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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<div id="generalised-linear-mixed-models-with-r2jags-an-extremely-brief-primer" class="section level2">
<h2>(Generalised) linear (mixed) models with R2jags: an extremely brief primer…</h2>
<p><strong><em>Andrew Letten</em></strong></p>
<p><strong><em>Friday, November 28, 2014</em></strong></p>
<p>The purpose of this <a href="http://www.eco-stats.unsw.edu.au/">ecostats</a> lab is to provide a very basic primer on running basic lm, glm and glmms in a Bayesian framework using the R2jags package (and of course JAGS). As such, the goal is not to debate the relative merits of Bayesian vs frequentist approaches, but hopefully to demystify the fitting of Bayesian models, and more specifically demonstrate that in a wide variety of (more basic) use cases the parameter estimates obtained from the two approaches are typically very similar.</p>
<p>We will be attempting to reproduce a small element of the analysis from a recently published article in <em>Journal of Ecology</em> (for which all the data is available at datadryad.org).</p>
<p>Kessler, M., Salazar, L., Homeier, J., Kluge, J. (2014), Species richness-productivity relationships of tropical terrestrial ferns at regional and local scales. <em>Journal of Ecology</em>, 102: 1623-1633. <a href="http://onlinelibrary.wiley.com/doi/10.1111/1365-2745.12299/abstract">http://onlinelibrary.wiley.com/doi/10.1111/1365-2745.12299/abstract</a></p>
<p>First things first: JAGS, R packages and data.</p>
<p>If you don’t have it already you will need to download JAGS from <a href="http://sourceforge.net/projects/mcmc-jags/files/rjags/">http://sourceforge.net/projects/mcmc-jags/files/rjags/</a>.</p>
<pre class="r"><code># required packages
#
# install.packages(&quot;lme4&quot;)
# install.packages(&quot;R2jags&quot;)
# install.apckages(&quot;runjags&quot;)
# install.packages(&quot;coefplot2&quot;, repos=&quot;http://www.math.mcmaster.ca/bolker/R&quot;, type = &quot;source&quot;)

library(lme4)
library(R2jags)
library(runjags)
library(coefplot2)</code></pre>
<p>To obtain the data you can either uncomment the following code block, or alternatively proceed directly to the next block and read in ‘KessDiv.csv’ which has already been modified into a useable format.</p>
<pre class="r"><code># temp &lt;- tempfile(fileext=&quot;.csv&quot;)
# download.file(
#   &quot;http://datadryad.org//bitstream/handle/10255/dryad.67401/Kessler Data Table.csv&quot;, temp)
# fern.dat &lt;- read.csv(temp, header = TRUE, stringsAsFactors=FALSE)
# rm(temp)
# 
# fern.dat = fern.dat[-1,]
# names(fern.dat) = c(&quot;plot&quot;, &quot;elev&quot;, &quot;tag&quot;, &quot;species&quot;, 
#                     &quot;N.09&quot;, &quot;av.09&quot;, &quot;new.10&quot;, &quot;N.10&quot;, 
#                     &quot;av.10&quot;, &quot;new.11&quot;, &quot;N.11&quot;, &quot;av.11&quot;, 
#                     &quot;l.09&quot;, &quot;l.10&quot;, &quot;l.11&quot;, &quot;prod.0910&quot;, 
#                     &quot;prod.1011&quot;, &quot;rhz.09&quot;, &quot;rhz.10&quot;, &quot;rhz.11&quot;, 
#                     &quot;rhz.w&quot;, &quot;rhz.09g&quot;, &quot;rhz.10g&quot;, &quot;rhz.11g&quot;, 
#                     &quot;rhz.prod.0910&quot;, &quot;rhz.prod.1011&quot;, &quot;arb.09&quot;, &quot;arb.10&quot;, 
#                     &quot;arb.11&quot;, &quot;av.diam&quot;, &quot;t.rhz.09&quot;, &quot;t.rhz.10&quot;,
#                     &quot;t.rhz.11&quot;, &quot;t.rhz.prod.0910&quot;, &quot;t.rhz.prod.1011&quot;)
# 
# fern.dat[,c(16,17,25,26)][is.na(fern.dat[,c(16,17,25,26)])] = 0 # convert productivty NAs to zero
# # length(unique(fern.dat$species)) # 88, but article says 91...
# 
# library(dplyr)
# div = fern.dat %&gt;% 
#   mutate(prod = (as.numeric(prod.1011) + as.numeric(rhz.prod.1011))) %&gt;% 
#   filter(elev != 1500, elev != 3000) %&gt;% # plots at 1500 and 3000 m appear not included?? (fig 1) 
#   group_by(plot, elev) %&gt;% 
#   summarise(length(unique(species)), log(sum(prod)))
# 
# colnames(div)[2:4] = c(&quot;elev&quot;, &quot;rich&quot;, &quot;logprod&quot;)</code></pre>
<p>Data consists of the fern species richness and productivity sampled at 18 sites nested at 6 different elevations (i.e. three sites at each elevation.</p>
<pre class="r"><code>div = read.csv(file = &quot;KessDiv.csv&quot;, header = TRUE)

div</code></pre>
<pre><code>##    plot elev rich logprod
## 1     1 4000    3   5.486
## 2    13 1000   10   5.310
## 3    14 1000    8   5.721
## 4    15 1000    9   5.295
## 5    16  500    9   6.369
## 6    17 4000    3   4.614
## 7    18 3500    6   4.070
## 8    19 4000    3   4.527
## 9     2 2000   14   6.594
## 10   20 3500    6   5.540
## 11   21 3500    5   4.405
## 12    3 2000   22   6.863
## 13    4 2500   20   8.048
## 14    5 2000   17   7.433
## 15    6 2500   19   7.667
## 16    7 2500   16   8.213
## 17    8  500   13   6.339
## 18    9  500   12   6.452</code></pre>
<pre class="r"><code>div$elev = as.numeric(as.factor(div$elev)) # formatting for JAGS</code></pre>
<p><strong>Hypothesis: There is a significant relationship between fern species richness <code>div$rich</code> and fern productivity <code>div$logprod</code>.</strong></p>
<p><em>Frequentist approach</em></p>
<pre class="r"><code># lm 
mod = lm(rich ~ logprod, data = div) 
summary(mod) # -ve intercept = negative species richness at zero productivity!</code></pre>
<pre><code>## 
## Call:
## lm(formula = rich ~ logprod, data = div)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -5.454 -1.704  0.579  1.282  7.763 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)  -14.584      3.677   -3.97   0.0011 ** 
## logprod        4.199      0.595    7.05  2.7e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.11 on 16 degrees of freedom
## Multiple R-squared:  0.757,  Adjusted R-squared:  0.742 
## F-statistic: 49.8 on 1 and 16 DF,  p-value: 2.72e-06</code></pre>
<pre class="r"><code>plot(rich ~ logprod, data=div) 
abline(mod)</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk unnamed-chunk-4" alt="plot of chunk unnamed-chunk-4" width="672" /></p>
<p>With a random effect (intercept only) on elevation (NB: only 6 levels with 3 data points in each)</p>
<pre class="r"><code># lmm
Elev = as.factor(div$elev) # back to a factor - not necessary for lmer but doing it here for peace of mind.
mixmod = lmer(rich ~ logprod + (1|Elev), data = div) 
summary(mixmod) # parameter estimates same sign as original paper but different abs value (poss due to how the productivty measure was calculated)</code></pre>
<pre><code>## Linear mixed model fit by REML ['lmerMod']
