, and merge back together

meta1b <-
  meta1 %>%
  group_by(parameter_group) %>% 
  summarize(par_group_size = length(unique(parameter_name, na.rm = TRUE)))

meta1$par_group_size <- meta1b$par_group_size[match(meta1$parameter_group, meta1b$parameter_group)]

# Create subsets with > 1 count (par_group_size > 1) 

meta1_sub <- subset(meta1,par_group_size >1) # 90 observations   
meta1_sub$sampleSize <- as.numeric(meta1_sub$sampleSize)

# Nesting and meta-analyses on correlated traits, using robumeta

n_count <- meta1_sub %>%
  group_by(parameter_group) %>%
  mutate(raw_N = sum(sampleSize)) %>%
  nest() %>%
  ungroup()

model_count <- n_count %>%
  mutate(
    model_lnRR = map(data, ~ robu(.x$lnRR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnRR_se)^2)),
    model_lnVR = map(data, ~ robu(.x$lnVR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnVR_se)^2)),
    model_lnCVR = map(data, ~ robu(.x$lnCVR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnCVR_se)^2))
  )
```

#### Extracting and save parameter estimates
Here we apply an additional Function to collect the outcomes of the 'mini-meta-analysis' that has ondensed our non-independent traits.

```{r}
count_fun <- function(mod_sub) {
  return(c(mod_sub$reg_table$b.r, mod_sub$reg_table$CI.L, mod_sub$reg_table$CI.U, mod_sub$reg_table$SE))
} # estimate, lower ci, upper ci, SE
```

Extraction of values created during meta-analyses using robumeta

```{r}
robusub_RR <- model_count %>%
  transmute(parameter_group, estimatelnRR = map(model_lnRR, count_fun)) %>%
  mutate(r = map(estimatelnRR, ~ data.frame(t(.)))) %>%
  unnest(r) %>%
  select(-estimatelnRR) %>%
  purrr::set_names(c("parameter_group", "lnRR", "lnRR_lower", "lnRR_upper", "lnRR_se"))

robusub_CVR <- model_count %>%
  transmute(parameter_group, estimatelnCVR = map(model_lnCVR, count_fun)) %>%
  mutate(r = map(estimatelnCVR, ~ data.frame(t(.)))) %>%
  unnest(r) %>%
  select(-estimatelnCVR) %>%
  purrr::set_names(c("parameter_group", "lnCVR", "lnCVR_lower", "lnCVR_upper", "lnCVR_se"))

robusub_VR <- model_count %>%
  transmute(parameter_group, estimatelnVR = map(model_lnVR, count_fun)) %>%
  mutate(r = map(estimatelnVR, ~ data.frame(t(.)))) %>%
  unnest(r) %>%
  select(-estimatelnVR) %>%
  purrr::set_names(c("parameter_group", "lnVR", "lnVR_lower", "lnVR_upper", "lnVR_se"))

robu_all <- full_join(robusub_CVR, robusub_VR) %>% full_join(., robusub_RR)
```

#### Combining data 
Merge the two data sets (the new [robu_all] and the initial [uncorrelated sub-traits with count = 1]) 

```{r}
meta_all <- meta1 %>%
  filter(par_group_size == 1) %>%
  as_tibble()
# glimpse(meta_all)
# glimpse(robu_all)

# Step 1:  Columns are matched by name (in our case, 'parameter_group'), and any missing columns will be filled with NA
combinedmeta <- bind_rows(robu_all, meta_all)
# glimpse(combinedmeta)

# Steps 2&3: Add information about number of traits in a parameter group, procedure, and grouping term
metacombo <- combinedmeta
metacombo$counts <- meta1$par_group_size[match(metacombo$parameter_group, meta1$parameter_group)] 
metacombo$procedure2 <- meta1$procedure[match(metacombo$parameter_group, meta1$parameter_group)]
metacombo$GroupingTerm2 <- meta1$GroupingTerm[match(metacombo$parameter_group, meta1$parameter_group)]

```

Clean-up, reorder, and rename 

```{r}
metacombo <- metacombo[c("parameter_group", "counts","procedure2","GroupingTerm2", "lnCVR","lnCVR_lower","lnCVR_upper","lnCVR_se","lnVR","lnVR_lower","lnVR_upper","lnVR_se","lnRR","lnRR_lower","lnRR_upper","lnRR_se")] 

names(metacombo)[names(metacombo)=="procedure2"] <- "procedure" 
names(metacombo)[names(metacombo)=="GroupingTerm2"] <- "GroupingTerm" 

# Quick pre-check before doing plots
metacombo %>%
  group_by(GroupingTerm) %>%
  dplyr::summarize(MeanCVR = mean(lnCVR), MeanVR = mean(lnVR), MeanRR = mean(lnRR))
```

### Table 3: Full corrected dataset

We use this corrected (for correlated traits) "results" table, which contains each of the meta-analytic means for all effect sizes of interest, for further analyses.  We further use this table as part of the Shiny App, which is able to provide the percentage differences between males and females for mean, variance and coefficient of variance. 

This is the full result dataset
```{r}
kable(metacombo) %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "200px")

# trait_meta_results <- write.csv(metacombo, file = "export/trait_meta_results.csv")  #Felix 7/2/2020: I think this can be deleted for publication!
```

## 3. Second-order meta-analysis for functional groups
(Section H in Figure 3 in main article)

### Performing meta-analyses (3 for each of the 9 grouping terms: lnCVR, lnVR, lnRR) 

#### Preparation of data

Nesting, calculating the number of parameters within each grouping term, and running the meta-analyses

```{r}
metacombo_final <- metacombo %>%
  group_by(GroupingTerm) %>%
   nest()

# **calculate number of parameters per grouping term

metacombo_final <- metacombo_final %>% mutate(para_per_GroupingTerm = map_dbl(data, nrow))

# For all grouping terms
metacombo_final_all <- metacombo %>%
  nest(data = everything())


# **Final fixed effects meta-analyses within grouping terms, with SE of the estimate

overall1 <- metacombo_final %>%

  mutate(
    model_lnCVR = map(data, ~ metafor::rma.uni(
      yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
    )),
    model_lnVR = map(data, ~ metafor::rma.uni(
      yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
    )),
    model_lnRR = map(data, ~ metafor::rma.uni(
      yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
    ))
  )

# **Final fixed effects meta-analyses ACROSS grouping terms, with SE of the estimate

overall_all1 <- metacombo_final_all %>%

  mutate(
    model_lnCVR = map(data, ~ metafor::rma.uni(
      yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
    )),
    model_lnVR = map(data, ~ metafor::rma.uni(
      yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
    )),
    model_lnRR = map(data, ~ metafor::rma.uni(
      yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
    ))
  )
```

### Re-structuring the data for each grouping term
We here delete unused variables, and select the respective effect sizes. Please note - the referencing of the cells does NOT depend on previous ordering of the data. This would only be affected if the output structure from metafor::rma.uni changes. 

```{r}
Behaviour <- overall1 %>% 
  filter(., GroupingTerm == "Behaviour") %>% 
  mutate(
    lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
    lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
    lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
  )  %>%
  select(., GroupingTerm, lnCVR:lnRR_se)

Immunology <- overall1 %>% 
  filter(., GroupingTerm == "Immunology") %>% 
  mutate(
    lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
    lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
    lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
  )  %>%
  select(., GroupingTerm, lnCVR:lnRR_se)

Hematology <- overall1 %>% 
  filter(., GroupingTerm == "Hematology") %>% 
  mutate(
    lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
    lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
    lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
  )  %>%
  select(., GroupingTerm, lnCVR:lnRR_se)

Hearing <- overall1 %>% 
  filter(., GroupingTerm == "Hearing") %>% 
  mutate(
    lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
    lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
    lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
  )  %>%
  select(., GroupingTerm, lnCVR:lnRR_se)

Physiology <- overall1 %>% 
  filter(., GroupingTerm == "Physiology") %>% 
  mutate(
    lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
    lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
    lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
  )  %>%
  select(., GroupingTerm, lnCVR:lnRR_se)

Metabolism <- overall1 %>% 
  filter(., GroupingTerm == "Metabolism") %>% 
  mutate(
    lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
    lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
    lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
  )  %>%
  select(., GroupingTerm, lnCVR:lnRR_se)

Morphology <- overall1 %>% 
  filter(., GroupingTerm == "Morphology") %>%
  mutate(
    lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
    lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
    lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
  )  %>%
  select(., GroupingTerm, lnCVR:lnRR_se)
  
Heart <- overall1 %>% 
  filter(., GroupingTerm == "Heart") %>% 
  mutate(
    lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
    lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
    lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
  )  %>%
  select(., GroupingTerm, lnCVR:lnRR_se)

Eye <- overall1 %>% 
  filter(., GroupingTerm == "Eye") %>% 
  mutate(
    lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
    lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
    lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
  )  %>%
    select(., GroupingTerm, lnCVR:lnRR_se)

All <- overall_all1 %>% 
  mutate(
    lnCVR = .[[2]][[1]]$b, lnCVR_lower = .[[2]][[1]]$ci.lb, lnCVR_upper = .[[2]][[1]]$ci.ub, lnCVR_se = .[[2]][[1]]$se, 
    lnVR = .[[3]][[1]]$b, lnVR_lower = .[[3]][[1]]$ci.lb, lnVR_upper = .[[3]][[1]]$ci.ub, lnVR_se = .[[3]][[1]]$se,
    lnRR = .[[4]][[1]]$b, lnRR_lower = .[[4]][[1]]$ci.lb, lnRR_upper = .[[4]][[1]]$ci.ub, lnRR_se = .[[4]][[1]]$se
  )  %>%
    select(., lnCVR:lnRR_se)

All <- All %>% mutate(GroupingTerm = "All")

overall2 <- bind_rows(Behaviour, Morphology, Metabolism, Physiology, Immunology, Hematology, Heart, Hearing, Eye, All) 
```

# Visualisation
## Figure 4 
#### Preparation for plots: Count data, based on First-order metamanalysis results
This includes all separate eligible traits.
Re-ordering of grouping terms 

```{r}

meta_clean$GroupingTerm <- factor(meta_clean$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye"))
meta_clean$GroupingTerm <- factor(meta_clean$GroupingTerm, rev(levels(meta_clean$GroupingTerm)))