## Formula: rich ~ logprod + (1 | Elev)
##    Data: div
## 
## REML criterion at convergence: 87.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.3761 -0.5986 -0.0094  0.5593  2.2647 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  Elev     (Intercept) 3.32     1.82    
##  Residual             7.42     2.72    
## Number of obs: 18, groups: Elev, 6
## 
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept)  -11.857      4.671   -2.54
## logprod        3.749      0.755    4.97
## 
## Correlation of Fixed Effects:
##         (Intr)
## logprod -0.978</code></pre>
<p><em>JAGS (Bayesian) approach (linear model without random effect)</em></p>
<p>First need to bundle data into a JAGS friendly format, starting with the design matix…</p>
<pre class="r"><code>X = model.matrix(~ scale(logprod), data = div) #standardize (scale and centre) predictors for JAGS/BUGS analysis
X</code></pre>
<pre><code>##    (Intercept) scale(logprod)
## 1            1        -0.4471
## 2            1        -0.5857
## 3            1        -0.2615
## 4            1        -0.5977
## 5            1         0.2497
## 6            1        -1.1351
## 7            1        -1.5639
## 8            1        -1.2037
## 9            1         0.4276
## 10           1        -0.4041
## 11           1        -1.2999
## 12           1         0.6395
## 13           1         1.5741
## 14           1         1.0890
## 15           1         1.2735
## 16           1         1.7045
## 17           1         0.2259
## 18           1         0.3149
## attr(,&quot;assign&quot;)
## [1] 0 1</code></pre>
<p>A brief aside - remember the goal of regression is to find the best solution (parameters) to a series of linear equations (deterministic part) assumming our response is a random variable following some probability distribution (stochastic part):</p>
<pre class="r"><code># deterministic &amp; stochastic part 
data.frame(&quot;y&quot; = div$rich, 
           &quot;int&quot; = paste(rep(&quot;  =  beta1&quot;, times = 18), &quot;*&quot;, X[,1]), 
           &quot;beta&quot; = rep(&quot;  +   beta2 *&quot;, times = 18), 
           &quot;x&quot; = X[,2],
           &quot;residuals&quot; = paste(&quot;+   e&quot;, 1:18, sep = &quot;&quot;), 
           row.names = paste(&quot;Obs&quot;, 1:18, &quot;      &quot;, sep = &quot;&quot;))</code></pre>
<pre><code>##              y            int          beta       x residuals
## Obs1         3   =  beta1 * 1   +   beta2 * -0.4471    +   e1
## Obs2        10   =  beta1 * 1   +   beta2 * -0.5857    +   e2
## Obs3         8   =  beta1 * 1   +   beta2 * -0.2615    +   e3
## Obs4         9   =  beta1 * 1   +   beta2 * -0.5977    +   e4
## Obs5         9   =  beta1 * 1   +   beta2 *  0.2497    +   e5
## Obs6         3   =  beta1 * 1   +   beta2 * -1.1351    +   e6
## Obs7         6   =  beta1 * 1   +   beta2 * -1.5639    +   e7
## Obs8         3   =  beta1 * 1   +   beta2 * -1.2037    +   e8
## Obs9        14   =  beta1 * 1   +   beta2 *  0.4276    +   e9
## Obs10        6   =  beta1 * 1   +   beta2 * -0.4041   +   e10
## Obs11        5   =  beta1 * 1   +   beta2 * -1.2999   +   e11
## Obs12       22   =  beta1 * 1   +   beta2 *  0.6395   +   e12
## Obs13       20   =  beta1 * 1   +   beta2 *  1.5741   +   e13
## Obs14       17   =  beta1 * 1   +   beta2 *  1.0890   +   e14
## Obs15       19   =  beta1 * 1   +   beta2 *  1.2735   +   e15
## Obs16       16   =  beta1 * 1   +   beta2 *  1.7045   +   e16
## Obs17       13   =  beta1 * 1   +   beta2 *  0.2259   +   e17
## Obs18       12   =  beta1 * 1   +   beta2 *  0.3149   +   e18</code></pre>
<p>We can also write these equations in a more simple form using vectors and matrices, where we have a a vector consisting of the paramaters (betas) and and the matrix is the design matrix <code>X</code> we get from <code>model.matrix</code>. In the second example, we’ll take advantage of matrix algebra to simplifiy the JAGS code.</p>
<p>JAGS expects the data as a named list:</p>
<pre class="r"><code>jags.data = list(Y = div$rich, 
                 X = X, 
                 N = nrow(div), 
                 K = ncol(X))</code></pre>
<p>Specify the model in JAGS code. The sink and cat functions writes the model into a text file in the working directory. Something that can be confusing at first is that unlike R code which is read top to bottom, JAGS does not care about the order. As such variables may be called before they appear to have been defined. (Note the open and closing braces before and after sink are only needed when compiling an html/pdf with <code>knitr</code>, see <a href="https://groups.google.com/forum/#!topic/knitr/TCz9vNLlslY">here</a>).</p>
<pre class="r"><code># JAGS Code
#######################################
{sink(&quot;model.txt&quot;)
cat(&quot;
model{

    #Priors beta 
    #In JAGS: dnorm(0, 1/sigma^2)
    for (i in 1:K) {beta[i] ~ dnorm(0, 0.0001)} # small precision(tau) = large variance = diffuse prior
     
    #Priors sigma
    tau &lt;- 1 / (sigma * sigma)  # tau = 1/ sigma^2
    sigma ~ dunif(0.0001, 10)

    #Likelihood
    for (i in 1:N) {
      Y[i]   ~ dnorm(mu[i], tau)   
      mu[i]  &lt;- eta[i]
      eta[i] &lt;- beta[1] * X[i,1] + beta[2] * X[i,2] 
  
     #Residuals
     Res[i]    &lt;- Y[i] - mu[i]
    }
}
&quot;,fill = TRUE)
sink()}
##########################################</code></pre>
<p>Provide a function to generate inits for each parameters (can skip this for now as JAGS defaults to random starting values)</p>
<pre class="r"><code># # Initial values
# inits  = function (){
#   list(beta = rnorm(ncol(X), 0, 0.01), 
#        sigma = runif(1, 0.0001, 10))
#   }</code></pre>
<p>Tell JAGS which parameters you want to save</p>
<pre class="r"><code>params = c(&quot;beta&quot;, &quot;sigma&quot;, &quot;Res&quot;, &quot;mu&quot;)</code></pre>
<p>Finally, call JAGS using the <code>jags</code> function in R2Jags</p>
<pre class="r"><code>fitmod = jags(data = jags.data,
              # inits = inits, 
              parameters = params,
              model = &quot;model.txt&quot;,
              n.chains = 3,
              n.thin = 10,
              n.iter = 5000,
              n.burnin = 1000)</code></pre>
<pre><code>## module glm loaded</code></pre>
<p>Extract output…</p>
<pre class="r"><code>out = fitmod$BUGSoutput # output in WinBUGS format
out</code></pre>
<pre><code>## Inference for Bugs model at &quot;model.txt&quot;, fit using jags,