# *Preparing data for all traits

meta.plot2.all <- meta_clean %>%
  select(lnCVR, lnVR, lnRR, GroupingTerm) %>%
  arrange(GroupingTerm)

meta.plot2.all.b <- gather(meta.plot2.all, trait, value, c(lnCVR, lnRR)) # lnVR has been removed here and in the steps below, as this is only included in the supplemental figure

meta.plot2.all.b$trait <- factor(meta.plot2.all.b$trait, levels = c("lnCVR", "lnRR")) 
meta.plot2.all.c <- meta.plot2.all.b %>%
  group_by_at(vars(trait, GroupingTerm)) %>%
  summarise(
    malebias = sum(value > 0), femalebias = sum(value <= 0), total = malebias + femalebias,
    malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total
  )

meta.plot2.all.c$label <- "All traits"

# Re-structure to create stacked bar plots

meta.plot2.all.d <- as.data.frame(meta.plot2.all.c)
meta.plot2.all.e <- gather(meta.plot2.all.d, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE)

# Create new sample size variable

meta.plot2.all.e$samplesize <- with(meta.plot2.all.e, ifelse(sex == "malepercent", malebias, femalebias))

# Add summary row ('All') and re-arrange rows into correct order for plotting (warnings about coercing 'id' into character vector are ok)

meta.plot2.all.f <- meta.plot2.all.e %>% group_by(trait, sex) %>% 
	summarise(GroupingTerm = "All", malebias = sum(malebias), femalebias = sum(femalebias), total = malebias + femalebias, 
	label = "All traits", samplesize = sum(samplesize)) %>%
	mutate(percent = ifelse(sex == "femalepercent", femalebias*100/(malebias+femalebias), malebias*100/(malebias+femalebias))) %>%
	bind_rows(meta.plot2.all.e, .) %>%
	mutate(rownumber = row_number()) %>%
	.[c(37, 1:9, 39, 10:18, 38, 19:27, 40, 28:36), ] 
  #line references in previous code line corresponding to: 
  #'lnCVR(male(All)), lnCVR(male('single grouping terms'), lnRR(male(All)), lnRR(male('single grouping terms')),
  #lnCVR(female(All)), lnCVR(female('single grouping terms'), lnRR(female(All)), lnRR(female('single grouping terms'))'

meta.plot2.all.f$GroupingTerm <- factor(meta.plot2.all.f$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) 
meta.plot2.all.f$GroupingTerm <- factor(meta.plot2.all.f$GroupingTerm, rev(levels(meta.plot2.all.f$GroupingTerm)))

malebias_Fig2_alltraits <-
  ggplot(meta.plot2.all.f) +
  aes(x = GroupingTerm, y = percent, fill = sex) +
  geom_col() +
  geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") +
  geom_text(
    data = subset(meta.plot2.all.f, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5),
    color = "white", size = 3.5
  ) +
  facet_grid(
    cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18),
    scales = "free", space = "free"
  ) +
  scale_fill_brewer(palette = "Set2") +
  theme_bw(base_size = 18) +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.position = "none",
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  ) +
  coord_flip()

# malebias_Fig2_alltraits     #(panel A in Figure 4 in ms)
```


###  Overall results of second order meta-analysis (Figure 4, Panel B)
#### Re-structure data for plotting 
Data are re-structured, and grouping terms are being re-ordered

```{r}
overall3 <- gather(overall2, parameter, value, c(lnCVR, lnRR), factor_key = TRUE) 

lnCVR.ci <- overall3 %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3 %>%
  filter(parameter == "lnVR") %>%
  mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3 %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4 <- bind_rows(lnCVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) 

# Re-order grouping terms

overall4$GroupingTerm <- factor(overall4$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All"))
overall4$GroupingTerm <- factor(overall4$GroupingTerm, rev(levels(overall4$GroupingTerm)))
overall4$label <- "All traits"

kable(cbind(overall4, overall4)) %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "200px")
```

```{r}
Metameta_Fig3_alltraits <- overall4 %>%

  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "black",
    color = "black", size = 2.2,
    show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(-0.24, 0.25),
    breaks = c(-0.2, -0.1, 0, 0.1, 0.2),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), rows = vars(label),
    labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_text(hjust = 0.5, size = 14),
    axis.title.y = element_blank()
  )

# Metameta_Fig3_alltraits
```

### Fig 4 # SZ STILL TO DO
Join the different parts and  #TO DO!! add M / F symbols in Metameta_Fig3_alltraits 
```{r}
#Test
#male <- readPNG(system.file("img", "male"))
#test <- Metameta_Fig3_alltraits 

#library(png)
```

### Figure 4
Panel A shows the numbers of traits across functional groups that are either male-biased (blue-green) or female-biased (orange-red), as calculated in Step D (figure 3). Panel B shows effect sizes and 95% CI from separate meta-analysis for each functional group (step H in Figure 3). Both panels represent results evaluated across all traits (Phase 3, Figure 3). Traits that are male biased is shown in blue, whereas female bias data is represented in orange.


```{r}
Fig4 <- ggarrange(malebias_Fig2_alltraits, Metameta_Fig3_alltraits,  nrow = 2, align = "v", heights = c(1, 1), labels = c("A", "B"))
Fig4
```


## Figure 5
#### Preparing data for traits with CI not overlapping 0
To further investigate sex bias in this dataset, and in particular if the extent of sex bias differs between traits, we investigate the magnitude of male- and female bias in significantly different traits on (both for means and variability)

To do this, we select only traits that have CIs that do not overlap with zero.
The code below creates Figure 5A.
```{r}

meta.plot2.sig <- meta_clean %>%
  mutate(
    lnCVRsig = ifelse(lnCVR_lower * lnCVR_upper > 0, 1, 0), lnVRsig = ifelse(lnVR_lower * lnVR_upper > 0, 1, 0),
    lnRRsig = ifelse(lnRR_lower * lnRR_upper > 0, 1, 0)
  )

meta.plot2.sig.b <- meta.plot2.sig[, c("lnCVR", "lnRR", "lnCVRsig", "lnVRsig", "lnRRsig", "GroupingTerm")] 

meta.plot2.sig.c <- gather(meta.plot2.sig.b, trait, value, lnCVR:lnRR)
meta.plot2.sig.c$sig <- "placeholder"

meta.plot2.sig.c$trait <- factor(meta.plot2.sig.c$trait, levels = c("lnCVR", "lnRR")) 

meta.plot2.sig.c$sig <- ifelse(meta.plot2.sig.c$trait == "lnCVR", meta.plot2.sig.c$lnCVRsig,
  ifelse(meta.plot2.sig.c$trait == "lnVR", meta.plot2.sig.c$lnVRsig, meta.plot2.sig.c$lnRRsig)
)

# Choosing sex biased ln-ratios significantly larger than 0
meta.plot2.sig.malebias <- meta.plot2.sig.c %>%
  group_by_at(vars(trait, GroupingTerm)) %>%
  filter(sig == 1) %>%
  summarise(male_sig = sum(value > 0), female_sig = sum(value < 0), total = male_sig + female_sig)

meta.plot2.sig.malebias <- ungroup(meta.plot2.sig.malebias) %>%
  add_row(trait = "lnCVR", GroupingTerm = "Hearing", male_sig = 0, female_sig = 0, .before = 4) %>% # add "Hearing" for lnCVR (not filtered as only zeros)
  mutate(malepercent = male_sig * 100 / total, femalepercent = female_sig * 100 / total)

meta.plot2.sig.malebias$label <- "CI not overlapping zero"

# Re-structure to create stacked bar plots

meta.plot2.sig.bothsexes <- as.data.frame(meta.plot2.sig.malebias)
meta.plot2.sig.bothsexes.b <- gather(meta.plot2.sig.bothsexes, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE)

# create new sample size variable

meta.plot2.sig.bothsexes.b$samplesize <- with(meta.plot2.sig.bothsexes.b, ifelse(sex == "malepercent", male_sig, female_sig))

# Add summary row ('All') and re-arrange rows into correct order for plotting 

meta.plot2.sig.bothsexes.c <- meta.plot2.sig.bothsexes.b %>% group_by(trait, sex) %>% 
  summarise(GroupingTerm = "All", male_sig = sum(male_sig), female_sig = sum(female_sig), total = male_sig + female_sig, 
            label = "CI not overlapping zero", samplesize = sum(samplesize)) %>%
  mutate(percent = ifelse(sex == "femalepercent", female_sig*100/(male_sig+female_sig), male_sig*100/(male_sig+female_sig))) %>%
  bind_rows(meta.plot2.sig.bothsexes.b, .) %>%
  mutate(rownumber = row_number()) %>%
  .[c(37, 1:9, 39, 10:18, 38, 19:27, 40, 28:36), ] 
  #line references in previous code line corresponding to: 
  #'lnCVR(male(All)), lnCVR(male('single grouping terms'), lnRR(male(All)), lnRR(male('single grouping terms')),
  #lnCVR(female(All)), lnCVR(female('single grouping terms'), lnRR(female(All)), lnRR(female('single grouping terms'))'

meta.plot2.sig.bothsexes.c$GroupingTerm <- factor(meta.plot2.sig.bothsexes.c$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) 
meta.plot2.sig.bothsexes.c$GroupingTerm <- factor(meta.plot2.sig.bothsexes.c$GroupingTerm, rev(levels(meta.plot2.sig.bothsexes.c$GroupingTerm)))

# Plot Fig2 all significant results (CI not overlapping zero)
# Several grouping terms are added post-hoc (with no data to display): no significant lnCVR for 'Hearing' in either sex; no sig. male-biased lnCVR for 'Immunology' and 'Eye, and no significant female-biased lnRR for 'Eye'.

malebias_Fig2_sigtraits <-
  ggplot(meta.plot2.sig.bothsexes.c) +
  aes(x = GroupingTerm, y = percent, fill = sex) +
  geom_col() +
  geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") +
  geom_text(
    data = subset(meta.plot2.sig.bothsexes.c, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5),
    color = "white", size = 3.5
  ) +
  facet_grid(
    cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18),
    scales = "free", space = "free"
  ) +
  scale_fill_brewer(palette = "Set2") +
  theme_bw(base_size = 18) +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.position = "none",
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  ) +
  coord_flip()
```

### Preparation for Plots on significant sex-bias (Second-order meta analysis results)

#### Figure 5 B - traits with CI not overlapping 0 
Prepare data 
create column with 1= different from zero, 0= zero included in CI
#### Male-biased (significant) traits

```{r}
meta.male.plot3.sig <- metacombo %>%
  mutate(
    sigCVR = ifelse(lnCVR_lower > 0, 1, 0),
    sigVR = ifelse(lnVR_lower > 0, 1, 0),
    sigRR = ifelse(lnRR_lower > 0, 1, 0)
  )