##  3 chains, each with 5000 iterations (first 1000 discarded), n.thin = 10
##  n.sims = 1200 iterations saved
##          mean  sd 2.5%  25%  50%  75% 97.5% Rhat n.eff
## Res[1]   -5.5 0.9 -7.2 -6.1 -5.5 -4.9  -3.7    1  1200
## Res[2]    2.3 0.9  0.5  1.6  2.3  2.9   4.2    1  1200
## Res[3]   -1.4 0.8 -3.1 -2.0 -1.5 -0.9   0.2    1  1200
## Res[4]    1.3 1.0 -0.5  0.7  1.3  2.0   3.3    1  1100
## Res[5]   -3.2 0.9 -4.8 -3.7 -3.1 -2.6  -1.5    1  1200
## Res[6]   -1.8 1.2 -4.2 -2.6 -1.8 -1.0   0.6    1   680
## Res[7]    3.5 1.5  0.5  2.4  3.5  4.5   6.4    1   570
## Res[8]   -1.5 1.3 -3.9 -2.3 -1.4 -0.6   1.1    1   650
## Res[9]    0.9 0.9 -0.9  0.3  0.9  1.5   2.8    1  1200
## Res[10]  -2.7 0.9 -4.4 -3.3 -2.7 -2.1  -1.0    1  1200
## Res[11]   1.1 1.4 -1.6  0.2  1.1  2.0   3.7    1   620
## Res[12]   7.8 1.0  5.7  7.1  7.8  8.4   9.8    1  1200
## Res[13]   0.8 1.6 -2.4 -0.2  0.8  1.9   4.0    1   580
## Res[14]   0.4 1.3 -2.1 -0.4  0.4  1.2   2.8    1   710
## Res[15]   1.4 1.4 -1.4  0.5  1.4  2.3   4.1    1   650
## Res[16]  -3.9 1.7 -7.2 -5.0 -3.9 -2.8  -0.6    1   560
## Res[17]   1.0 0.9 -0.7  0.4  1.0  1.5   2.7    1  1200
## Res[18]  -0.5 0.9 -2.2 -1.1 -0.5  0.1   1.3    1  1200
## beta[1]  10.8 0.8  9.2 10.3 10.8 11.4  12.4    1  1200
## beta[2]   5.3 0.9  3.6  4.7  5.3  5.9   7.0    1   530
## deviance 93.4 2.7 90.1 91.4 92.7 94.7 100.2    1  1200
## mu[1]     8.5 0.9  6.7  7.9  8.5  9.1  10.2    1  1200
## mu[2]     7.7 0.9  5.8  7.1  7.7  8.4   9.5    1   970
## mu[3]     9.4 0.8  7.8  8.9  9.5 10.0  11.1    1  1200
## mu[4]     7.7 1.0  5.7  7.0  7.7  8.3   9.5    1   950
## mu[5]    12.2 0.9 10.5 11.6 12.1 12.7  13.8    1  1200
## mu[6]     4.8 1.2  2.4  4.0  4.8  5.6   7.2    1   490
## mu[7]     2.5 1.5 -0.4  1.5  2.5  3.6   5.5    1   570
## mu[8]     4.5 1.3  1.9  3.6  4.4  5.3   6.9    1   650
## mu[9]    13.1 0.9 11.2 12.5 13.1 13.7  14.9    1  1200
## mu[10]    8.7 0.9  7.0  8.1  8.7  9.3  10.4    1  1200
## mu[11]    3.9 1.4  1.3  3.0  3.9  4.8   6.6    1   620
## mu[12]   14.2 1.0 12.2 13.6 14.2 14.9  16.3    1  1000
## mu[13]   19.2 1.6 16.0 18.1 19.2 20.2  22.4    1   600
## mu[14]   16.6 1.3 14.2 15.8 16.6 17.4  19.1    1   710
## mu[15]   17.6 1.4 14.9 16.7 17.6 18.5  20.4    1   650
## mu[16]   19.9 1.7 16.6 18.8 19.9 21.0  23.2    1   580
## mu[17]   12.0 0.9 10.3 11.5 12.0 12.6  13.7    1  1200
## mu[18]   12.5 0.9 10.7 11.9 12.5 13.1  14.2    1  1200
## sigma     3.4 0.6  2.4  2.9  3.3  3.7   4.8    1  1200
## 
## For each parameter, n.eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor (at convergence, Rhat=1).
## 
## DIC info (using the rule, pD = var(deviance)/2)
## pD = 3.7 and DIC = 97.1
## DIC is an estimate of expected predictive error (lower deviance is better).</code></pre>
<p>Check mixing/convergence of betas (intercept and slope)…</p>
<pre class="r"><code>traceplot(fitmod, varname = c(&quot;beta&quot;))</code></pre>
<p><img src="data:image/png;base64,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" 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w6gMcfvSYCKrY94AtCFlQBQvx7QyDOl4LNFALo3AJ2WA2jWu9Riujqgq98B0HEAup6ZKwAqATS16wO68i0AHQeg6wHoEICmBaDlAtB5ALq3GEAFgG4vHUCXvwmgUQHoegA6BKBpfS1UAGiZAHQegO4NQKcBqLcw718AWjMAXe9CgC6tIYBGLR1Al78JoFEB6HoA6orcJAC1AWi5AHQegO7tEYAKAM1ZGwA9rqsC2r9HB0DtlwAKoKOFef8C0Jq1AmjgoADoUgA6DUC9hXn/AtCatQPo6lEB0KUAdBqALi8MQGsGoOsBqAtAEzsfULc0AK1ZEUDTpxmA7q1VQAWA6pUA0FI9AdDUeXZzQAMzs1QAOi0SUG/lABRA9wag6wGo61aAurUDUADdW11AV3e1vYUbDEATA9BpADpaGIAeUglARQDQjW+40QA0MQCdVh3QNBABtFSNA9oB6EpXA3SyngAavJrMADRexDMB7QD0uAB0bdAJoMvb3hig3fgaABq4mgRQAN0fgK4POgF0aeMBdJQ+PkKErpvCCoCur01wWQB6VAUADezStUMuPED1QgE0saYBDR2XFIZSAFUxgH59KxLQ/tIZoNGr3gqg4w0eVQzQ8VUBdAPQ2fd3AzqfqgvDjm8HoJGAqsVbxCz3poB20YB21wJ0uPkY0O4AQP3rAugmoJMrAOhs4S0Aqk9hb0UuD2gYMLfXHwuovT2AHth+QPUps3wLAK1WMqCRp7+7GoAuvI0pet0BtFwAunQ5gO7rJoCG1wlA/QC0wQB0fdBkQM1VADQF0NBKAagfgDbYRQF9XVQeUG9HpADqvmOuA6C3BTRaUAAt16UADZ0pDwDURykB0PHrLpcEdLhrHwvowjYCqHddAC0UgC5dDqD7Kg+oGF8tAtDZRUuATkeNBNTOCgB1AWiDAejySAC6E9Dp8xrzFe8HDADaAeg4AG0wAF0eCUB3AzreKfMVD/HVHqBft7KAxkw7AC0XgC5dDqDxjYeeMQGgenUqA6oGQLfnHYCWC0CXLn8koCmn1PJ6uXWvAKgYXQ1A/bfNeYCKaoBuCAqgDdYIoKFJeQVAR+aEAM0SFEABVLYDqPROwvl1ARRA5RTQDkABVLlJdyCg/jLaA7RbuO7TAO3Gv14BoMOXuwGdDbKB2FoA+mBAh4W0AGi/CgA61BSgy0NfEtDFXQWg/r+3AR2seiig9hUCb3VXA9BT2gVo/9UOQIWYArrB2pUA7RYBlSln1cJ6yXqAKr0tsmsPUP3FtQAVAFqoOwOqcgFV5kwD0IjiAJ34Ug1Q+xWABpoBunGbfEDnhwNADwxAl0dqAtBlg84HVI3+O1rIowANzL8FQIOTdQ+g08MxB3TxlAbQEoUAXXgGT+4HVE/MewEqR4+hg4CmCNotWTYA2g1XA9BzAA288inGggJodhcGdLwTSwHaAWhcALoM6OJtVgAdr+TIKXFPQPtDCKAHlQhodwCg8xOuc4uTxwNqdtADAPW2dnr91gDt5nNuFVB/LQHUvyGA7q9JQLvJ9YOAZin6WEBXR68J6PR4uivvA3R84CsA6lkJoHYdJID6NQFotw/QDEHzAR3fsF1Ae2OWAF0ZHkBn4ysAnQWgk+4N6OoEiQFUrADq37IooLMNAdCdgH5dBUABtGZVARUJgA6TPQ3Ql3SrW7c2DVoF1J2Y4/UB0GxABYDWAtQdDgDdBaiezIsLXwP0Na3LAPr6ZguASkuYvsoKoOZKa2srpifmTkCF8M9KvacOAlQB6LmAysKA2n8C6KQSgHajO/ZeRwAa/JXjpwPq3wXtARUAKlsEtCsHqKgEqFk6gI4rCuj0+FYA1B08AE0DtN8MAF0DdPrsCYACaFTXBNSAYQFdnZrpgA7ePBZQs4Y3AVTEAiqGlTLXXAVUTq+8tPoAWioAnS7FzsEmAXUTD0ABdAao7GfbCqDetrQE6IqTAFqiwwD1Hxu1D2g3BXRltvRbCKCj3VEMUHE9QL2ngBoFdFheJUDXl5lb44COT+mKgA4zMxFQOZxyAAqgzQA6e4B1JKDu8mRAhxsC6P7elNoP6No72q8PqBQiC9DZrrKbkgvo+BCN9koGoAvr0DqgUr9nbVjEZLq9VqYgoF37gHZuxUb/BdBDaw9Q77iY69cF1LvKgwCdrcQpgAbel7oKqFIpgPbbNR4cQO0N17GbvfAEoCs1Dag5H7YBXZubcYC6LysCOthZA1CzCg8B1H3+jH/9pwOqdgE6PQoAGlsbgJoLNgAd5u4VAR2utLKyADq52qMAlf6v3Q4bdRig48MAoLFdC9DOB/Q1SV+zvGlAPUbqA+ov2wdUOzEGdH6sqgE63uQ6gL6cMCcvgALocb2Zs9EGoJGA6n9GAOqte1VAB/rshR6gYg7oZJ9VANSu/DKgwr6yvrwzkgHtZC1AlbYZQKUP6HBcAXT9TBntjDVA9aR6HKCdLA/o7Cwe74FhDc4CtD81WwZ09E6t15yaXS0DUPUUQL2FAmhUtwd0+XBuAyrtWjUPaDc8fD8C0G4T0K+FpwM6XZ/TAZXu195zAR1td6uAujsMo+8CaGxZgKrhCquAGiD3ASqEf6BWAZ2dfWtb4H9j4cs8QAcWNwB1HK6UDaj3+6Zy2FknAtrFAjoMsoDRGqBdEFB/vZcA7W4KqL39DNBuA1D/aQ8ATa0ioKIEoJPPBwLQRUDFCFB7ozxAhVvPhWnRVQR0vGPuDeh01y0DqtIA7QD0+AKAqukbbY8A1DvzrgSoPblCgA6yrdQIoKI6oJ2+7gzQydWER9VJgKpqgI733Rqg/iIBtMVSARUrgIo1QIcTpE1A1R5ADQIVAe3kjKB6gPYbUwZQ/6jlAmquJVMAFW0AqufkKYCaIwigx5QMqFgA1M6qZUA792+7FH0+qGsBqqyO3pOB7QJqiOnPSrEbUDsVzHkeBlQAaD6go30AoH0PAtQ7wOYJLB9Qe6gsoMIA6iYlgAKodzVzLXkmoGZaLgEqxHTe7QR0CmYBQPtTBEArdhSgGku1DejouAAogNr/jwd0GK82oJOJB6ASQOsDqsy1zwXU3uREQKfr1jygevEAOuB5BKDe/WlhL64EqBsNQGN7MKBidpXh9DoI0PHKnQdovwt3A9rv1HMBHc2XFUDdrM4EdFgh/1/eOtsdWgBQN+3sLrI3HqbGCqCW2ShAh7kJoIllACrVgwFV2YAOP+W9tRutVBygKhtQvcOWAe2OANQcuxhAvWvJgwC1LwwNax0DaDe60QDowKs3ln8QR7vOAtqNdsICoGYODlOjOwxQ5Y6H3TYAbRJQMRyXGaDdNQA11y4OqDmTUwC1rLUBqADQxX13OqDSLg5A0zoHULkPUPfvJgHtp/ceQEUAUNUyoMMhXwRUAOiwQ3MB7Y4F1N50A1BvgIsB+s9vP//Zf/Hx9tVPf6Te/umAjjbydEDt/CwKqLB/G68soPN9e0lAuzCgah+gbg/NAZ2cWicA2r+xcBNQNbrgboC+v731gH6+mQyn0QFoOUC7hgE1Fx4FaLcAqNm8KoA6Bs14jsgCgKoFQPUKRABqd1EcoD7+chFQ7WwpQMXTAX039zqdn8l3QksD6s+YFgBdOZ7FAFXRgNovAbQEoPYA+lPDB1TZPar8qxUA1O5JOxMB9MKA/v3r29s3+Xocb+56/vt76n3QN/PWOdt+QL3ZKeQRgIr5mxqHrSkCqP7GfkDNV96+Hq3UMqBKDXN1N6DDeXc4oN6j3qKA+isbANSbQpcG1BwAAC3R1x3QH6//fmhHX33dF/2esggLqN3sOwM6+vJ4QN0gw0o0B+jrv96VJ/sxDlB9P/d6gE4szAPUOybXA3QY/hmAft3f/Gb+O9ztfH/75a+EZRhAxXRH6YOfBaidu+mA6hN8DKjosgB1Do7UdF8rcQCgKgzoTKDbACpbBdQeFzvk8PNZmPFPBdRDyVyrNUCHw3EHQL8euf8w/x3Q/Eh7DB8AtFOTvbEGqCoKqFwG1N43HQGqT6RlQJXZiPHdTvvlEqDeqdcmoCoAqGPzOECn50oyoP4gAAqgKx0B6Dd34WcI0Ld5bQAqDaBKyd2AWmvkEqDmiq0Cas/HIwD19hmAeoAKbwIEAPWW2xigEkDj+nro/t38tyFApX2yJAyovkIdQLsLA9qVBnTYivYBHfAIAGoO0wKg/TbcD1DlHpicBaj7BIx7ASrfDZzvo4fwpZ4DXQRUCu+tSvGA9qdWGNDXVdUhgHadA1T521geUFkZUPPlJqDynoC6x9BZgHpr2ziget7WAFS/6mvHDQAq1gD1H3XqG7zJwtUD9NO86/PvX/VjeTncK43t6y7oBFBpCYkCVF0S0O4SgFokg4CqMwH1z/bHAermWhag01OrKKBOzSMAFdcF9PW2z/4+6PDK0XviO+nTAFX9xNgAVNp9XhlQUQ/QrhPtA6pPpX6/TAAd5nZ1QH1Bldth6YCKNgGVdQBV81PrioDqsS4MaP9O+v4hu3Hz9RtJSXdAHaD2BDe7Mw/QHsmagAo7Mc31DwRUtQmoPBpQp8LVAfXvL+cAOsy1IoDa8yAOULtTAXRnvaCjvm3fyG8AtN9z+jQZpsiRgOo7VACaBqg+WaeA2vM6HtBuEVBzpQYBtfztAlS66XUlQOUOQL2drPegB6g3/GMA1b8O7/Vj+xajWgRU2HWpB6jQf2/xBECFm8BTQN0dqjVAlf38sRCgw6m4Cej4tDI/u7z3PCnHbCqg4uqA6t37AECVBdR853mA6t+E1yW9/q4LAdolAaqP8QGA2icZTgPU3Y31ANWbkQKomr6KPQJU2fVxn5Q3AGqfZykPqCwJqDusi4DanxEbgJrVNwtWXRcAVM++aoC6WTcsrzygo9flALSv7Q9UjgdUVQPUnbGFAVVbgPbfzABUhAE1194F6HAvsyKgdr8B6DKgMgnQzv0WsgeoPewZgNqfxIuA2mmRAahyRwtAd3ccoMrO7xCgr9smASoBdBegw12nbUAdQhagWEANUecAqtx+jQHUvb/TA7TrDgTUE1Tv8jhAVRag5l4NgOa3BmhnP09ouOoMUHtaNQ6om5ElAbX3frMBHf1Kl7mkLKCi/2S5dEBlNKB21tj9exqg+jv7ALXzvlsAtLsmoPatf6mA2seCEkA3KwOo7OyvDNoLNgDVe/4oQN19vhCgxsApoMM8nQCqz+IdgIrxObQXUD0KgEYCqpoEVMUDqjr7k70CoB2ARhYEdHyKxwNqJtUSoEY0fQa3CahygMqrAur2eRhQNXr1w244gCYB2mUCOjr6YUBFMUBVPqD9UgB0WhhQ0Rig8jBA5TCILASoOaezATV3W1oHtH8653BAhV7X4U6bvfHNADUTax+g/lMYPqASQJM7CVAZBah9DD0B1J5aUYB2AUDNTDoA0GFGng2otExcDlC7V9cB9R/12hvvBFQeAah9TnMLUFkHULtv9gFqtu+hgNr5agDtdgDqTpBtQOUUUJkNqLeiNQHtN/lqgKrbAtq5t0O6OXNxQP33MS0CKvcCKs31NgB1J0c8oGaqAOgmoOYMiAVUf28AVO//0oC6NY0CVBUD1KHZAKDSbvDFAO0HOhnQ7hKAyhhA1Rqgdo4PR2sNUHvqtwbo8FtDC6X+TfcSBQBVvTPDVX1Au2Gn1gdUz4FcQIfzEkB3ACqHE60GoF0tQC1lwzbZHXkTQLtKgAq9WAANlwyoe1Z9BVB7hJYANUc+DKh6OqDCfqaAD6hB4WaADgB2g4bDGtm9sg/QriCgSm0BqszlBpk2AVV2xypzBrpNAtDEDgPUPHu6E9DX7W4OqN0vuwA1y6sHqD6tR4CqEaCyJqB6NpmbCFEHUPdMldk7kYAaxTIA1SdABUDdULcAdO3zkoJ/zKhaBwLatQaok9P8JxlQuQyoPd02AR0Lejygwp359lxPALQLAWoXK8sDauabD6g4ElBhruatZjFA5X5A+9OsHKD6PwC62tubsH+P/fXPKEBlAFBzYocAlXmA2jkp7DkAoLmAdp1WzgNUWBd3AaoXtA6omQjmww8vAai5f3E6oOaedhhQtQCo6NzHum4A6v5/BKje/yFABYBeEFBVG1D9fwOgMh1QS4x+B7Q5bSeADudVo4AKmQJoFweoiANUmc8O9AAVOwEdduSRgNoNdrtqmHdyA1C9hf2unQE6bOsWoGI8x+UcULvf7eGYAeoO4pmANtcKoJasREBlAUCF/S1KH1BxF0DVHFAxnGRXBdRcqTCgSkUAajY/EVB7b6o0oMI9EWmWUgZQWQ1QNbwoK90xWwPU3pUF0KExoELsBHSQsCCgcn7Sda4AACAASURBVANQ/f1VQI2EomFAlb7E4JIFqDIn3G5Ah81PAVRYQLtkQO3PjQVApWwNUPc4OhJQ6Y5tDqBmpc8CtLPP4QDoalNARTOA2gdZcYB6a7oGqPAAVVGA6lXTK58HqDoIUCOCMoCaEQZAvS26EaDWl3KAilxAhVuTCED1ZW7GqoYB7br2AHV/StP07++nPPups4AqH1BDTC6g0ptUpQAVJQCVyYCaD6moDqg+mXcDKqw7eoRkQPsd6QbW59/DAZUnAOpztw2oOzpBQP1pGgDUbl3DgNq/Auf+fGazgIrhZDQTagtQvbuH8+VCgKpzABUWUPfZGBmAmutbQO2uPwNQGQGodPdsPUBlMqAqBlB1TUCNRUUB7QKA+mePD6jZxzNA3Sc4HQ/o8Fc07Z3QVgGVI0A7Y9clANV/rdVsyaGAqtqAKvtkZwhQez9hP6D2PCsGqP7nFqDurnQbgFqwEgEVjqZDAVU5gNqVCQLaOUDtqultPxDQ1+P31xuZPgZBWwFUjQCVsgVAVSagahegdnbkAKpGgNoTLA5QtQmo3AuoSABULgKqzAFfBVQ0AKhzC0A3ANVvKwgDKiaASvu4/wRA399++qP/4otNI+gFAO0qAWqORD6gYhtQtQRoVxlQ6QEqxJUAVauAWqLCgJr97MZsAlC76DhAxQhQCaABQPWGHQfoF5buuc93I+itAbXQrALaXzcAqMwE1Cqj8gBVc0CVnZUFATVY5gGq99gUULULUO8Xsc4E1J3nTQGqbgSosL/rbgF1jzV3Afrl53G/iWQEbR9Qe6imgNqTrg1Ah5+hUYCaKTQGVE8JZTZsEVC5Bag7D8aA9pO0FKDuMrkL0OGx9sGAykqA2gMBoFL/KBcG0O6OgL4E/dYAoCof0G4FUPcrilcEVJwOqNup9hQ1gJrNzATUnYQAehKgnd2Hw/9LAN1qHdDX86DfmwbUnQhPA1TPyCMBFWNAu2VARTSgSgNqnqloCVBRGlBDhF09B6jeA80A2tljGQLUqCfGT9TUB9SuTghQe36fCKj/HKj+59uPiwGqzHmzAKj9CRULqHmG5lBARRBQtQNQlQ6o/l8QUJUMqNgDqCwLqPNmFVDnUQygoh6g5oAmA2o2Jg5Qe0wdoGaG1gNU7gXUbl8jgL7evuR/HNPfv7799P+1COjre0mAijVAhQO0mwPaRQMqNwEd7qvtBdRabwCVJwNqTslrAurYmAIqh5nlAHWCFgO0KwqoSgPUzpgQoFa68oD2y9wDqJ3CDlDVAKCv94F+9/79edpn0esMoGIMqDnqJwBqZn0GoJ19hLQOqNB/DX4CqN7aLUDlTkBlLqDmrvU+QPs9Oge0cy/66VN/BKhcB9R+zkQYUHuQlwCVZnYcDajMA1QaRM8F1DzdEQ1olwKotPOsbUD7d9D7D+M/GwBU75AjADUMLAFqbxwDqFgEVNoJtwtQNQK0M4CqPYDKfEDdXYNKgJrlAGhTgMo0QM1FAUDNVt4B0F5Q/z7o6z5pk4CKZUDlEqC+cUUAFbsA7fYAqiaASuNTLqBSeoDaE30RUOkD6r1z3+zTUwC15/IY0K4moHb3ngeoO4TS7ubGAe2OBtROnBMAfb1y9H10wcfjARVWSXlPQCWAJgEq1gGVGYCKYZwagKoQoG5NmgDU7YA8QKU73h6gys0+eQygTVUFUHtGyrsBqq85B1SZM2wEqAJQD1B7cFcBdf9YAVTOAHUn8X5A3X8rA6rmgJpJruwiDwXUPGMVC+gweQHUNAJUFAa0G6a3qgWoXVgEoCIAqDKvstcFVK4DKksBajayKUC9g1sUUMPWxQEVc0ClPgGWAdVnq3k0vhfQLgio/f9mAG3yj8qVArSz9zNqANp1agVQNQa0M3/aKwyo7H8ZXu+DDEA7e7fKA9Sc9CoEqNwCVOltXwdUTAEV+YCqYe2eB2i/jjGA9mdBC4B2DlAxB1SGAB0d0ARAlZ7gm4Dq/QmguwFVSYCqXYCqNgBVbhBz0uv94QC1J0ECoPIYQPUmeodrFVBltkiIEaDyaEDt5R6g5peTKgBqd3I+oG5uAOiu7gaoLAOoPn9bBNSdmZqaNUCNTiuAKvv0UjKgcgKoPhpRgKragIo7ANo/G70AqOgXVQ1Qt0fngJpdqmdjGqBK2xsFqNoNqNnLpwK63imA9ieYPoZZgKplQIWwZ4W++NKA6hU5GlB3riUC2m9aGUD1spsD1B3xZUCtCHoqmZ/hVQB1E9N7VmobUC1VAUBVEUBHP6cBNLE0QM19EgCNAtSev3IBUHEcoHrtTgTUgCftnKoAqDoW0C4DUJkEqN6EPYD2K+n9EI8HtFsCVALoYtmAihmgcgVQO9s795tMMg5Qe+cpFlA1ACraB1SjOQK0c0+SlQFU7QTU/tb6bkBVNqDy+oDqGZ4MaOcf5TMA7fp3MTUGaHMFAZUWx1KAGrjkgwBVq4Dqc9UDVIYAdQeoIKCDAxcEdDhMpQCVq4CKfYB2M0DtsdVUiW1Ah6OWAehwn7ZzT3nGAioBNNgEUDNBPECH82kLULPDpQwBqmoBKqMBHSDfBlRGAyoTAdV3KDxA9YruB1StACrqAtpPo8qAmhdORJOA6m2X7hsbgHYBQGUUoP0F+lDEASoKAGqPUGffQuYA1ZcAaHVA7dkQAFTfYgqodzdkG1B3TPMAdY9vNwE1p4oFVIQAlSNAzSwMAGrsGgMqigFqHbCHYAao8AE159GdAHX3aYsCah+QRQKq130JUIdxE4B2nRoDKqaA6h/4ZwD6z38/73fhowFV1QB187wwoOYuorFAbgFqJvhOQMUqoFJvXRKgYgqo0lYFAJUtA2q32+yNDEDVFFDhnNAHcxlQ4Y3lAdoB6CagWtDWAP08/eUj3YGAmgNeAVAzSruAaq/01o0BVauAdsuAqlqA6m8EAZW1ARX1APXvhaYCqonbBFQA6GGAvp//+rtuC1D7tM5eQO0RXQXUe5S3F1A95fMBlQ6xFgE1p769aBVQtwdmgKp9gA5LuBGgZkeMABVuLztA3SJngJpTQh8TvbkjQL2X5G8JqDgU0NdngPqt/XZS/SIAFQUAtafJNqCOydKAqkhApQwA2l/9hoC6e1ZhQKXZn0cAqrdOD58NqJ4UdlCDWySgw0vNci+gQpgX15Xbw9uADi9IrQMqFwCVc0BFJUD7SXEGoPrvGX/0n0z/fqafA6B6nij3uN09hJ8DKhcBNZNOL1Yf/jCg8hBAzTReAtRN2TRANUWGyOYAVTsA7To1B9QsPAlQfRQ3AXU7OwtQs235gHZZgCq9IHuDAoDKEwB1B3URUHe9OaBuYiwA2h0F6L+/v/3yl/vt9zM/TzkCUPtJt2cAalTJANR/BjAAqPmp6dhYAbRfqysAqlRFQOUUUHtHLASo9ioPULO3twEVpwOqd1QKoGZXbgMqzPpFAerulRcFdLgz1Qag//zWfyb937/+9IfsOW3gIXxxQLuGAe1PhmMBNTtmclbeHlClD9bFAVUBQK1osYAqB6g4HVC7B3YAKk8C9MfwH/NQ/pwiAJWJgJqjtQioOhLQfqoNgCoHqLSAmvXYAag9VZ4FqN7UpgG16389QK1+zQNqn/Y6DVD7x5H+/vX1gP6cfECFkCuAOgkcoJ0PqJoAKrYBNVjEAqoAdAFQPZkvB6gsAqjcAnT49aplQPWy0gDVj2uqAmof8bkfNKUBFXZDwoDa8zkEqN7AfmFnACrf9V3Pv39t4G1M+wGVLQEqywGq1EUBlRuADgycC6h7utwB6tZAut0TC6i973QsoLIcoPanxwhQGQRUbABqz+/6gH4t46AXkfq7nh/9a0knfZSyLgJQOxNTADUC7AZU2vlqpRoDKg4AVNp1Ph1QmQKoWgRUzAAV5wA6LDIR0H5q7Ae0XxO3KSNAzWaXArSLBlR4MyQLUOm9tWsAVLpn1YZJWQVQvVaHvI3JvPD++da/imQcPaUcQM2M7H/mRAJqp1I0oEpjtwqoVqIYoHZmzAB9fXEkoMPYOwG1O88HVLhdbYbaDaiyytlXl+IBVQcDarYvD1BVElC9Lh6gBqcJoF1xQEUsoKKzH1Ih2wT07197Or/+80Xn59u5LyJJu2PWAZX3B1QvYAyo3kUXAlQaQPW5nAWovx4XBlSuAirdnUB1GqAiB1ARD6hMBFTpKWu2dgyoGk7REKBm6h8EaP+rnN+H3+g89W1MHqBqDVCpn+LYAFQdBqgoC6hYAlSZXVQPULui8gKAWtvOB3T4/kUAHbhfBFQCaLglQP/9vVfTfED9eXdAA4Dqy+IBFccCKqsAak8kOQZUlQa06