# Significant subset for lnCVR
metacombo_male.plot3.CVR <- meta.male.plot3.sig %>%
  filter(sigCVR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_male.plot3.CVR.all <- meta.male.plot3.sig %>%
  filter(sigCVR == 1) %>%
  nest(data = everything())    #Felix added 'data = everything()' on 4/2/2020

# Significant subset for lnVR
metacombo_male.plot3.VR <- meta.male.plot3.sig %>%
  filter(sigVR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_male.plot3.VR.all <- meta.male.plot3.sig %>%
  filter(sigVR == 1) %>%
  nest(data = everything())    

# Significant subset for lnRR
metacombo_male.plot3.RR <- meta.male.plot3.sig %>%
  filter(sigRR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_male.plot3.RR.all <- meta.male.plot3.sig %>%
  filter(sigRR == 1) %>%
  nest(data = everything())   

# **Final fixed effects meta-analyses within grouping terms, with SE of the estimate

plot3.male.meta.CVR <- metacombo_male.plot3.CVR %>%
  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.VR <- metacombo_male.plot3.VR %>%
  mutate(model_lnVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.RR <- metacombo_male.plot3.RR %>%
  mutate(model_lnRR = map(data, ~ metafor::rma.uni(
    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

# Across all grouping terms #

plot3.male.meta.CVR.all <- metacombo_male.plot3.CVR.all %>%
  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.CVR.all <- plot3.male.meta.CVR.all %>% mutate(GroupingTerm = "All")

plot3.male.meta.VR.all <- metacombo_male.plot3.VR.all %>%
  mutate(model_lnVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.VR.all <- plot3.male.meta.VR.all %>% mutate(GroupingTerm = "All")

plot3.male.meta.RR.all <- metacombo_male.plot3.RR.all %>%
  mutate(model_lnRR = map(data, ~ metafor::rma.uni(
    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.RR.all <- plot3.male.meta.RR.all %>% mutate(GroupingTerm = "All")

# Combine with separate grouping term results

plot3.male.meta.CVR <- bind_rows(plot3.male.meta.CVR, plot3.male.meta.CVR.all)
plot3.male.meta.VR <- bind_rows(plot3.male.meta.VR, plot3.male.meta.VR.all)
plot3.male.meta.RR <- bind_rows(plot3.male.meta.RR, plot3.male.meta.RR.all)

# **Re-structure data for each grouping term; delete un-used variables

plot3.male.meta.CVR.b <- as.data.frame(plot3.male.meta.CVR %>% group_by(GroupingTerm) %>%
  mutate(
    lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)),
    lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3))
  ))[, c(1, 4:7)]
add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.CVR.b))

plot3.male.meta.CVR.b <- bind_rows(plot3.male.meta.CVR.b, add.row.hearing)
plot3.male.meta.CVR.b <- plot3.male.meta.CVR.b[order(plot3.male.meta.CVR.b$GroupingTerm), ]

plot3.male.meta.VR.b <- as.data.frame(plot3.male.meta.VR %>% group_by(GroupingTerm) %>%
  mutate(
    lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)),
    lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3))
  ))[, c(1, 4:7)]
plot3.male.meta.VR.b <- plot3.male.meta.VR.b[order(plot3.male.meta.VR.b$GroupingTerm), ]

plot3.male.meta.RR.b <- as.data.frame(plot3.male.meta.RR %>% group_by(GroupingTerm) %>%
  mutate(
    lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)),
    lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3))
  ))[, c(1, 4:7)]
plot3.male.meta.RR.b <- plot3.male.meta.RR.b[order(plot3.male.meta.RR.b$GroupingTerm), ]

overall.male.plot3 <- full_join(plot3.male.meta.CVR.b, plot3.male.meta.VR.b)
overall.male.plot3 <- full_join(overall.male.plot3, plot3.male.meta.RR.b)

overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All"))
overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, rev(levels(overall.male.plot3$GroupingTerm)))

overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All"))
overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, rev(levels(overall.male.plot3$GroupingTerm)))

# str(overall.male.plot3)
```


Restructure MALE data for plotting 

```{r}
overall3.male.sig <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnRR), factor_key = TRUE) 

lnCVR.ci <- overall3.male.sig %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
# lnVR.ci <- overall3.male.sig  %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3.male.sig %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4.male.sig <- bind_rows(lnCVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) 

overall4.male.sig$label <- "CI not overlapping zero"
```

Plot Fig 5B all significant results (CI not overlapping zero) for males. This is the right panel in Figure 5B.

```{r}

Metameta_Fig3_male.sig <- overall4.male.sig %>%
  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "mediumaquamarine", color = "mediumaquamarine", size = 2.2,
    show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(0, 0.4),
    breaks = c(0, 0.3),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), rows = vars(label),
    labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  )

# Metameta_Fig3_male.sig
```

#### Female part, significant traits
Female Fig5B sig

Prepare data for traits with CI not overlapping 0
create column with 1= different from zero, 0= zero included in CI

```{r}

# Female-biased traits

meta.female.plot3.sig <- metacombo %>%
  mutate(
    sigCVR = ifelse(lnCVR_upper < 0, 1, 0),
    sigVR = ifelse(lnVR_upper < 0, 1, 0),
    sigRR = ifelse(lnRR_upper < 0, 1, 0)
  )

# Significant subset for lnCVR

metacombo_female.plot3.CVR <- meta.female.plot3.sig %>%
  filter(sigCVR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_female.plot3.CVR.all <- meta.female.plot3.sig %>%
  filter(sigCVR == 1) %>%
  nest(data = everything())   

# Significant subset for lnVR

metacombo_female.plot3.VR <- meta.female.plot3.sig %>%
  filter(sigVR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_female.plot3.VR.all <- meta.female.plot3.sig %>%
  filter(sigVR == 1) %>%
  nest(data = everything())   

# Significant subset for lnRR

metacombo_female.plot3.RR <- meta.female.plot3.sig %>%
  filter(sigRR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

#Felix added 7/2/2020: metacombo_female.plot3.RR[4,2][[1]] [[1]]$lnRR_upper; #only two data points: -0.12377263 -0.01553462; should probably be excluded?!

metacombo_female.plot3.RR.all <- meta.female.plot3.sig %>%
  filter(sigRR == 1) %>%
  nest(data = everything())   

# **Final fixed effects meta-analyses within grouping terms, with SE of the estimate

plot3.female.meta.CVR <- metacombo_female.plot3.CVR %>%
  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.female.meta.VR <- metacombo_female.plot3.VR %>%
  mutate(model_lnVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.female.meta.RR <- metacombo_female.plot3.RR %>%
  mutate(model_lnRR = map(data, ~ metafor::rma.uni(
    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

# Across all grouping terms #

plot3.female.meta.CVR.all <- metacombo_female.plot3.CVR.all %>%
  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.female.meta.CVR.all <- plot3.female.meta.CVR.all %>% mutate(GroupingTerm = "All")

plot3.female.meta.VR.all <- metacombo_female.plot3.VR.all %>%
  mutate(model_lnVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.female.meta.VR.all <- plot3.female.meta.VR.all %>% mutate(GroupingTerm = "All")

plot3.female.meta.RR.all <- metacombo_female.plot3.RR.all %>%
  mutate(model_lnRR = map(data, ~ metafor::rma.uni(
    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.female.meta.RR.all <- plot3.female.meta.RR.all %>% mutate(GroupingTerm = "All")

# Combine with separate grouping term results

plot3.female.meta.CVR <- bind_rows(plot3.female.meta.CVR, plot3.female.meta.CVR.all)
plot3.female.meta.VR <- bind_rows(plot3.female.meta.VR, plot3.female.meta.VR.all)
plot3.female.meta.RR <- bind_rows(plot3.female.meta.RR, plot3.female.meta.RR.all)

# **Re-structure data for each grouping term; delete un-used variables

plot3.female.meta.CVR.b <- as.data.frame(plot3.female.meta.CVR %>% group_by(GroupingTerm) %>%
  mutate(
    lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)),
    lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3))
  ))[, c(1, 4:7)]

add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.female.meta.CVR.b))

plot3.female.meta.CVR.b <- bind_rows(plot3.female.meta.CVR.b, add.row.hearing)
plot3.female.meta.CVR.b <- plot3.female.meta.CVR.b[order(plot3.female.meta.CVR.b$GroupingTerm), ]

plot3.female.meta.VR.b <- as.data.frame(plot3.female.meta.VR %>% group_by(GroupingTerm) %>%
  mutate(
    lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)),
    lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3))
  ))[, c(1, 4:7)]

plot3.female.meta.VR.b <- plot3.female.meta.VR.b[order(plot3.female.meta.VR.b$GroupingTerm), ]

plot3.female.meta.RR.b <- as.data.frame(plot3.female.meta.RR %>% group_by(GroupingTerm) %>%
  mutate(
    lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)),
    lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3))
  ))[, c(1, 4:7)]

plot3.female.meta.RR.b <- plot3.female.meta.RR.b[order(plot3.female.meta.RR.b$GroupingTerm), ]

overall.female.plot3 <- full_join(plot3.female.meta.CVR.b, plot3.female.meta.VR.b)
overall.female.plot3 <- full_join(overall.female.plot3, plot3.female.meta.RR.b)

overall.female.plot3$GroupingTerm <- factor(overall.female.plot3$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All"))
overall.female.plot3$GroupingTerm <- factor(overall.female.plot3$GroupingTerm, rev(levels(overall.female.plot3$GroupingTerm)))

```

Re-structure data for plotting

```{r}
overall3.female.sig <- gather(overall.female.plot3, parameter, value, c(lnCVR, lnRR), factor_key = TRUE) 

lnCVR.ci <- overall3.female.sig %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
# lnVR.ci <- overall3.female.sig  %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3.female.sig %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4.female.sig <- bind_rows(lnCVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # lnVR.ci,

overall4.female.sig$label <- "CI not overlapping zero"

```

Plotting Fig5B all significant results (CI not overlapping zero, female )

```{r}

Metameta_Fig3_female.sig  <- overall4.female.sig %>%
  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "salmon1", color = "salmon1", size = 2.2,
    show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(-0.4, 0),
    breaks = c(-0.3, 0),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), # rows = vars(label),
    # labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  )

# Metameta_Fig3_female.sig #(Figure 5B left panel)
```
## JOIN!!! CODE MISSING??
malebias_Fig2_sigtraits
Metameta_Fig3_female.sig #(Figure 5B left panel)
Metameta_Fig3_male.sig
```{r}
Fig5B <- ggarrange(Metameta_Fig3_female.sig, Metameta_Fig3_male.sig,
  ncol = 2, nrow = 1, widths = c(1, 1.20), heights = c(1, 1)
)

Fig5 <- ggarrange(malebias_Fig2_sigtraits, Fig5B,
  ncol = 1, nrow = 2, widths = c(1, 1.10), heights = c(1.10, 1),  labels = c("A", "B")
)
Fig5
```



# Supplemental Plots
## Figure S1 
### Including lnVR
### Count data, including lnVR (Fig S1 panel A)

```{r}
# *Prepare data for all traits

meta.plot2.all <- meta_clean %>%
  select(lnCVR, lnVR, lnRR, GroupingTerm) %>%
  arrange(GroupingTerm)

meta.plot2.all.bS1 <- gather(meta.plot2.all, trait, value, c(lnCVR, lnVR, lnRR))

meta.plot2.all.bS1$trait <- factor(meta.plot2.all.bS1$trait, levels = c("lnCVR", "lnVR", "lnRR"))

meta.plot2.all.cS1 <- meta.plot2.all.bS1 %>%
  group_by_at(vars(trait, GroupingTerm)) %>%
  summarise(
    malebias = sum(value > 0), femalebias = sum(value <= 0), total = malebias + femalebias,
    malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total
  )

meta.plot2.all.cS1$label <- "All traits"

# Re-structure to create stacked bar plots

meta.plot2.all.dS1 <- as.data.frame(meta.plot2.all.cS1)
meta.plot2.all.eS1 <- gather(meta.plot2.all.dS1, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE)

# Create new sample size variable

meta.plot2.all.eS1$samplesize <- with(meta.plot2.all.eS1, ifelse(sex == "malepercent", malebias, femalebias))

# Add summary row ('All') and re-arrange rows into correct order for plotting (warnings about coercing 'id' into character vector are ok)

meta.plot2.all.fS1 <- meta.plot2.all.eS1 %>% group_by(trait, sex) %>% 
  summarise(GroupingTerm = "All", malebias = sum(malebias), femalebias = sum(femalebias), total = malebias + femalebias, 
            label = "All traits", samplesize = sum(samplesize)) %>%
  mutate(percent = ifelse(sex == "femalepercent", femalebias*100/(malebias+femalebias), malebias*100/(malebias+femalebias))) %>%
  bind_rows(meta.plot2.all.eS1, .) %>%
  mutate(rownumber = row_number()) %>%
  .[c(55, 1:9, 57, 10:18, 59, 19:27, 56, 28:36, 58, 37:45, 60, 46:54), ] 

meta.plot2.all.fS1$GroupingTerm <- factor(meta.plot2.all.fS1$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) 
meta.plot2.all.fS1$GroupingTerm <- factor(meta.plot2.all.fS1$GroupingTerm, rev(levels(meta.plot2.all.fS1$GroupingTerm)))

malebias_FigS1_alltraits <-
  ggplot(meta.plot2.all.fS1) +
  aes(x = GroupingTerm, y = percent, fill = sex) +
  geom_col() +
  geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") +
  geom_text(
    data = subset(meta.plot2.all.fS1, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5),
    color = "white", size = 3.5
  ) +
  facet_grid(
    cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18),
    scales = "free", space = "free"
  ) +
  scale_fill_brewer(palette = "Set2") +
  theme_bw(base_size = 18) +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.position = "none",
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  ) +
  coord_flip()

# malebias_FigS1_alltraits     #(panel A in Figure S1)
```

###  Overall results of second order meta analysis, INCLUDING VR
#### Re-structure data for plotting 
Restructure MALE data for plotting 

```{r}
overall3.male.sigS <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE)

lnCVR.ci <- overall3.male.sigS %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3.male.sigS %>%
  filter(parameter == "lnVR") %>%
  mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3.male.sigS %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4.male.sigS <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high)

overall4.male.sigS$label <- "CI not overlapping zero"

# Data are re-structured, and grouping terms are being re-ordered

overall3S <- gather(overall2, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE)

lnCVR.ci <- overall3S %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3S %>%
  filter(parameter == "lnVR") %>%
  mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3S %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4S <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high)

# Re-order grouping terms

overall4S$GroupingTerm <- factor(overall4S$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All"))
overall4S$GroupingTerm <- factor(overall4S$GroupingTerm, rev(levels(overall4S$GroupingTerm)))
overall4S$label <- "All traits"
```

#### Preparation for plot, including lnVR
Preparation: Sub-Plot  for Figure S1: all traits (S1 B)

```{r}
Metameta_FigS1_alltraits <- overall4S %>%

  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "black",
    color = "black", size = 2.2,
    show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(-0.24, 0.25),
    breaks = c(-0.2, -0.1, 0, 0.1, 0.2),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), rows = vars(label),
    labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_text(hjust = 0.5, size = 14),
    axis.title.y = element_blank()
  )

# Metameta_FigS1_alltraits
```

### Heterogeneity
The analysis for heterogeneity follows the workflow of the above steps for the different meta-analyses. However, in the initial meta-analysis we extract sigma^2 and errors for mouse strains and centers (Institutions). 

```{r}
# Create dataframe to store results
results.allhetero.grouping <- as.data.frame(cbind(c(1:n), matrix(rep(0, n * 30), ncol = 30)))
names(results.allhetero.grouping) <- c(
  "id", "sigma2_strain.CVR", "sigma2_center.CVR", "sigma2_error.CVR", "s.nlevels.strain.CVR",
  "s.nlevels.center.CVR", "s.nlevels.error.CVR", "sigma2_strain.VR", "sigma2_center.VR", "sigma2_error.VR", "s.nlevels.strain.VR",
  "s.nlevels.center.VR", "s.nlevels.error.VR", "sigma2_strain.RR", "sigma2_center.RR", "sigma2_error.RR", "s.nlevels.strain.RR",
  "s.nlevels.center.RR", "s.nlevels.error.RR", "lnCVR", "lnCVR_lower", "lnCVR_upper", "lnCVR_se", "lnVR", "lnVR_lower", "lnVR_upper",
  "lnVR_se", "lnRR", "lnRR_lower", "lnRR_upper", "lnRR_se"
)

```

LOOP
Parameters to extract from metafor (sigma2's, s.nlevels)

```{r}

for (t in 1:n) {
  tryCatch(
    {
      data_par_age <- data_subset_parameterid_individual_by_age(data, t, age_min = 0, age_center = 100)

      population_stats <- calculate_population_stats(data_par_age)

      results <- create_meta_analysis_effect_sizes(population_stats)

      # lnCVR, logaritm of the ratio of male and female coefficients of variance

      cvr. <- metafor::rma.mv(yi = effect_size_CVR, V = sample_variance_CVR, random = list(
        ~ 1 | strain_name, ~ 1 | production_center,
        ~ 1 | err
      ), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), data = results)
      results.allhetero.grouping[t, 2] <- cvr.$sigma2[1]
      results.allhetero.grouping[t, 3] <- cvr.$sigma2[2]
      results.allhetero.grouping[t, 4] <- cvr.$sigma2[3]
      results.allhetero.grouping[t, 5] <- cvr.$s.nlevels[1]
      results.allhetero.grouping[t, 6] <- cvr.$s.nlevels[2]
      results.allhetero.grouping[t, 7] <- cvr.$s.nlevels[3]
      results.allhetero.grouping[t, 20] <- cvr.$b
      results.allhetero.grouping[t, 21] <- cvr.$ci.lb
      results.allhetero.grouping[t, 22] <- cvr.$ci.ub
      results.allhetero.grouping[t, 23] <- cvr.$se

      # lnVR, male to female variability ratio (logarithm of male and female standard deviations)

      vr. <- metafor::rma.mv(yi = effect_size_VR, V = sample_variance_VR, random = list(
        ~ 1 | strain_name, ~ 1 | production_center,
        ~ 1 | err
      ), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), data = results)
      results.allhetero.grouping[t, 8] <- vr.$sigma2[1]
      results.allhetero.grouping[t, 9] <- vr.$sigma2[2]
      results.allhetero.grouping[t, 10] <- vr.$sigma2[3]
      results.allhetero.grouping[t, 11] <- vr.$s.nlevels[1]
      results.allhetero.grouping[t, 12] <- vr.$s.nlevels[2]
      results.allhetero.grouping[t, 13] <- vr.$s.nlevels[3]
      results.allhetero.grouping[t, 24] <- vr.$b
      results.allhetero.grouping[t, 25] <- vr.$ci.lb
      results.allhetero.grouping[t, 26] <- vr.$ci.ub
      results.allhetero.grouping[t, 27] <- vr.$se

      # lnRR, response ratio (logarithm of male and female means)

      rr. <- metafor::rma.mv(yi = effect_size_RR, V = sample_variance_RR, random = list(
        ~ 1 | strain_name, ~ 1 | production_center,
        ~ 1 | err
      ), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), data = results)
      results.allhetero.grouping[t, 14] <- rr.$sigma2[1]
      results.allhetero.grouping[t, 15] <- rr.$sigma2[2]
      results.allhetero.grouping[t, 16] <- rr.$sigma2[3]
      results.allhetero.grouping[t, 17] <- rr.$s.nlevels[1]
      results.allhetero.grouping[t, 18] <- rr.$s.nlevels[2]
      results.allhetero.grouping[t, 19] <- rr.$s.nlevels[3]
      results.allhetero.grouping[t, 28] <- rr.$b
      results.allhetero.grouping[t, 29] <- rr.$ci.lb
      results.allhetero.grouping[t, 30] <- rr.$ci.ub
      results.allhetero.grouping[t, 31] <- rr.$se
    },
    error = function(e) {
      cat("ERROR :", conditionMessage(e), "\n")
    }
  )
}
```

#### Exclude traits without variation between mouse strains; merge datasets  #Felix added 6/2/2020

```{r}
results.allhetero.grouping2 <- results.allhetero.grouping[results.allhetero.grouping$s.nlevels.strain.VR != 0, ]
# nrow(results.allhetero.grouping) #223  Felix 7/2/2020 added: not sure. Run again and it was 232?!
```

Merge data sets containing metafor results with procedure etc. names 

```{r}
# procedures <- read.csv(here("export", "procedures.csv"))

results.allhetero.grouping2$parameter_group <- data$parameter_group[match(results.allhetero.grouping2$id, data$id)]
results.allhetero.grouping2$procedure <- data$procedure_name[match(results.allhetero.grouping2$id, data$id)]

results.allhetero.grouping2$GroupingTerm <- procedures$GroupingTerm[match(results.allhetero.grouping2$procedure, procedures$procedure)]
results.allhetero.grouping2$parameter_name <- data$parameter_name[match(results.allhetero.grouping2$id, data$id)]
```

#### Correlated parameters