yxm2r2jAJUGEh9Q1dlXGbYBdVcsCqgYOzYFVO4F1N6mRUDNyh4OqFlPD1D3vPQVAJXys3/6094VPa03a9FOQM0JOwFUH9A9gMpYQN2q6qmeBKi9fANQ5Sa0A9TM0BRAhf25kgmoXAO0p2YVUHNRIqBq0HkA1G4qgEYD6i7q7C9+ZgNqJ9luQL1RNgFV9rX7dgBtpB2AqhCgIh5QtR9QfXIoe/czEVCRDKibrIUANcuIAFQvIwVQCaBPAHSYk+UAVQC62bGAynhAzQl/DKDyfEAtKIcAaocaAapWANW7eAqo78gGoKI+oOYit7gWAB12ZAhQdQag4uKA/vObfvxuej/3bUzVALVnqAXUcHc4oNIQkw2ovAig8uKAig1A5SGAdtGAOo8soMoc/hCgQr+18ihAzSl5c0DPfCP96//XAJVdZ3/ftRSgYgVQ/4dlRUCVfZdgOqBimNBu6nZqBKh0sx5A5Rqg7qFIJKByGVA9Ye4MqNmiEKAKQPuX4k8EtN/c0wFVq4Dac6UioGZqOEAtgWYXTQHtx7CAyhCgcgaoEHbYBUA9vPUYuwBVIUDtaZsKqKVsH6CDYbIKoGb3lQdU1gdUuNcLHKDCreMyoGIM6PCj+I6AfrwtdeJDeACVnXsa4aqASvvUSAqgnXmKehVQYT59Sy88AKg+DDsB7TfXLG7YE5UBFUmA6uMUB6hcBVTGA2oumQGqmezMlhQF1F4WB6i+8ZGAmnd+TjrvjUy7ARWrgLq7ONcE1AlaAVBzP6QEoO69mguAjs7kywBqMSwAaD8p1HGAykRAO/fy6jGA2h1xYUAnf47zbD+ngJqjUQBQc+vTADUHeQyozARUzgDVRHayKqDyUEBlBqDuMfcGoKqzH5UBoBUBleuAqjmgXWff13AlQF9NXkQ6s0VARS1AxQjQLgSom1EzQM35ng6olCNA1RKgZrJKGQRUb2wKoMqdHgUBVe0AKs0DUrPKVwW0G56K3QRUDYCqAVCrpd2R5gwyMyAX0NeX7QCqANRVCVB3a3MOxQAqcwFVhwLadcNHHU0AFaUA7czvh2sGWwLUnEPrgA77qxygbiKcAWhnHm0cAKiQOYD2t9gC1Aq3BKgqAKg5enYWF63tN9LrnxdmtxYH1FzvYEDNaPUBFXUAVa0CquwH/9UC1J3iMgyo6Lo0QA1DOwC1e2QA1L1VbQNQ4XF/MKDqxoC+Xk76uiP6cd4r8K8ANB9Qtxq5gIpuOO8qAqpP+qKAagnPB1SdBagMAWrm6j5A1X5A+w+XGQ7OLkCNlM0Aql+NfwF66p+F3wGoPAlQmQOo/m8VQJUQEYAO9vmACuvBCqD6VmZ6d94rUUcDaoBy27wEqDwKUCXEGFBxBKDmcCwDqg4FVO4G1O7yywJq3s30Bej7qR8HegigyjAi9JlYHVB72scAatZjC1A9ktn4eEAt5quA9mdJFUCVD6j98bUGqHttyNx6uL81giEdUBUCVG/EbkD1nowHVNqpcgFA9SUFALX7+SaAvj4O9Je/9N+F/xL0vF9EOhpQex7HASrdFW8LqKoD6HA2bANqn9xaBFQZsKoBqnYC6uaUB6hsDFC7touA2kNxDKDDV+rKgH727/3UgL4exJ/9RvotQJUDVNnzZhlQPYnKAGrWMApQdVlA+3meBqi0b7BZAlQ2DKjZFT6g9sWNLEClfkizD1BZHFDlAap3bjyg+lwwey4NULODPEDVcJ8mCVClAVVBQPsz7yxA9ecvGUC/7o6e/aucFlB5X0DtHEkGVP9nN6Bu64w8agyoKgPosMYzQOV+QC2N+YB2i4Cas1mTciygRqTrAiqzAVWbgOrZtw2oPdfsvi/c6p81NoDav9G5K/2kajrEBwEq6wKqrgqouV4rgCoATQHUDjZ5rli5RzxzQKVbFwuoXdgUULkAqF1dB2j/bXsPZQyoPo0PBdTtu7ItvZG+f93IApr7d+G/FmPFtH+dLvnPK20AakUyH8kQBtScOQFA5YmA9gtbA1TuBrQ/J1QKoHIRUGVOgxMAVe6G24DaZyLcebUPUH+qLAGqZAuA6m0/DVBhf85lAap39CKg+hVb97qjltYBqiygcgao8AE1Z9wlAX0ffq8+8U7oHFCrTS6gYhVQM1IQ0OHMywDUTrUBUGkfJU8AVZa26wNqz6IUQN3jxmhAZR6gcgBUHg5oFwWoxiABUHu1NECVjAVUngCoPm0vA+jkIXzuJ9I7QD/tB5K8Hsin3QddBtRcJFMAlcuASvuuEjPgIqCqPqB6YWuAdvcFtOsyARXW3jYA7fTvTm4A6m4/B1ReFlC1AKhQbgd5gMpsQO2PqThAzfacBaj5DHoD6Jd6eX/X2ALqfyLzx1var9lvA6onkPnCHt0ZoK8CgA57dQegagSo3ALUUHdnQN14M0DNyvffdx86J+zZuA2oHS0dUDV6+FIVUBUFqNuTJwFqR54A2kUAKodDtgConQwPBPSzv6OoATV/Ij4jC+in9z6or6UlaVwfUDNd7IDnAiqvBKg9CHGAqnRApVvJtgB1R0bGASoLA6oPhFlmFKD2CM8BlWFAVTSgr5vvAVTWBNScFYcBqt893wP6kf+B9BbQ0b3Oj7SlVQdU1QRUmley1wCVI0CH+xUtAirHgHpPRA+AmjW7EKAyG1CVCKhdchBQaWfXWYCKmwFqduPhgI4+mD73TUwW0Hcf0OArUkufht9/Yw6oOR0LAGrfAiXNSLGAuuleDtCuGKBqwMQBKtYA1fd8zObtA1Sj4tYwF1BRHFC9b5oBVG9BXUDl5QGVlwW0f+C+008PUG8RewDtv4oG1OzkioB6A44BdbMiAKjMAHT0glcioELuANScjrIBQPXBjgdUWUC7dUDdESkMqJ1NUYDaCeUDquer3YsxgNqvh9HyAZXVANUDbABqN+aSgDpCd7yHfvEhfOJ7oqIAtc+H6wv2ASrzALU/UKMAtbN7H6DSeSKXHsLLfYDKCaDdOqB6FaoAqtzZK4OASgeodmAGqCoGqDoGULEAqDwIUDkH1N6zGAHqtv4KgMqjAd2f9yLS8CpU4nui9gJqJ+KrNUBV24B6s/kegPbApQAqNwA1J1YSoJa0vYAa/PSJ6gBV0YDKMoCKTUA9UXxAzaotACoNY2uA2hl2LUC77oA/6VEoC6j/yvtn4ieTtAmokScNUHk+oMN9bfviUT6gqh1ALRZpgDp/xPCkitk/SYB2M0DNFzcB1Jxa+YDKYoCavfUgQPXf9HRqvp4WSHtOYAyoBrFdQEcn/ARQ2SSg7hfztgHVt2wNUH1ydhNA1RKgXTqg+uDcAlCxCKhoE9CuU/GASjNhGgP0718LPAc6fiWq/43OtPeUTgG1O7kIoHIPoOMBtwDtLcsD1M74OoAOp8N+QO3SLwKoiATUzYUVQLvCgPbHIwNQezDiAR1WTZ0JqDsZO3kbQL0/D7/nL3Q6Q3tAP9I/3b4GoNI/Zs0B2pkHjnsANVhZQPWGrwKqEgFVFwNUXh/Qzt43tRuVAahaB1TKaoD2c/B5gH6M3km09096fN2ZNS/HJ/9O6M0AFfUAFeb92TIfUJEAqKwNqN0PuwG1d4IKASpnLyJ13aUBVe7UKAqoO50qAKpaB/T18N08dve+PKMZoPbgmCOkzgZU4zkGVAUBlRNAhf2ZsAqoPARQGQWotGtWCFCzn+2yKgIqJ4Daj3tX5QB1P8/3AipngCqzu+IAVd5oy4Dak8IcvIsBqvdgu4C++/c6P9I/xbNcS4AOzztLo445JPrftwZUxgCq8gAdWIkB1J50Mh3Qfo/Yo+cANasQD6gdopsCKsQSoGI3oHIZUL06cgegbsPtnF4A1FKbDaiIB9ROpBmgg5AA6lr8ODufzNyPsyvRIYDKZEBH51pVQO1JEwOoveMQBtSdZmmA2n10CUBFKqDm6yCgMh3Q1yFXW4CaNToIUD2tSwMq6gMqhRgBqtoF1Hygsi33A5VLlAmoVaCzv+3xagegek5Phj0OULMttQHVq/wYQFUpQIV1000o+8xSPKDKzo1hmWFA7SIDgOpJuwCoigfUHqQgoPbvv6wDaudtQUAFgEYUA6g+JcoBKt1yOv2alfJnixvWB1RWANQQYQE1i10BVDpAX6ubC6iU+wAVewD17ogVBbTfu3pNhAeo3AJUr8DDAXUHqQagchlQvXuuDOjXg/Zvq/86tt2A2vNKTgE1B2MGqLTLLQaoTAV0RMTRgKrzAFWVANWf32IOugVUXRpQdSKg+vkJ75Q4FVB9b2MO6PCa2Tmvwg+/cPm5652gOxsANdN4DVCL4Dqg3pNfPqBm3k8HPhhQu8wIQN26uqmvV+65gNpt8QAVDlA5AlQNgKogoHIBUP2vBUBFHqBdp44DVHqAytsBamaZNPNgAVB1JKCv99EbNb8ezp/o53mAmrOpPqD6IJsB9RLOBrSz7xS7DaAqBlC9ZWmAurs2DwDUzOajARXlAO3XvCqgow9S3v+RyvtrF1D3QucIUHPw6wHqrWsqoCoXULEKqHsaahtQ97sp+jvRgCpz9rYCqDCvPk0AlVa4SwMq0gEV5jeFNwEVU0D16AuAuvOyP+Tuh28WoPrkMoAKAI0AtPM+96gaoPbTenYBKhMA7S+OBtTOuNsDKhMAFfUAtc+t7gVUWANCgCofULPragIqATTYcwFVhQFVDwZUX9EeA7vHTwC036VqALTbAaieGwC6AKiddR6g6jBAzf9HAKoOBrS5MgHVkK0DqifQ3QCVOYDK4f6EXmouoFKeBKhaAbTrZBBQ+/OjLUBFDKBuLiQBas+jWEDtTDsBUAmgZVoD1B0gAJVm5eoC6saNAtTgFgeo3AWoctviDlNTgKopoNKIOAJU9WushHkgUQPQ4Tw6EFB7gwmg9ofmCqDuYFcA9A1AAdSug/0/WRFQ+9C8FqDu/1sA1K1QGUCHHbMAqJoCqsxJ35N3BqDqIoAqAI2uHKDu8Z1MB1S6lcgF1AC2BKiKA1RcHlBv5yYAKjIANcc0BKg9gMcCanbGBqBmzBhAHUP3AHSYJPsB7e/OA+g2oLIaoGoMqPTGvRCg0jwNVwFQlQ+o28nu/5Wh7QhAhwNon1LfAFRuAKqKAWomtd78XECH0VYBHR2KMoA6wcKASjlacwCt2QjQbgFQd0pMAJX3A1TuBFRmACrrAOrtZPf/A6BqN6B2dwo9YxYBtcjsBdR/f+l1AB2Od0FAhzs6AUDtIuMAteslswBVAPokQC1OkYA6uNyG3QJQJdMAVcruOgeoW7YPqBwBaq5SA1B9t/1kQO0yKgHqZt0MUFESUL2IJEDNBPUAlQCaC6i4BKAqBlARCag8AFCZDKg99xZ2svv/AVB5JqB2G/cDKlcAlQUAlTcB1DupNgG19zAFgMa3BKgP2QxQO9PbAtQ+B7kGqB5wHVC7tPG6VgRU2UWuAyqVeZ7pToDauXQZQM1OuCGg9qsJoO4ssMc5FlAFoMuAml19LqD2obMB1F51cGMVUBkAVAzbdSqgVhC9msqdthpQ8SBA+20bALWTsT6gdk6779UFVKwAamfIFFDzvZ2ALk4SAN3TBFD9fwcCKqsA6p1M1rLCgEp7H7ggoHa/xACq7CCpgOqVLg6orA2oexjUHKD2K7tzJ4C6x3AAmtltATVgjKBJBbQ/TgOg/rADoOZ6y4C627YMqLfdPqByhKDdLycDas+YSECH+/sNA6qP0mGAjo7FIYAKAD2nfECVSgF0PvI2oOaUqA6o2wMHA+rWbgLosOKbgLprRwOqygLq9mAAUOVOe7c/XwfBPe9YAtDRMb8eoAJA13s0oO6snOUBai+qCqhZ2AxQs4R7A6rsLr0MoCoFULsgD1BZBlB7JKoCaieTt2fbBlQCqK0uoGo4K2fZOwkAOgPU2/U+oPpclWmAug2ZAeoNbmlYA9Ts9U1AHRbD8GcAKgsAKv39+RhAlQTQtNoFVE4BlT6g5roAGgbU35vlAB1WdASoHLBw2yLNj7t1QM0m2AEANAVQYR/zA+hJtQqouYoHqHs0GQLUrmUNQGUIULOoIoAq962SgJoD2Z8suwD1DiCA3g9QPd0ANLJFQMd3Be8LqFNQrgI6Xvs4QN1JmgXosMPWAO3sO792AyoBdAeg9mozQEe3XQFU+IcdQEM9AtDRI92CgPbnQBagbnaq4d7gAqB2AQBaEVA5AVTtANQe0VRAPSjqAjq5bQygZiJuA+p+FLUFqH6FAkBvAahwtxy24IaAGhkuA6h+a1EdQN292hKAqkFGb/8NgEoADQEqxFMBFVcA1J7Ka4AqIQ4B1D3qygV0+OcRgOotOQDQYbrYfWCPlooBVG+cwa0hQGUaoOND8SRAX2MBKIBOt91b5wRAzZKuDagsAujA2SmAWsDsPmoD0GGKtgOoWgVUhAGVUpkzEkB9l5oFVM4B1YYeBKi97RzQYb62CWgvyfUAdWOWBlTeCFD/x7fwFyErAyoBtDCg9sInA+puVB9QeRVA9aQ4AFD5NEDlBNDRomUMoGYmA2hCAFoLULdmYz/LAip2AioB9GBAzWpeFFB9hAB0aAyonRZjQPUX2YCujHwNQGfrfDigZrU0oO70KwqoO1OGVWofULtqlwHUrEAcoKPLmwO0310A+qo6oKvlA2rPh/2Ajm8fA+hwNtcH1H55FKD+Kg0XtgnosGoAdGhohAAAHntJREFUuh9Q6RYHoImlAGr/uwmovCugehW3ALX