```{r}
metahetero1 <- results.allhetero.grouping2
# length(unique(metahetero1$procedure)) #19
# length(unique(metahetero1$GroupingTerm)) #9 
# length(unique(metahetero1$parameter_group)) #152
# length(unique(metahetero1$parameter_name)) #223

# Count of number of parameter names (correlated sub-traits) in each parameter group (par_group_size)

metahetero1b <-
  metahetero1 %>%
  group_by(parameter_group) %>%
  mutate(par_group_size = n_distinct(parameter_name))

metahetero1$par_group_size <- metahetero1b$par_group_size[match(metahetero1$parameter_group, metahetero1b$parameter_group)]

# Create subsets with > 1 count (par_group_size > 1)

metahetero1_sub <- subset(metahetero1, par_group_size > 1) # 92 observations
# str(metahetero1_sub)
# metahetero1_sub$sampleSize <- as.numeric(metahetero1_sub$sampleSize) #from previous analysis? don't think is used: : delete in final version

# Nest data

n_count. <- metahetero1_sub %>%
  group_by(parameter_group) %>%
  # mutate(raw_N = sum(sampleSize)) %>%  #Felix added: don't think is necessary: delete in final version
  nest()

# meta-analysis preparation

model_count. <- n_count. %>%
  mutate(
    model_lnRR = map(data, ~ robu(.x$lnRR ~ 1,
      data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8,
      small = TRUE, var.eff.size = (.x$lnRR_se)^2
    )),
    model_lnVR = map(data, ~ robu(.x$lnVR ~ 1,
      data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8,
      small = TRUE, var.eff.size = (.x$lnVR_se)^2
    )),
    model_lnCVR = map(data, ~ robu(.x$lnCVR ~ 1,
      data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8,
      small = TRUE, var.eff.size = (.x$lnCVR_se)^2
    ))
  )


# Robumeta object details:
# str(model_count.$model_lnCVR[[1]])

## *Perform meta-analyses on correlated sub-traits, using robumeta
 # Susi / FELIX: what's this below?
# Shinichi: We think we want to use these for further analyses:
# residual variance: as.numeric(robu_fit$mod_info$term1)     (same as 'mod_info$tau.sq')
# sample size: robu_fit$N

## **Extract and save parameter estimates

# Felix: doesn't work , error message:
#!!!!!!!!!!! ERROR!!!!!!!!!!!!!!!!!!!!
#Error: Column `parameter_group` can't be modified because it's a grouping variable

count_fun. <- function(mod_sub) {
  return(c(as.numeric(mod_sub$mod_info$term1), mod_sub$N))
}

robusub_RR. <- model_count. %>%
  transmute(estimatelnRR = map(model_lnRR, count_fun.)) %>%    #Felix 4/2/2020: deleted: 'parameter_group' (in brackets, after 'transmute')
  mutate(r = map(estimatelnRR, ~ data.frame(t(.)))) %>%
  unnest(r) %>%
  select(-estimatelnRR) %>%
  purrr::set_names(c("parameter_group", "var.RR", "N.RR"))

robusub_CVR. <- model_count. %>%
  transmute(estimatelnCVR = map(model_lnCVR, count_fun.)) %>%
  mutate(r = map(estimatelnCVR, ~ data.frame(t(.)))) %>%
  unnest(r) %>%
  select(-estimatelnCVR) %>%
  purrr::set_names(c("parameter_group", "var.CVR", "N.CVR"))

robusub_VR. <- model_count. %>%
  transmute(estimatelnVR = map(model_lnVR, count_fun.)) %>%
  mutate(r = map(estimatelnVR, ~ data.frame(t(.)))) %>%
  unnest(r) %>%
  select(-estimatelnVR) %>%
  purrr::set_names(c("parameter_group", "var.VR", "N.VR"))

robu_all. <- full_join(robusub_CVR., robusub_VR.) %>% full_join(., robusub_RR.)
```

Merge the two data sets (the new [robu_all.] and the initial [uncorrelated sub-traits with count = 1])

In this step, we 	
1) merge the N from robumeta and the  N from metafor (s.nlevels.error) together into the same columns (N.RR, N.VR, N.CVR)
2) calculate the total variance for metafor models as the sum of random effect variances and the residual error, then add in the same columns together with the residual variances from robumeta

```{r}
metahetero_all <- metahetero1 %>%
  filter(par_group_size == 1) %>%
  as_tibble()
metahetero_all$N.RR <- metahetero_all$s.nlevels.error.RR
metahetero_all$N.CVR <- metahetero_all$s.nlevels.error.CVR
metahetero_all$N.VR <- metahetero_all$s.nlevels.error.VR
metahetero_all$var.RR <- log(sqrt(metahetero_all$sigma2_strain.RR + metahetero_all$sigma2_center.RR + metahetero_all$sigma2_error.RR))
metahetero_all$var.VR <- log(sqrt(metahetero_all$sigma2_strain.VR + metahetero_all$sigma2_center.VR + metahetero_all$sigma2_error.VR))
metahetero_all$var.CVR <- log(sqrt(metahetero_all$sigma2_strain.CVR + metahetero_all$sigma2_center.CVR + metahetero_all$sigma2_error.CVR))
# str(metahetero_all)
# str(robu_all.)

metahetero_all <- metahetero_all %>% mutate(
  var.RR = if_else(var.RR == -Inf, -7, var.RR),   #Felix commented 6/2/2020: can't remmeber, why -7, -6, -5 in this section!
  var.VR = if_else(var.VR == -Inf, -5, var.VR),
  var.CVR = if_else(var.CVR == -Inf, -6, var.CVR)
)

# **Combine data
## Step1
combinedmetahetero <- bind_rows(robu_all., metahetero_all)
# glimpse(combinedmetahetero)

# Steps 2&3

metacombohetero <- combinedmetahetero
metacombohetero$counts <- metahetero1$par_group_size[match(metacombohetero$parameter_group, metahetero1$parameter_group)]
metacombohetero$procedure2 <- metahetero1$procedure[match(metacombohetero$parameter_group, metahetero1$parameter_group)]
metacombohetero$GroupingTerm2 <- metahetero1$GroupingTerm[match(metacombohetero$parameter_group, metahetero1$parameter_group)]

# **Clean-up and rename

metacombohetero <- metacombohetero %>% select(parameter_group, var.CVR, N.CVR, var.VR, N.VR, var.RR, N.RR, counts, procedure = procedure2, GroupingTerm = GroupingTerm2)  #Felix changed 6/2/2020: was: c(1:7, 43:45, and 2 renaming lines)

```

#### Meta-analysis of heterogeneity

```{r}
## Perform meta-meta-analysis (3 for each of the 9 grouping terms: var.CVR, var.VR, var.RR)

metacombohetero_final <- metacombohetero %>%
  group_by(GroupingTerm) %>%
  nest()

# Final fixed effects meta-analyses within grouping terms, with SE of the estimate

heterog1 <- metacombohetero_final %>%

  mutate(
    model_heteroCVR = map(data, ~ metafor::rma.uni(
      yi = .x$var.CVR, sei = sqrt(1 / 2 * (.x$N.CVR - 1)),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F
    )),
    model_heteroVR = map(data, ~ metafor::rma.uni(
      yi = .x$var.VR, sei = sqrt(1 / 2 * (.x$N.VR - 1)),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F
    )),
    model_heteroRR = map(data, ~ metafor::rma.uni(
      yi = .x$var.RR, sei = sqrt(1 / 2 * (.x$N.RR - 1)),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F
    ))
  )

# Across all grouping terms   

metacombohetero_all_final <- metacombohetero %>%
  nest(data = everything()) 

# Final fixed effects meta-analyses ACROSS grouping terms, with SE of the estimate

heterog1_all <- metacombohetero_all_final %>%
  
  mutate(
    model_heteroCVR = map(data, ~ metafor::rma.uni(
      yi = .x$var.CVR, sei = sqrt(1 / 2 * (.x$N.CVR - 1)),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F
    )),
    model_heteroVR = map(data, ~ metafor::rma.uni(
      yi = .x$var.VR, sei = sqrt(1 / 2 * (.x$N.VR - 1)),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F
    )),
    model_heteroRR = map(data, ~ metafor::rma.uni(
      yi = .x$var.RR, sei = sqrt(1 / 2 * (.x$N.RR - 1)),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F
    ))
  )


# Re-structure data for each grouping term; extract heterogenenity/variance terms; delete un-used variables

Behaviour. <- heterog1 %>%
  filter(., GroupingTerm == "Behaviour") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)

Immunology. <- heterog1 %>%
  filter(., GroupingTerm == "Immunology") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)

Hematology. <- heterog1 %>%
  filter(., GroupingTerm == "Hematology") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)


Hearing. <- heterog1 %>%
  filter(., GroupingTerm == "Hearing") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)

Physiology. <- heterog1 %>%
  filter(., GroupingTerm == "Physiology") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)

Metabolism. <- heterog1 %>%
  filter(., GroupingTerm == "Metabolism") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)

Morphology. <- heterog1 %>%
  filter(., GroupingTerm == "Morphology") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)

Heart. <- heterog1 %>%
  filter(., GroupingTerm == "Heart") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)

Eye. <- heterog1 %>%
  filter(., GroupingTerm == "Eye") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)

#Reorder to be able to keep cell referencing 
heterog1_all <- heterog1_all %>% mutate(GroupingTerm = "All") %>% select(GroupingTerm, everything())

All. <- heterog1_all %>% 
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)

heterog2 <- bind_rows(Behaviour., Morphology., Metabolism., Physiology., Immunology., Hematology., Heart., Hearing., Eye., All.)
# str(heterog2)
```

#### Heterogeneity PLOT
Restructure data for plotting 

```{r}
heterog3 <- gather(heterog2, parameter, value, c(heteroCVR, heteroVR, heteroRR), factor_key = TRUE)

heteroCVR.ci <- heterog3 %>%
  filter(parameter == "heteroCVR") %>%
  mutate(ci.low = heteroCVR_lower, ci.high = heteroCVR_upper)
heteroVR.ci <- heterog3 %>%
  filter(parameter == "heteroVR") %>%
  mutate(ci.low = heteroVR_lower, ci.high = heteroVR_upper)
heteroRR.ci <- heterog3 %>%
  filter(parameter == "heteroRR") %>%
  mutate(ci.low = heteroRR_lower, ci.high = heteroRR_upper)

heterog4 <- bind_rows(heteroCVR.ci, heteroVR.ci, heteroRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high)

# **Re-order grouping terms

heterog4$GroupingTerm <- factor(heterog4$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All"))
heterog4$GroupingTerm <- factor(heterog4$GroupingTerm, rev(levels(heterog4$GroupingTerm)))
heterog4$label <- "All traits"
# write.csv(heterog4, "heterog4.csv")
```

#### Plot S1 C (Second-order meta analysis on heterogeneity)

```{r}
heterog5 <- heterog4
heterog5$mean <- as.numeric(exp(heterog5$value))
heterog5$ci.l <- as.numeric(exp(heterog5$ci.low))
heterog5$ci.h <- as.numeric(exp(heterog5$ci.high))

heterog6 <- heterog5

HeteroS1 <-
  heterog6 %>%
  ggplot(aes(y = GroupingTerm, x = mean)) +
  geom_errorbarh(aes(
    xmin = ci.l,
    xmax = ci.h
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "black",
    color = "black", size = 2.2,
    show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(-0.1, 1.4),
    # breaks = c(0, 0.1, 0.2),
    name = "sigma^2"
  ) +
  # geom_vline(xintercept=0,
  # color='black',
  # linetype='dashed')+
  facet_grid(
    cols = vars(parameter), rows = vars(label),
    labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_text(hjust = 0.5, size = 14),
    axis.title.y = element_blank()
  )

#HeteroS1

```


#### Combined Figure S1: overall Count data, Meta anlysis results, Heterogeneity)

```{r}
FigS1 <- ggarrange(malebias_FigS1_alltraits + xlab("percentage sex bias"), Metameta_FigS1_alltraits, HeteroS1, nrow = 3, align = "v", heights = c(1, 1, 1), labels = c("A", "B", "C"))
FigS1
# ggsave("FigS1_OverallResults.pdf", plot = Fig4, width = 6, height = 5)
```

## Figure S2

Plot FigS2 all significant results (CI not overlapping zero) for males
### FELIX:  "ALL" missing. Felix added 11/2/2020: done
```{r}
meta.plot2.sig.bS <- meta.plot2.sig[, c("lnCVR", "lnVR", "lnRR", "lnCVRsig", "lnVRsig", "lnRRsig", "GroupingTerm")]

meta.plot2.sig.cS <- gather(meta.plot2.sig.bS, trait, value, lnCVR:lnRR)
meta.plot2.sig.cS$sig <- "placeholder"

meta.plot2.sig.cS$trait <- factor(meta.plot2.sig.cS$trait, levels = c("lnCVR", "lnVR", "lnRR"))

meta.plot2.sig.cS$sig <- ifelse(meta.plot2.sig.cS$trait == "lnCVR", meta.plot2.sig.cS$lnCVRsig,
  ifelse(meta.plot2.sig.cS$trait == "lnVR", meta.plot2.sig.cS$lnVRsig, meta.plot2.sig.cS$lnRRsig)
)

# choosing sex biased ln-ratios significantly larger than 0
meta.plotS2.sig.malebias <- meta.plot2.sig.cS %>%
  group_by_at(vars(trait, GroupingTerm)) %>%
  filter(sig == 1) %>%
  summarise(male_sig = sum(value > 0), female_sig = sum(value < 0), total = male_sig + female_sig)

meta.plotS2.sig.malebias <- ungroup(meta.plotS2.sig.malebias) %>%
  add_row(trait = "lnCVR", GroupingTerm = "Hearing", male_sig = 0, female_sig = 0, .before = 4) %>% # add "Hearing" for lnCVR (not filtered as only zeros)
  mutate(malepercent = male_sig * 100 / total, femalepercent = female_sig * 100 / total)

meta.plotS2.sig.malebias$label <- "CI not overlapping zero"

# restructure to create stacked bar plots

meta.plotS2.sig.bothsexes <- as.data.frame(meta.plotS2.sig.malebias)
meta.plotS2.sig.bothsexes.b <- gather(meta.plotS2.sig.bothsexes, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE)

# create new sample size variable

meta.plotS2.sig.bothsexes.b$samplesize <- with(meta.plotS2.sig.bothsexes.b, ifelse(sex == "malepercent", male_sig, female_sig))

# Add summary row ('All') and re-arrange rows into correct order for plotting (warnings about coercing 'id' into character vector are ok)

meta.plotS2.sig.bothsexes.c <- meta.plotS2.sig.bothsexes.b %>% group_by(trait, sex) %>% 
  summarise(GroupingTerm = "All", male_sig = sum(male_sig), female_sig = sum(female_sig), total = male_sig + female_sig, 
            label = "CI not overlapping zero", samplesize = sum(samplesize)) %>%
  mutate(percent = ifelse(sex == "femalepercent", female_sig*100/(male_sig + female_sig), male_sig*100/(male_sig + female_sig))) %>%
  bind_rows(meta.plotS2.sig.bothsexes.b, .) %>%
  mutate(rownumber = row_number()) %>%
  .[c(55, 1:9, 57, 10:18, 59, 19:27, 56, 28:36, 58, 37:45, 60, 46:54), ] 

meta.plotS2.sig.bothsexes.c$GroupingTerm <- factor(meta.plotS2.sig.bothsexes.c$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) 
meta.plotS2.sig.bothsexes.c$GroupingTerm <- factor(meta.plotS2.sig.bothsexes.c$GroupingTerm, rev(levels(meta.plotS2.sig.bothsexes.c$GroupingTerm)))

# *Plot Fig2 all significant results (CI not overlapping zero):
#     no sig. lnCVR for 'Hearing' in either sex; no sig. male-biased lnCVR for 'Immunology' and 'Eye, and no sig. male-biased lnVR for 'Eye'

malebias_FigS2_sigtraits <-
  ggplot(meta.plotS2.sig.bothsexes.c) +
  aes(x = GroupingTerm, y = percent, fill = sex) +
  geom_col() +
  geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") +
  geom_text(
    data = subset(meta.plotS2.sig.bothsexes.c, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5),
    color = "white", size = 3.5
  ) +
  facet_grid(
    cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18),
    scales = "free", space = "free"
  ) +
  scale_fill_brewer(palette = "Set2") +
  theme_bw(base_size = 18) +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.position = "none",
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  ) +
  coord_flip()

# malebias_FigS2_sigtraits # this is Figure S2 A
```
### Prepare data for traits with effect size ratios > 10% larger in males, supplemental Figure S2
### FELIX:  "ALL" missing. Felix added 11/2/2020: done
This Figure extends Figure 4, as it includes results not only for lnCVR and lnRR but also lnCVR. In addition, we compare two different assessments of sex-bias, significance (CI not overlapping zero) and sex differences in male / female ratios > 10%

### Over 10% male bias, count data (first- order metanalysis) 
```{r}
meta.plot2.over10 <- meta_clean %>%
  select(lnCVR, lnVR, lnRR, GroupingTerm) %>%
  arrange(GroupingTerm) 

meta.plot2.over10.b <- gather(meta.plot2.over10, trait, value, c(lnCVR, lnVR, lnRR)) 

meta.plot2.over10.b$trait <- factor(meta.plot2.over10.b$trait, levels = c("lnCVR", "lnVR", "lnRR")) 

meta.plot2.over10.c <- meta.plot2.over10.b %>%
  group_by_at(vars(trait, GroupingTerm)) %>%
  summarise(
    malebias = sum(value > log(11 / 10)), femalebias = sum(value < log(9 / 10)), total = malebias + femalebias,
    malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total
  )

meta.plot2.over10.c$label <- "Sex difference in m/f ratios > 10%"

# restructure to create stacked bar plots

meta.plot2.over10.c <- as.data.frame(meta.plot2.over10.c)
meta.plot2.over10.d <- gather(meta.plot2.over10.c, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE)

# create new sample size variable

meta.plot2.over10.d$samplesize <- with(meta.plot2.over10.d, ifelse(sex == "malepercent", malebias, femalebias))

# Add summary row ('All') and re-arrange rows into correct order for plotting (warnings about coercing 'id' into character vector are ok)

meta.plot2.over10.e <- meta.plot2.over10.d %>% group_by(trait, sex) %>% 
  summarise(GroupingTerm = "All", malebias = sum(malebias), femalebias = sum(femalebias), total = malebias + femalebias, 
            label = "Sex difference in m/f ratios > 10%", samplesize = sum(samplesize)) %>%
  mutate(percent = ifelse(sex == "femalepercent", femalebias*100/(malebias + femalebias), malebias*100/(malebias + femalebias))) %>%
  bind_rows(meta.plot2.over10.d, .) %>%
  mutate(rownumber = row_number()) %>%
  .[c(55, 1:9, 57, 10:18, 59, 19:27, 56, 28:36, 58, 37:45, 60, 46:54), ] 

meta.plot2.over10.e$GroupingTerm <- factor(meta.plot2.over10.e$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) 
meta.plot2.over10.e$GroupingTerm <- factor(meta.plot2.over10.e$GroupingTerm, rev(levels(meta.plot2.over10.e$GroupingTerm)))


# *Plot Fig2 Sex difference in m/f ratio > 10%
malebias_Fig2_over10 <-
  ggplot(meta.plot2.over10.e) +
  aes(x = GroupingTerm, y = percent, fill = sex) +
  geom_col() +
  geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") +
  geom_text(
    data = subset(meta.plot2.over10.e, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5),
    color = "white", size = 3.5
  ) +
  facet_grid(
    cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18),
    scales = "free", space = "free"
  ) +
  scale_fill_brewer(palette = "Set2") +
  theme_bw(base_size = 18) +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_blank(),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.position = "none",
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  ) +
  coord_flip()

# malebias_Fig2_over10  (supplemental Figure S2)
```

#### Fig S2, second-order meta-analysis, male traits
#### Female Figure, significant traits
Female FigS2 B sig

Prepare data for traits with CI not overlapping 0
create column with 1= different from zero, 0= zero included in CI


Restructure data for plotting

```{r}
overall3.female.sigS <- gather(overall.female.plot3, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE)

lnCVR.ci <- overall3.female.sigS %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3.female.sigS %>%
  filter(parameter == "lnVR") %>%
  mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3.female.sigS %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4.female.sigS <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high)

overall4.female.sigS$label <- "CI not overlapping zero"

##

Metameta_FigS2_female.sig <- overall4.female.sigS %>%
  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "salmon1", color = "salmon1", size = 2.2,
    show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(-0.4, 0),
    breaks = c(-0.3, 0),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), # rows = vars(label),
    # labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  )