/GDbUX8I2oN6XZwLqr8QzAFWnAzrsWf/yMaD6HbX+SHsBdWtsAB1fGUBDxQNqOxpQDeASoNJf0dsA6l/1sYAOf0t5F6AyAVAzz84E1N+z/uVHAqoWAZUAutJ1AfWGs1tQHtDZWg+AmrU9CVBzCI4FdLzM5gE1eykD0NHx60dKAXS2O6sDqk8QAD2hxwBqJn0MoKH9FQTUrugmoN5mHgjocNjuCqjdj/mAqm1AZQOAqkMBtfMBQOeVB1T/f6uAdpuAhjsJUDUGVNq3NjUDqN2CRgE1u3EL0Nc12wZUtgWof3IBaDyg0p7RM0ClWVybgMogoOaZxWD3A9Qy6F/5VEDlGFB/l28A6nZjOqCyZUDtiupLNgBdWYSZNgUBlQAq7wao2gJUb58loQSgUk+54Qe/OaviAbWvWswD0LKAug01P7PKAzo+8v6+l6cDKlcmSR6g0p7lAPpqmEZPA1SObx/eW3pEAHXbtQNQcxxiAbUL9EScA+qv2hMBFQUBFQAa0zUBHQ1kv5Ler1SsAmofuvn3V/zbB1bYjRgG1CysHqD6ABUEdHrLuoBKjdQGoGp4dULMZiOAVgfU2+1K2Sk7A3R0cgHoVQCdDGS/ksMjvQCg5pwrAKgsBOj0Ct5V3Znfn9oNAurfK7bXPhJQuTD5/FWLAVQdB+iwt84CVM7zrpAGqPsmgO4GdHKlCwI6eqQYaAaozAF0NA6AZgLqbdrKqiUCKusAqnySzgR0sRGgMh9QCaAA+jhAJYCWBnS+O1cA9Q47gAYC0MUAVC5fwbvqKqA9gq9hPUBD667s04X6sMnGAJUAWgrQtUWslw6ot+IACqDe4HmAqmEKDoAOW1IPUOEBOjtO0yVNAR0G3wuoGO3eeEBVy4COd8rrOt6tAdTbOACVYUAnt3g8oHICqKwNqN6wXYDKKaD+2s3Pr6qAugNyHKBmL+4B1LuoCKDjCTgFVI0vB9Bm2wR01n0AHa9PJKDSd+0wQOWVAdWXnweobVjQXkCH1c4C1Ad46WbD9gGovCig67fwyNwANHxKRwCqj1XDgJp/1wPUbduZgJpLcgE1lzcJqP5AjERAvTHKAerfKgyorAqoOwkANKplQEPZybYfUHlxQN2/awE6rNqJgLpL7gCo+/7VAFVidMgWABXlAXVrA6CrjQB9fbV5NvcVAFTaAz8cXQBdbhVQUQbQ2bVPAtTcpASgIhdQBaD5gKpHAzoiYKOCgE5uNsoufT+g3r+KA+rN/+YB7a8lxmdjSUBlAFC5Aai9yX5ApUgBVM4BnaIDoBGAGhMeCeiMgI0AdHbbYYXrACoNoObKwhB6GUDlwFlbgNqx9R6qDqg9hKOjDqCBAHR9KQA6v8I84V4/CAG6vc7NATospwFAzfIBFEATAlBvUVcGNGadbwno4kTLBVReF1C3Vf7azZc9D0D3BKDeoq4GqAJQ+WhAzWW1AZUAutrdAHU3BND5Oq8AOr92E4DaW+8AdPyNCwHqbcfpgOqlAehyAOotKhtQCaClAJWde33pIoCONr8IoKPtAFAAXV8KgM6vMK8coHrNzwbUKemtgXfq1wfUjHk7QP29WBPQ0cZ5gEoAbRvQ2fo1CmiIqGKA9mdV5PFKBFQeD6icfGsfoEaFooCK2R4xCz0dULOwswE1NwPQiJvNAB1/W5ijvrmUJEAX1m8NUP9xxj0BteflmYBKAL0joO5m/rYC6HIA6i1qe43tAqcXPRFQt04hQIe1lWmAyvsDOl61ZgAdbjbaVocrgPotABpzs7sCurHCboHTi+xq1AHULP5sQNfWDEBzAJ2sLoCudi1A4zKT4maAbqzusNqrG3YEoMNaHAvo6po1C6jKB3QyGQA0BOj4yQgAjag9QOX4OczZOVEU0NVvNQ/o1toNl7YAqDNqtBkFAZUACqC7OhXQAIfet5sDNFBNQO3ZZ5fTCqDisoDaywBUzK87B9ScXwDqB6Crg+fUMKDyIoD2N7oOoLIqoOYbANpsjwF0+NcjAbU32Vq74VIA9ceIB3Q2jQB0ZwC63jMBFVNAtxZ7BqAbFwxr1gCgK+sFoJPNXhkCQPPbCagA0PkKyzCg/YqmLfZ2gA5KPgLQ0fduBai8EaD//Pb2PflGALo6eE6RgEbsmcm6yTKA2sUVBFSMP44IQG8O6OSdM2r+VprgaBk9ElAJoAur0q/AeYAOizsF0Ol3jgd0vjkACqC2hgC119lczjMAHV0FQO13jgZ0aXP2AGqX8DhAJ1t3WUC/xFzq5z8TlnErQFWTgE4TyU+BAqj9XkOADiMdBKi/8ucBOt2lAKr/e2tAJ/+6NqD2MgBdGnFYr1hA5TUAHe87AC3S59mAyisAKqecXhnQoWYAlYUBlUcCOv0XgC7dbAB0tkcvDWh/H/SXv4Z/HP0cKIBOugagsiqg41vdBFAVIC4R0KV9GQJU5gE6WcR6TwZUyve3t5/+0F9GALp0h1V/B0AB1GsBtcCS/DN3uphzAV1e6QcAOlvEemmATm99dUDl37++GTfPBHTlOpvLAdCozgc0uCQAbQLQxScHAHSrf383D+P3PoRPudkY0NXrbC5nE1AJoBJAAXTyxMjhgEoD6OzW1wdUyo+v+5E/bgmoXc7lAA0tLQPQflY2CqjMBVTdBVD/JNgAdGHwtZv5tzkb0MAq3gHQ/mH8NwB9FqCyCUDNta8OqL8wDej4+gB6a0D7h/E//28AXQfUW9pFAV05MW8KqH/r6oCOF/Y0QP3Bnwqofhj/BqArgI6WBqDtAzq69QKg/tVXAA3+kdXrA6pGD0oAdHevh/GHAqpvdidAN9d2OwCdLiYL0PGtZ4COKw/o/Og9EtCoVbwNoP1bQgF0ecGjpa0AWiQAnS7GXq5UNKDTWx8L6GREe3FrgMragEau4o0AzaoxQFeWEw3oyoJHSwNQAN3YvuYBlRJA22gnoKGZmA7o6nIiAY1Z8P0ATV2WrADoytUBVALo7gB0PQCNax3QrAAUQBcHB9DUHgeoPZMBVHcmoBtjASiAvgLQ9QA0rjsCujnWRQFdXb8AoKvzJQjo9CgC6PFVBXR7OfGALt8iA1DzHtZagC6uzSgAlamAuosAVAJoU1UENKYcQP3vtQbo0ootfOtmgAYNNF/tANQrGlAJoFuL9q8AoLldDtBxAJoVgALo6AoAmtsDAZUACqAAOroCgOYGoHUC0Mk3ABRAMwPQ9fYD6n0ZD6icPpdaPACdfOPJgE6+d01AV74HoPq/FwXUKx7QIwLQyTdOATR9hd2Np2PkAzpbahlAR3cJAfSsALROQUAz5Fs4fwF0/bKtCQ2g4ysAaG4AWqfA/lQ58oWeW0tvdOotXeHxgM7GKAro+veWLwfQZrsEoFFXaw3Q1W81AejGFR4O6MIYBQFN+R6AAmioyJtfD9BADQC6WbuAzq8GoPsBzV/F2d0bAI0JQPMDUJkN6FJHALr6Lp7bARp1tXEAKgH0wABUAmjs95YBna1RKUCzrjYeFEBjugCguatUPQCVABr7vSVAF9bIAZo4bNwVEq8Wc62UAHS9aoA2HIBKAI39HoACaCgAjQxAVwNQAD0vAD08AJUAGvs9AAXQUJG3BlAAXS0P0IgFT5d3JqAbawSgZ3UyoJEBKICulgNoeqcCurlGAHpWAHp4ACoBNPZ7AAqgBQLQWwO6vtQ4QJcuvT+gEkAbCEAPD0AjB4gEdDkAjVlE3BUSrxZzrZQAdHcACqCJnQZo3ioB6HoAujsAfSigKSsx7iRAc1cJQNcD0N0B6MLGAWgoAI1ZRNwVEq8Wc62UAHR3ALq8nBKLWQxAF5YHoHFXi7lWSgC6OwBdXk6JxSwGoAvLA9CYq1lRygWguwPQxeWUWMpyALqwPACNuRqAxuTOhZYAPWBFSlQK0JoB6MLy9gMaXHzS5ZPvAehZZQLqagfQywSgMu6X0e8G6Mbiky6ffA9AzwpAD+8CftYHtMJKjAPQmEXEXSHpagCaFIAmB6B1VmIcgMYsIu4KSVcD0KQANDkArbMS4wA0ZhFxV0i6GoAmBaDJAWidlRgHoDGLiLtC0tUANCkATQ5A66zEuNJvymgSUPclgJ4VgB4egNZZibo1CKgXgJ4VgB4egNZZibqtniAAOg5AkwLQ5AC0zkrUDUABdCkAPTwArbMSdQNQAF0KQA8PQOusRN0ANFIIAE0KQJMD0DorUTcAjeyxgObdHkCTA9A6K1E3AI3soYDmBqDJAWidlagbgEYGoEkBaHIAWmcl6gagkQFoUgCaHIDWWYm6AWhkAJoUgCYHoHVWom6tA6r/H0CPDkAPD0DrrETd2gZUB6DHB6CHd0tAN5cDoFmLB1AALVBTp9LebghoxHIANGvxAHp7QMusx9YoDZ1KewPQg1eiSAAaGYA2WFOn0t4A9OCVKBKARgagDdbUqbQ3AD14JYoEoJEBaIM1dSrt7RqAlloOgO5aPIACaIGaOpX2dgVAS/UUQKstHkABtEBNnUp7A9DkGzdQANCaiy8CaObYeQFogzV1Ku0NQJNv3EBllYlefAFAs8fOC0AbrKlTaW8AmnzjBgLQyAC0wZo6lfYGoMk3biAAjQxAG6ypU2lvAJp84wYC0MgAtMGaOpX2BqDJN26g0wBNWAiAHh+AHh6AJt+4gU4CNCkAPT4APTwATb5xAwFoZADaYE2dSnsD0OQbNxCARgagDdbUqbQ3AE2+cQMBaGQA2mBNnUp7eyqgyZvd1FEH0MgAtMGaOpX2BqDJN24gAI0MQBusqVNpbwCafOMGqg5ogYUA6PEB6OEBaPKNG6gyoEUC0OMD0MMD0OQbNxCARgagDdbUqbQ3AE2+cQMBaGQA2mBNnUp7A9DkGzfQrQEtuRIA2mBNnUp7eyagGZvd1FG/MaBluyqg//7+9tX31JsB6OEBaPqNzw9AI7sYoJ9fav7yl/7vq5//TLs9gB4egEbfuPDa7AlAI7sUoPpu59tPf/z961ueoAB6eAB6xQA0sksB+m7U/OV/Gjnf9f3R+AD08C4uSVIAemRNnCZXAvTrfueLzY+3N+fm10P5HymLANDDu7gkSQHokTVxmlwJ0A/zgP399SheX/T1oP5byiKuAujZa1Cwi0uSFIAeGYCm9YWlftX9656oe+D+kfYY/hqA3qprQ5IWgB4ZgKb1z2/m4bp/t/Mz9DLS20K11o4IQA8NQNNygH49hgdQajAAPTIATesLUPPG+Xf/IXzSG5kAlCrmndEAWj0ATevrkfv8+c73O76IRNcPQKsHoIl9zN+z9Jn465wASscEoNUD0MS+HsOP72++Lkj7VSQApWMC0OoBaGr9b8APd0Lfx/+MCUDpmAC0egCa3OfwFnqpfyUpzU8ApYMC0OoBaEbv3kP2j7QXkF4BKB0TgFYPQI8PQOmYALR6AHp8AErHBKDVa8FPACWqEYA+IwAlqhCAPiMAJaoQgD4jACWqEIA+IwAlqhCAPiMAJaoQgD4jACWqEIA+IwAlqhCAPiMAJaoQgD4jACWqEIA+IwAlqhCAPiMAJaoQgD4jACWqEIA+IwAlqhCAPiMAJarQ5QE9ew0uEoASVejigFJkAEpUIQB9RgBKVCEAfUYASlQhAH1GAEpUIQB9RgBKVCEAfUYASlQhAH1GAEpUIQB9RgBKVCEAfUYASlQhAH1GAEpUIQB9RgBKVCEAfUYASlQhAH1GAEpUIQB9RgBKVCEAfUYASlQhAH1GAEpUIQB9RgBKVCEAfUYASlQhAH1GAEpUIQB9RgBKVCEAfUYASlQhAH1GAEpUIQB9RgBKVCEAfUYASlQhAH1GAEpUIQB9RgBKVCEAfUYASlQhAH1GAEpUIQB9RgBKVCEAfUYASlQhAH1GAEpUIQB9RgBKVCEAfUYASlQhAH1GAEpUIQB9RgBKVCEAfUYASlQhAH1GAEpUIQB9RgBKVCEAfUYASlQhAH1GAEpUIQB9RgBKVCEAfUYASlQh/HxGAEpElBmAEhFlBqBERJkBKBFRZgBKRJQZgBIRZQagRESZASgRUWYASkSUGYASEWUGoEREmQEoEVFmAEpElBmAEhFlBqBERJkBKBFRZgBKRJQZgBIRZQagRESZASgRUWYASkSU2eMAJSIqV2mjAJSIHlNpo5oG9MgOfrqA4S473K03juESA1DT5Y8kw91yNIZrezgANV3+SDLcLUdjuLaHA1DT5Y8kw91yNIZrezgANV3+SDLcLUdjuLaHA1DT5Y8kw91yNIZrezgANV3+SDLcLUdjuLaHA1DT5Y8kw91yNIZrezgANV3+SDLcLUdjuLaHA1DT5Y8kw91yNIZrezgANV3+SDLcLUdjuLaHA1DT5Y8kw91yNIZrezgANV3+SDLcLUdjuLaHA1DT5Y8kw91yNIZrezgANV3+SDLcLUdjuLaHA1DT5Y8kw91yNIZrezgANV3+SDLcLUdjuLaHA1DT5Y8kw91yNIZrezgANV3+SDLcLUdjuLaHA1AioswAlIgoMwAlIsoMQImIMgNQIqLMAJSIKDMAJSLKDECJiDIDUCKizACUiCgzACUiygxAiYgyA1AioswAlIgoMwAlIsoMQImIMgNQIqLMAJSIKDMAJSLKDECJiDIDUCKizACUiCgzACUiygxAiYgyA1AioswAlIgoMwAlIsoMQP/+9e2H+8c/v7199b3aSF998y6pNdw/v/3853Tcn/6oNu54OPn5Wri3U0sOF7Mt5Yabj2YvrjJl5