# Metameta_FigS2_female.sig
```

Prepare data for traits with m/f difference > 10%

Create column with 1= larger, 0= difference not larger than 10% between male/female ratios
```{r}
meta.male.plot3.perc <- metacombo %>%
  mutate(
    percCVR = ifelse(lnCVR > log(11 / 10), 1, 0),
    percVR = ifelse(lnVR > log(11 / 10), 1, 0),
    percRR = ifelse(lnRR > log(11 / 10), 1, 0)
  )

# Significant subset for lnCVR
metacombo_male.plot3.CVR.perc <- meta.male.plot3.perc %>%
  filter(percCVR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_male.plot3.CVR.perc.all <- meta.male.plot3.perc %>%
  filter(percCVR == 1) %>%
  nest(data = everything())

# Significant subset for lnVR
metacombo_male.plot3.VR.perc <- meta.male.plot3.perc %>%
  filter(percVR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_male.plot3.VR.perc.all <- meta.male.plot3.perc %>%
  filter(percVR == 1) %>%
  nest(data = everything())

# Significant subset for lnRR
metacombo_male.plot3.RR.perc <- meta.male.plot3.perc %>%
  filter(percRR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_male.plot3.RR.perc.all <- meta.male.plot3.perc %>%
  filter(percRR == 1) %>%
  nest(data = everything())


# **Final fixed effects meta-analyses within grouping terms and across grouping terms, with SE of the estimate

plot3.male.meta.CVR.perc <- metacombo_male.plot3.CVR.perc %>%
  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.VR.perc <- metacombo_male.plot3.VR.perc %>%
  mutate(model_lnVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.RR.perc <- metacombo_male.plot3.RR.perc %>%
  mutate(model_lnRR = map(data, ~ metafor::rma.uni(
    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

# Across all grouping terms #

plot3.male.meta.CVR.perc.all <- metacombo_male.plot3.CVR.perc.all %>%
  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.CVR.perc.all <- plot3.male.meta.CVR.perc.all %>% mutate(GroupingTerm = "All")

plot3.male.meta.VR.perc.all <- metacombo_male.plot3.VR.perc.all %>%
  mutate(model_lnVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.VR.perc.all <- plot3.male.meta.VR.perc.all %>% mutate(GroupingTerm = "All")

plot3.male.meta.RR.perc.all <- metacombo_male.plot3.RR.perc.all %>%
  mutate(model_lnRR = map(data, ~ metafor::rma.uni(
    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.RR.perc.all <- plot3.male.meta.RR.perc.all %>% mutate(GroupingTerm = "All")

# Combine with separate grouping term results

plot3.male.meta.CVR.perc <- bind_rows(plot3.male.meta.CVR.perc, plot3.male.meta.CVR.perc.all)
plot3.male.meta.VR.perc <- bind_rows(plot3.male.meta.VR.perc, plot3.male.meta.VR.perc.all)
plot3.male.meta.RR.perc <- bind_rows(plot3.male.meta.RR.perc, plot3.male.meta.RR.perc.all)


# **Re-structure data for each grouping term; delete un-used variables: "Hearing missing for all 3 parameters"

plot3.male.meta.CVR.perc.b <- as.data.frame(plot3.male.meta.CVR.perc %>% group_by(GroupingTerm) %>%
  mutate(
    lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)),
    lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3))
  ))[, c(1, 4:7)]
add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.CVR.perc.b))
plot3.male.meta.CVR.perc.b <- rbind(plot3.male.meta.CVR.perc.b, add.row.hearing)
plot3.male.meta.CVR.perc.b <- plot3.male.meta.CVR.perc.b[order(plot3.male.meta.CVR.perc.b$GroupingTerm), ]

plot3.male.meta.VR.perc.b <- as.data.frame(plot3.male.meta.VR.perc %>% group_by(GroupingTerm) %>%
  mutate(
    lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)),
    lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3))
  ))[, c(1, 4:7)]
add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.VR.perc.b))
plot3.male.meta.VR.perc.b <- rbind(plot3.male.meta.VR.perc.b, add.row.hearing)
plot3.male.meta.VR.perc.b <- plot3.male.meta.VR.perc.b[order(plot3.male.meta.VR.perc.b$GroupingTerm), ]

plot3.male.meta.RR.perc.b <- as.data.frame(plot3.male.meta.RR.perc %>% group_by(GroupingTerm) %>%
  mutate(
    lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)),
    lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3))
  ))[, c(1, 4:7)]
add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>%
  setNames(names(plot3.male.meta.RR.perc.b))
plot3.male.meta.RR.perc.b <- rbind(plot3.male.meta.RR.perc.b, add.row.hearing)

add.row.eye <- as.data.frame(t(c("Eye", NA, NA, NA, NA))) %>%
  setNames(names(plot3.male.meta.RR.perc.b))
plot3.male.meta.RR.perc.b <- rbind(plot3.male.meta.RR.perc.b, add.row.eye)

plot3.male.meta.RR.perc.b <- plot3.male.meta.RR.perc.b[order(plot3.male.meta.RR.perc.b$GroupingTerm), ]

plot3.male.meta.CVR.Vr.perc <- full_join(plot3.male.meta.CVR.perc.b, plot3.male.meta.VR.perc.b)
overall.male.plot3.perc <- full_join(plot3.male.meta.CVR.Vr.perc, plot3.male.meta.RR.perc.b)


overall.male.plot3.perc$GroupingTerm <- factor(overall.male.plot3.perc$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All"))
overall.male.plot3.perc$GroupingTerm <- factor(overall.male.plot3.perc$GroupingTerm, rev(levels(overall.male.plot3.perc$GroupingTerm)))

```

Restructure data for plotting : Male biased, 10% difference

```{r}
overall3.perc <- gather(overall.male.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) 

lnCVR.ci <- overall3.perc %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3.perc  %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3.perc %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4.male.perc <- bind_rows(lnCVR.ci,lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) 

overall4.male.perc$label <- "Sex difference in m/f ratios > 10%"

overall4.male.perc$value <- as.numeric(overall4.male.perc$value)
overall4.male.perc$ci.low <- as.numeric(overall4.male.perc$ci.low)
overall4.male.perc$ci.high <- as.numeric(overall4.male.perc$ci.high)
```

Plot Fig S2 all >10% difference (male bias)
S2 B, bottom right 

```{r}

Metameta_Fig3_male.perc <- overall4.male.perc %>% # filter(., GroupingTerm != "Hearing") %>%
  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(
    shape = parameter,
    fill = parameter
  ),
  color = "mediumaquamarine", size = 2.2,
  show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(-0.2, 0.62),
    breaks = c(0, 0.3),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), rows = vars(label),
    labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_blank(),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_text(hjust = 0.5, size = 14),
    axis.title.y = element_blank()
  )

# Metameta_Fig3_male.perc (Figure S2 right panel)
```

#### Female Fig S2 >10%

```{r}

meta.plot3.perc <- metacombo %>%
  mutate(
    percCVR = ifelse(lnCVR < log(9 / 10), 1, 0),
    percVR = ifelse(lnVR < log(9 / 10), 1, 0),
    percRR = ifelse(lnRR < log(9 / 10), 1, 0)
  )

# Significant subset for lnCVR
metacombo_plot3.CVR.perc <- meta.plot3.perc %>%
  filter(percCVR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_plot3.CVR.perc.all <- meta.plot3.perc %>%
  filter(percCVR == 1) %>%
  nest(data = everything())

# Significant subset for lnVR
metacombo_plot3.VR.perc <- meta.plot3.perc %>%
  filter(percVR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_plot3.VR.perc.all <- meta.plot3.perc %>%
  filter(percVR == 1) %>%
  nest(data = everything())

# Significant subset for lnRR
metacombo_plot3.RR.perc <- meta.plot3.perc %>%
  filter(percRR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_plot3.RR.perc.all <- meta.plot3.perc %>%
  filter(percRR == 1) %>%
  nest(data = everything())


# **Final fixed effects meta-analyses within grouping terms, with SE of the estimate

plot3.meta.CVR.perc <- metacombo_plot3.CVR.perc %>%
  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.meta.VR.perc <- metacombo_plot3.VR.perc %>%
  mutate(model_lnVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.meta.RR.perc <- metacombo_plot3.RR.perc %>%
  mutate(model_lnRR = map(data, ~ metafor::rma.uni(
    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

# Across all grouping terms #

plot3.meta.CVR.perc.all <- metacombo_plot3.CVR.perc.all %>%
  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.meta.CVR.perc.all <- plot3.meta.CVR.perc.all %>% mutate(GroupingTerm = "All")

plot3.meta.VR.perc.all <- metacombo_plot3.VR.perc.all %>%
  mutate(model_lnVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.meta.VR.perc.all <- plot3.meta.VR.perc.all %>% mutate(GroupingTerm = "All")

plot3.meta.RR.perc.all <- metacombo_plot3.RR.perc.all %>%
  mutate(model_lnRR = map(data, ~ metafor::rma.uni(
    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.meta.RR.perc.all <- plot3.meta.RR.perc.all %>% mutate(GroupingTerm = "All")

# Combine with separate grouping term results

plot3.meta.CVR.perc <- bind_rows(plot3.meta.CVR.perc, plot3.meta.CVR.perc.all)
plot3.meta.VR.perc <- bind_rows(plot3.meta.VR.perc, plot3.meta.VR.perc.all)
plot3.meta.RR.perc <- bind_rows(plot3.meta.RR.perc, plot3.meta.RR.perc.all)