sNVnTJ64d9nl1SaKfPhqs4UO2boSBUY7vGAvvah28fvb7rZKVOgf383C68/3NdQg2h6krxylxUedzScOU/qDBe1LcWGWxjNXV7hGM6Hqzpl5guvOlMWtqXmTNFtHKkSwz0e0HfvkH7YYzw5Y4oNNJ5C1YZ795Y4nBXuwtLj+sMNZ8Xb2y9/FR4ualuKDbcwmq7OlFkYruaUGUCzC686U+bDVZ0ppvCRKjLc0wH9nBzR16F8XfYtfLO8gV4LfU1TfbxqDdfPVTclXrOkf4zybocpPO54uNfm9aeDG7fgcDHbUm64+Wi6SlNmPlzVKWOHcwes7kyZD1d1pujCR6rMcA8HVP/UNfv43du1xR/Ev9vnWtzDikrD6R/sVjTvQcyHubTsuOPhXoOYuxOf5qtyw0VtS7HhFkZzl1eYMssbV23KfP3YMwv4Wnj/VdWZMh+u6kyRdqjQkSoz3LMBfT179/94D2G8O/elX9l5dwfpw/0srjBcP2n+z+FJyc/hp+vrruKPwuNOhxtOFPtlweFitqXccPPR7NdVpszCcDWnzBcadrgPPVrVmTIfrupMsYsNHalCwz0b0NfB/LD72DvIX1+an47FGp0NP6oN93o89MN7VcefG+/9wEXHnQ7nLVzOLtk7XMy2lBtuPpq9uMqUWRjuoCljRqs8U6bDVZ0pfRtHqtBwjwb0a8d9l24fezNo/LpykdzS7Y/eOsN9vGbFyvL0CVl03Olwn7Of5ZX26tq21BluoOyQKfM+mSFVp4x3T2yyAlWHqz5Tto5UoeGeDOjXfvv6wePvY+9xWuknQV8Pdr95/6053PKE+Br59XO2/LjjO7w/zCuuP7xLig73anVbqgxnRpMHTRk73CFT5sN7oXqyAlWHqz1TNo9UoeGeDOj7eEd6j9O8uxzFGt4l8q32cMuAmh+55cf1hnst0L47RF9WZzNXt6XKcMOdlUOmjBuu+pTR75Oa+lltpvjD1Z4pm0eq0HAPBtQ8iPjwD6n9XgVA3dv63MuC1YZbBNQ+0VN+3DGg/2P87roqm7m+LTWGG54iO2TK+M/IVZ4yy4BWmykTQGvOlO0jVWi45wJqHyit7OP5z+WdDe+Ktu+iqDbcEqBfZ4X9QV963GE4/ZCs36323XU1NjOwLRWGc6MdM2WG4apPGfvu9vErKNVmij9c5ZkScaQKDfdcQN/NNDnoHui7ebvwa+b0yz72Huin9+st9e6B9qeFebRr3l1XYTND21J+uGG0Q6bMaLgjpox7hnW2AjWHqzxTIo4U90D3NX96uSqg3pt1zTuIDwV0dFpWfQg/evg5+0FfXLTqgH6+jV8hG31RdeOOmjLure3TFag6XNWZEnOkAHRXw7vAjnkVfvymiWqvF7vljQH1Z2vVV+HfvTen6Kehig8X3pbSw3mjHTFlJht3zJSZvAO04kzxhqs5U6KOFK/C78q+AGj7PnpnWvn3gfo/4/ShqzjcZHmvh0vDg7Ty4668b/+zxl7d2payw41Gqz9lxht32JQZFlt7pniLrTlToo5UoeEA1O3jqr+JND8bKg43/3w57z3L5ccdv5HenYCfFfbq5rYUHW48WvUpM9m4w6aMo6T6TPGGqzlToo4Uv4m0q4V9XPV34cePx4r/Tvq4EaDTj0ooP+74Vzn9D3csvZnb21JyuMlotafMdOOqTpmlX1StOFMWf0+02kyJOlL8LnyhhudCan4a0+fwsa0fVT4Vyc8HdPwSgawxrj+cW7g7QwoOF7MtRT8faWUBVabMbLiqU8bzy35Zc6YsDFd1pthCR4pPYyrTsI9rfh6oe9vG8P6NesN5ovkfJGQqPq4PqF14v/TCmxm1LcWGWxjNVmPKzIerOmVey3Tv9HGjVJsp8+GqzhRX6EiVGQ5AvVfjhrv+pT9KxH5upq7WB3Dbxk9K+tlfQy467ugZA+/hU+nNjNuWUsMtjSbdEMWnzMJwVaeMv3D38c31ZspsuKozxRU8UkWGA1BvH7tf/Sj9AP7V8IvN01/UKz7c9FeDpgxU/ZtIn7U2M3Zbygy3PJquwpRZHK7qlJn8on3tmTL9vX5ZcaYMhY8UfxOpRP4+PuKvcvo/7SoNN4jm/50bj4Gy407fBfJeZzOjt6XIcCuj9VWYMivDVZ0yeuFGr/ozZTScrtJMGdo4UvxVTiKi8wJQIqLMAJSIKDMAJSLKDECJiDIDUCKizACUiCgzACUiygxAiYgyA1AioswAlIgoMwAlIsoMQImIMgNQIqLMAJSIKDMAJSLKDECJiDIDUCKizACUiCgzACUiygxAiYgyA1AioswAlIgoMwAlIsoMQImIMgNQIqLMAJSIKDMAJSLKDECJiDIDUCKizACUiCgzACUiygxAiYgyA1AioswAlIgoMwAlIsoMQImIMgNQIqLMAJSIKDMAJSLKDECJiDIDUCKizACUiCgzACUiygxAiYgyA1AioswAlIgoMwAlIsoMQImIMgNQIqLMAJSIKDMAJSLKDECJiDIDUCKizACUWurf399+/rP/6p//N+f2+lbDUoiqBqDUUo6+97fvGTc3twJQOigApZay9H2+5QBqbwWgdFAASi22D1CigwJQajEApUsEoNRiAEqXCECppfSzl18Q9v3oL/v0vn69TvTLX69LfvpD/+vNfT3cynsOdHRjffnH65Jf/jp2w+ieASi11BzQv381/zAkfgH6XxbNjzfby8MFQP/5bXTj1+X/61dv4UT7AlBqqRmgzs839/6m/+wv+ub72T90nwPq/DQ3/rp8SN9tJdoTgFJLTd/G9CKvl+5Tm6kftOvH3y8ev9sv+oumb2N617L20n6TFtAf5hKeLqXdASi11BTQT/ds5ZeSvaTv7pJPQ2p/N3XK7uvfXxebu5nmqxeg5pmAD3djovwAlFpqCuj78EjbXPS+cNfxC9clQD0kP/rvvAA1N/4ildeRaHcASi01AfRLRsfcF3kvDt9nL//0D8yXAPWuqm/8dbn1GECpRABKLTUH1O9F3rv/6s+n+9YCoB6XxksApcIBKLXUBFDvNfgFQP3vLgPqfiNe35UFUCocgFJLzQGdMjcAau+e/vzn8nOg3AOl6gEotdT8Ifz0Y5UGQD/cS+orLyJ5z4HqV+wBlAoHoNRSE0Bfrw9NXjJygHoafi6/iOS9Cv9uX4UHUCoZgFJLTd/GNNzLHN7GNAP09Vg+9n2gAEolA1BqKQ/Q3rf+ec7+Puj7m30jvTXw3bD54X4x095q6TeRXt8AUCocgFJLWfr0C+w//Dcqzd5a739LX2hvFfhdeAClkgEotZSlT//W+vidTPp3iLy3Mb3b7/w381SpvVXg05gAlEoGoNRSjr7eQv0SkP7QpeHloOGN9BrX78OvxZtbBT4PFECpZABKRJQZgBIRZQagRESZASgRUWYASkSUGYASEWUGoEREmQEoEVFmAEpElBmAEhFlBqBERJkBKBFRZgBKRJQZgBIRZQagRESZASgRUWYASkSUGYASEWUGoEREmQEoEVFmAEpElBmAEhFlBqBERJkBKBFRZgBKRJQZgBIRZQagRESZASgRUWYASkSUGYASEWUGoEREmQEoEVFmAEpElBmAEhFlBqBERJkBKBFRZgBKRJQZgBIRZQagRESZASgRUWYASkSUGYASEWUGoEREmQEoEVFmAEpElBmAEhFlBqBERJkBKBFRZgBKRJTZ/w980XYIPtT+0wAAAABJRU5ErkJggg==" title="plot of chunk unnamed-chunk-14" alt="plot of chunk unnamed-chunk-14" width="672" /></p>
<p>Check density plots</p>
<pre class="r"><code>par(mfrow = c(1,2))
hist(out$sims.list$beta[,1])
abline(v = 0, col = &quot;red&quot;)

hist(out$sims.list$beta[,2])
abline(v = 0, col = &quot;red&quot;)</code></pre>
<p><img 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" title="plot of chunk unnamed-chunk-15" alt="plot of chunk unnamed-chunk-15" width="672" /></p>
<pre class="r"><code>dev.off()</code></pre>
<pre><code>## null device 
##           1</code></pre>
<p>Beta coefs and credible intervals</p>
<pre class="r"><code>my.coefs = out$summary[c(&quot;beta[1]&quot;, &quot;beta[2]&quot;),c(1:3,7)]
my.coefs</code></pre>
<pre><code>##           mean     sd  2.5%  97.5%
## beta[1] 10.834 0.8223 9.205 12.438
## beta[2]  5.303 0.8542 3.643  6.952</code></pre>
<p>Compare with results using lm above</p>
<pre class="r"><code>mod = lm(rich ~ scale(logprod), data = div) # this time with standardized predictors
summary(mod)</code></pre>
<pre><code>## 
## Call:
## lm(formula = rich ~ scale(logprod), data = div)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -5.454 -1.704  0.579  1.282  7.763 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)      10.833      0.733   14.77  9.6e-11 ***
## scale(logprod)    5.323      0.755    7.05  2.7e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.11 on 16 degrees of freedom
## Multiple R-squared:  0.757,  Adjusted R-squared:  0.742 
## F-statistic: 49.8 on 1 and 16 DF,  p-value: 2.72e-06</code></pre>
<pre class="r"><code>cbind(coef(mod), my.coefs[,1])</code></pre>
<pre><code>##                  [,1]   [,2]
## (Intercept)    10.833 10.834
## scale(logprod)  5.323  5.303</code></pre>
<pre class="r"><code># coef plots
mod.beta.freq = coef(mod)
mod.se.freq = sqrt(diag(vcov(mod)))
mod.beta.jags = my.coefs[,1]
mod.se.jags = my.coefs[,2]

par(mfrow = c(1,1))
coefplot2(mod.beta.freq, mod.se.freq, col = &quot;red&quot;, varnames = names(mod.beta.freq), xlim = c(-10,14))
coefplot2(mod.beta.jags, mod.se.jags, col = &quot;blue&quot;, varnames = names(mod.beta.freq), add = TRUE, offset = -0.1)</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk unnamed-chunk-17" alt="plot of chunk unnamed-chunk-17" width="672" /></p>
<p>Don’t forget model checks, e.g. residual plots:</p>
<pre class="r"><code>plot(out$mean$mu, out$mean$Res)</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk unnamed-chunk-18" alt="plot of chunk unnamed-chunk-18" width="672" /></p>
<p>Now with elevation treated as a random effect (intercept only)…</p>
<pre class="r"><code># Bundle data for JAGS
# Random effects:
Nre = length(unique(div$elev))
jags.data &lt;- list(Y = div$rich,
                 X = X,
                 N = nrow(div),
                 K = ncol(X),
                 Elev = div$elev,
                 Nre = Nre)</code></pre>
<p>Specify model - only need to modify a few lines of code to include the random effect.</p>