# **Re-structure data for each grouping term; delete un-used variables: "Hearing missing for all 3 parameters"

plot3.meta.CVR.perc.b <- as.data.frame(plot3.meta.CVR.perc %>% group_by(GroupingTerm) %>%
  mutate(
    lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)),
    lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3))
  ))[, c(1, 4:7)]
add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.CVR.perc.b))
plot3.meta.CVR.perc.b <- rbind(plot3.meta.CVR.perc.b, add.row.hearing)
plot3.meta.CVR.perc.b <- plot3.meta.CVR.perc.b[order(plot3.meta.CVR.perc.b$GroupingTerm), ]

plot3.meta.VR.perc.b <- as.data.frame(plot3.meta.VR.perc %>% group_by(GroupingTerm) %>%
  mutate(
    lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)),
    lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3))
  ))[, c(1, 4:7)]
add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.VR.perc.b))
plot3.meta.VR.perc.b <- rbind(plot3.meta.VR.perc.b, add.row.hearing)
plot3.meta.VR.perc.b <- plot3.meta.VR.perc.b[order(plot3.meta.VR.perc.b$GroupingTerm), ]

plot3.meta.RR.perc.b <- as.data.frame(plot3.meta.RR.perc %>% group_by(GroupingTerm) %>%
  mutate(
    lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)),
    lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3))
  ))[, c(1, 4:7)]
add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.RR.perc.b))
plot3.meta.RR.perc.b <- rbind(plot3.meta.RR.perc.b, add.row.hearing)
add.row.hematology <- as.data.frame(t(c("Hematology", NA, NA, NA, NA))) %>%
  setNames(names(plot3.meta.RR.perc.b))
plot3.meta.RR.perc.b <- rbind(plot3.meta.RR.perc.b, add.row.hematology)


plot3.meta.RR.perc.b <- plot3.meta.RR.perc.b[order(plot3.meta.RR.perc.b$GroupingTerm), ]

plot3.meta.CVR.perc.c <- full_join(plot3.meta.CVR.perc.b, plot3.meta.VR.perc.b)
overall.plot3.perc <- full_join(plot3.meta.CVR.perc.c, plot3.meta.RR.perc.b)


overall.plot3.perc$GroupingTerm <- factor(overall.plot3.perc$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All"))
overall.plot3.perc$GroupingTerm <- factor(overall.plot3.perc$GroupingTerm, rev(levels(overall.plot3.perc$GroupingTerm)))
```

Restructure data for plotting
Female bias, 10 percent difference

```{r}
overall3.perc <- gather(overall.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) 

lnCVR.ci <- overall3.perc %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3.perc  %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3.perc %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4.perc <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high)

overall4.perc$label <- "Sex difference in m/f ratios > 10%"

overall4.perc$value <- as.numeric(overall4.perc$value)
overall4.perc$ci.low <- as.numeric(overall4.perc$ci.low)
overall4.perc$ci.high <- as.numeric(overall4.perc$ci.high)
```

Plot FigS2 all >10% difference (female)
Figure S2B, bottom left

```{r}
Metameta_Fig3_female.perc <- overall4.perc %>%
  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "salmon1", color = "salmon1", size = 2.2,
    show.legend = FALSE
  ) +

  # scale_shape_manual(values =

  scale_x_continuous(
    limits = c(-0.53, 0.2),
    breaks = c(-0.3, 0),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), # rows = vars(label),
    # labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_blank(),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_text(hjust = 0.5, size = 14),
    axis.title.y = element_blank()
  )

# Metameta_Fig3_female.perc (Figure 5D left panel)
```
MISSING
Metameta_Fig3_female.sig Metameta_Fig3_female.sig VR!!!
# ADDED TO TEST
#Metameta_FigS2_male.sig (Figure S2B top right panel)

Restructure MALE data for plotting 

```{r}
overall3.male.sigS <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE)


lnCVR.ci <- overall3.male.sigS %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3.male.sigS %>%
  filter(parameter == "lnVR") %>%
  mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3.male.sigS %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4.male.sigS <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high)

overall4.male.sigS$label <- "CI not overlapping zero"
```

Plot FigS2 all significant results (CI not overlapping zero, male )

```{r}
Metameta_FigS2_male.sig <- overall4.male.sigS %>%
  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "mediumaquamarine", color = "mediumaquamarine", size = 2.2,
    show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(0, 0.4),
    breaks = c(0, 0.3),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), rows = vars(label),
    labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  )

# Metameta_FigS2_male.sig
```



#### Plot Fig S2:   plots combined

Metameta_FigS2_female.sig
```{r}
library(ggpubr)
FigS2b <- ggarrange(Metameta_FigS2_female.sig, Metameta_FigS2_male.sig,
  ncol = 2, nrow = 1, widths = c(1, 1.20), heights = c(1, 1)
)

FigS2d <- ggarrange(Metameta_Fig3_female.perc, Metameta_Fig3_male.perc,
  ncol = 2, nrow = 1, widths = c(1, 1.20), heights = c(1, 1)
)

# end combination Figure 5
FigS2 <- ggarrange(malebias_FigS2_sigtraits, malebias_Fig2_over10, FigS2b, FigS2d, ncol = 1, nrow = 4, heights = c(2.3, 2, 2.1, 2), labels = c("A", " ", "B", " "))
FigS2
```

## NOT SURE WHAT THIS BELOW IS?? Felix added 11/2/2020: Ich glaube, dass das von vorher war. wenn wir alles oben haben, dann unten loeschen

## Figure S2: sex-bias, including VR

Prepare data for traits with effect size ratios > 10% larger in males

```{r}
meta.plotS2.over10 <- meta_clean %>%
  select(lnCVR, lnVR, lnRR, GroupingTerm) %>%
  arrange(GroupingTerm)

meta.plotS2.over10.b <- gather(meta.plotS2.over10, trait, value, c(lnCVR, lnVR, lnRR))

meta.plotS2.over10.b$trait <- factor(meta.plotS2.over10.b$trait, levels = c("lnCVR", "lnVR", "lnRR"))

meta.plotS2.over10.c <- meta.plotS2.over10.b %>%
  group_by_at(vars(trait, GroupingTerm)) %>%
  summarise(
    malebias = sum(value > log(11 / 10)), femalebias = sum(value < log(9 / 10)), total = malebias + femalebias,
    malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total
  )

meta.plotS2.over10.c$label <- "Sex difference in m/f ratios > 10%"

# restructure to create stacked bar plots

meta.plotS2.over10.c <- as.data.frame(meta.plotS2.over10.c)
meta.plotS2.over10.d <- gather(meta.plotS2.over10.c, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE)

# create new sample size variable

meta.plotS2.over10.d$samplesize <- with(meta.plotS2.over10.d, ifelse(sex == "malepercent", malebias, femalebias))

# *Plot FigS2 Sex difference in m/f ratio > 10%
malebias_FigS2_over10 <-
  ggplot(meta.plotS2.over10.d) +
  aes(x = GroupingTerm, y = percent, fill = sex) +
  geom_col() +
  geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") +
  geom_text(
    data = subset(meta.plot2.over10.d, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5),
    color = "white", size = 3.5
  ) +
  facet_grid(
    cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18),
    scales = "free", space = "free"
  ) +
  scale_fill_brewer(palette = "Set2") +
  theme_bw(base_size = 18) +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_blank(),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.position = "none",
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  ) +
  coord_flip()

# malebias_FigS2_over10  #(Panel B in Fig S2 in ms)
```



#Metameta_FigS2_male.sig (Figure S2B top right panel)

Restructure MALE data for plotting 

```{r}
overall3.male.sigS <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE)


lnCVR.ci <- overall3.male.sigS %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3.male.sigS %>%
  filter(parameter == "lnVR") %>%
  mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3.male.sigS %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4.male.sigS <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high)

overall4.male.sigS$label <- "CI not overlapping zero"
```

Plot FigS2 all significant results (CI not overlapping zero, male )

```{r}
Metameta_FigS2_male.sig <- overall4.male.sigS %>%
  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "mediumaquamarine", color = "mediumaquamarine", size = 2.2,
    show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(0, 0.4),
    breaks = c(0, 0.3),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), rows = vars(label),
    labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  )

# Metameta_FigS2_male.sig
```

### 10 % Perc sex difference, male bias
Restructure data for plotting : Male biased, 10% difference

```{r}
overall3S.perc <- gather(overall.male.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) # lnVR,

lnCVR.ci <- overall3S.perc %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3S.perc %>%
  filter(parameter == "lnVR") %>%
  mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3S.perc %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4S.male.perc <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # lnVR.ci,

overall4S.male.perc$label <- "Sex difference in m/f ratios > 10%"

overall4S.male.perc$value <- as.numeric(overall4S.male.perc$value)
overall4S.male.perc$ci.low <- as.numeric(overall4S.male.perc$ci.low)
overall4S.male.perc$ci.high <- as.numeric(overall4S.male.perc$ci.high)
```

Plot FigS2  all >10% difference (male bias)

```{r}
Metameta_FigS2_male.perc <- overall4S.male.perc %>% # filter(., GroupingTerm != "Hearing") %>%
  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(
    shape = parameter,
    fill = parameter
  ),
  color = "mediumaquamarine", size = 2.2,
  show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(-0.2, 0.62),
    breaks = c(0, 0.3),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), rows = vars(label),
    labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_blank(),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_text(hjust = 0.5, size = 14),
    axis.title.y = element_blank()
  )

# Metameta_FigS2_male.perc (Figure 5D right panel)
```

Restructure data for plotting: 
Female bias, 10 percent difference, including VR

```{r}
overall3S.perc <- gather(overall.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) # lnVR,

lnCVR.ci <- overall3S.perc %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3S.perc %>%
  filter(parameter == "lnVR") %>%
  mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3S.perc %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4S.perc <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high)

overall4S.perc$label <- "Sex difference in m/f ratios > 10%"

overall4S.perc$value <- as.numeric(overall4S.perc$value)
overall4S.perc$ci.low <- as.numeric(overall4S.perc$ci.low)
overall4S.perc$ci.high <- as.numeric(overall4S.perc$ci.high)
```

Plot Fig5D all >10% difference (female)

```{r}
Metameta_Fig3S_female.perc <- overall4S.perc %>%
  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "salmon1", color = "salmon1", size = 2.2,
    show.legend = FALSE
  ) +

  # scale_shape_manual(values =

  scale_x_continuous(
    limits = c(-0.53, 0.2),
    breaks = c(-0.3, 0),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), # rows = vars(label),
    # labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_blank(),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_text(hjust = 0.5, size = 14),
    axis.title.y = element_blank()
  )

# Metameta_Fig3S_female.perc (Figure 5D left panel)
```

Figure S2 

```{r}
FigS2c <- ggarrange(Metameta_FigS2_female.sig, Metameta_FigS2_male.sig,
  ncol = 2, nrow = 1, widths = c(1, 1.20), heights = c(1, 1)
)

FigS2d <- ggarrange(Metameta_Fig3S_female.perc, Metameta_FigS2_male.perc,
  ncol = 2, nrow = 1, widths = c(1, 1.20), heights = c(1, 1)
)

# end combination Figure 5

FigS2 <- ggarrange(malebias_FigS2_sigtraits, malebias_FigS2_over10, FigS2c, FigS2d, ncol = 1, nrow = 4, heights = c(2.2, 2, 2.2, 2), labels = c("A", " ", "B", " "))
FigS2
```

## Acknowledgements
tbd

## R Session Information

```{r}
sessionInfo()
```

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