<pre class="r"><code>###################################################
# JAGS code
{sink(&quot;mixedmodel.txt&quot;)
cat(&quot;
    model{
    # Priors beta and sigma
    for (i in 1:K) {beta[i] ~ dnorm(0, 0.0001)}
    tau  &lt;- 1 / (sigma * sigma)
    sigma ~ dunif(0.0001, 20)
    
    # Priors random effects and sigma_Elev
    for (i in 1:Nre) {a[i] ~ dnorm(0, tau_Elev)}
    tau_Elev &lt;- 1 / (sigma_Elev * sigma_Elev)
    sigma_Elev ~ dunif(0.0001, 20)
    
    # Likelihood
    for (i in 1:N) {
    Y[i]    ~ dnorm(mu[i], tau)
    mu[i]  &lt;- eta[i] + a[Elev[i]] # random intercept
    eta[i] &lt;- inprod(beta[], X[i,])
    
    # Residuals
    Res[i]    &lt;- Y[i] - mu[i]
    }
    }
    &quot;,fill = TRUE)
sink()}
##############################################</code></pre>
<p>Initial values, parameters to save etc.</p>
<pre class="r"><code># # Initial values
# inits = function () {
#   list(
#     beta = rnorm(ncol(X), 0, 0.01),
#     sigma = runif(1, 0.0001, 20),
#     a = rnorm(Nre, 0, 0.01),
#     sigma_Elev = runif(1, 0.0001, 20))}

# Parameters to save
params = c(&quot;beta&quot;, &quot;sigma&quot;, &quot;Res&quot;, &quot;a&quot;, &quot;sigma_Elev&quot;, &quot;mu&quot;, &quot;eta&quot;)</code></pre>
<p>Run JAGS, run…</p>
<pre class="r"><code>fit.mixmod = jags(data = jags.data,
           # inits = inits,
           parameters = params,
           model = &quot;mixedmodel.txt&quot;,
           n.chains = 3,
           n.thin = 10,
           n.iter = 5000,
           n.burnin = 1000)</code></pre>
<p>Check output</p>
<pre class="r"><code>out = fit.mixmod$BUGSoutput
print(out, digits = 3)</code></pre>
<pre><code>## Inference for Bugs model at &quot;mixedmodel.txt&quot;, fit using jags,
##  3 chains, each with 5000 iterations (first 1000 discarded), n.thin = 10
##  n.sims = 1200 iterations saved
##              mean    sd    2.5%    25%    50%    75%  97.5%  Rhat n.eff
## Res[1]     -3.009 2.113  -6.726 -4.564 -3.134 -1.544  1.183 1.008   230
## Res[2]      1.530 1.467  -1.719  0.608  1.575  2.433  4.292 1.002   760
## Res[3]     -1.666 1.419  -4.668 -2.554 -1.659 -0.796  1.102 1.001  1200
## Res[4]      0.574 1.475  -2.746 -0.351  0.624  1.491  3.324 1.003   750
## Res[5]     -2.567 1.406  -5.326 -3.476 -2.583 -1.716  0.421 1.001  1200
## Res[6]     -0.471 1.628  -3.570 -1.554 -0.499  0.597  2.762 1.005   370
## Res[7]      2.125 1.910  -1.658  0.818  2.173  3.348  5.961 1.004   460
## Res[8]     -0.218 1.637  -3.332 -1.308 -0.242  0.848  3.126 1.005   430
## Res[9]     -1.322 1.927  -5.018 -2.747 -1.225  0.196  2.165 1.003   540
## Res[10]    -2.154 1.798  -5.443 -3.354 -2.215 -1.089  1.601 1.000  1200
## Res[11]     0.151 1.611  -3.077 -0.805  0.208  1.159  3.342 1.003   570
## Res[12]     5.896 1.732   2.631  4.660  5.905  7.196  9.112 1.003   680
## Res[13]     1.499 1.677  -1.884  0.501  1.494  2.565  4.674 1.004   530
## Res[14]    -0.762 1.641  -4.279 -1.776 -0.729  0.356  2.284 1.001  1200
## Res[15]     1.608 1.584  -1.457  0.625  1.604  2.622  4.676 1.005   390
## Res[16]    -2.983 1.781  -6.548 -4.017 -2.923 -1.854  0.328 1.003   610
## Res[17]     1.521 1.401  -1.220  0.629  1.513  2.352  4.459 1.001  1200
## Res[18]     0.193 1.429  -2.667 -0.708  0.182  1.062  3.255 1.001  1200
## a[1]       -0.259 2.462  -5.096 -1.341 -0.150  0.887  5.605 1.001  1200
## a[2]       -0.273 2.518  -6.615 -1.179 -0.012  1.002  4.426 1.005  1200
## a[3]        2.840 3.045  -1.248  0.628  2.116  4.475 10.441 1.001  1200
## a[4]        1.789 3.430  -2.972 -0.298  0.866  3.443 10.797 1.003   980
## a[5]       -1.259 3.169  -9.039 -2.514 -0.491  0.535  3.699 1.005  1200
## a[6]       -3.246 3.514 -11.829 -4.956 -2.328 -0.665  1.245 1.008   250
## beta[1]    10.904 2.215   6.151  9.818 10.843 11.790 15.962 1.006  1200
## beta[2]     3.689 2.011  -0.816  2.481  4.064  5.139  6.781 1.003   630
## deviance   87.204 6.172  75.704 82.559 87.764 91.546 98.594 1.002   780
## eta[1]      9.255 2.453   5.216  7.978  8.905 10.068 15.564 1.007   840
## eta[2]      8.743 2.587   4.669  7.308  8.293  9.669 15.228 1.007   730
## eta[3]      9.939 2.316   5.698  8.735  9.701 10.804 15.767 1.015  1200
## eta[4]      8.699 2.600   4.693  7.247  8.242  9.644 15.197 1.007   720
## eta[5]     11.826 2.234   6.450 10.803 11.918 12.891 16.258 1.006  1200
## eta[6]      6.717 3.300   2.065  4.665  5.985  8.142 15.020 1.006   560
## eta[7]      5.134 3.983  -0.440  2.477  4.222  6.889 15.480 1.005   520
## eta[8]      6.463 3.404   1.658  4.308  5.728  7.912 15.129 1.006   550
## eta[9]     12.482 2.314   6.934 11.430 12.709 13.733 16.546 1.007  1200
## eta[10]     9.413 2.417   5.326  8.151  9.100 10.232 15.521 1.007   890
## eta[11]     6.108 3.553   1.124  3.814  5.327  7.612 15.281 1.006   540
## eta[12]    13.264 2.475   6.975 12.098 13.581 14.651 17.240 1.009  1200
## eta[13]    16.712 3.723   7.634 14.764 17.365 19.207 22.448 1.001  1200
## eta[14]    14.922 2.994   7.548 13.426 15.472 16.818 19.391 1.017  1200
## eta[15]    15.603 3.256   7.563 13.971 16.249 17.711 20.525 1.023  1200
## eta[16]    17.193 3.937   7.676 15.103 17.860 19.843 23.254 1.001  1200
## eta[17]    11.738 2.227   6.383 10.732 11.819 12.796 16.276 1.006  1200
## eta[18]    12.066 2.257   6.629 11.042 12.210 13.192 16.373 1.006  1200
## mu[1]       6.009 2.113   1.817  4.544  6.134  7.564  9.726 1.008   230
## mu[2]       8.470 1.467   5.708  7.567  8.425  9.392 11.719 1.002   800
## mu[3]       9.666 1.419   6.898  8.796  9.659 10.554 12.668 1.000  1200
## mu[4]       8.426 1.475   5.676  7.509  8.376  9.351 11.746 1.002   780
## mu[5]      11.567 1.406   8.579 10.716 11.583 12.476 14.326 1.002  1200
## mu[6]       3.471 1.628   0.238  2.403  3.499  4.554  6.570 1.005   370
## mu[7]       3.875 1.910   0.039  2.652  3.827  5.182  7.658 1.004   460
## mu[8]       3.218 1.637  -0.126  2.152  3.242  4.308  6.332 1.005   430
## mu[9]      15.322 1.927  11.835 13.804 15.225 16.747 19.018 1.004   530
## mu[10]      8.154 1.798   4.399  7.089  8.215  9.354 11.443 1.000  1200
## mu[11]      4.849 1.611   1.658  3.841  4.792  5.805  8.077 1.003   570
## mu[12]     16.104 1.732  12.888 14.804 16.095 17.340 19.369 1.003   680
## mu[13]     18.501 1.677  15.326 17.435 18.506 19.499 21.884 1.004   500
## mu[14]     17.762 1.641  14.716 16.644 17.729 18.776 21.279 1.001  1200
## mu[15]     17.392 1.584  14.324 16.378 17.396 18.375 20.457 1.007   360
## mu[16]     18.983 1.781  15.672 17.854 18.923 20.017 22.548 1.003   590
## mu[17]     11.479 1.401   8.541 10.648 11.487 12.371 14.220 1.002  1200
## mu[18]     11.807 1.429   8.745 10.938 11.818 12.708 14.667 1.001  1200
## sigma       2.893 0.713   1.758  2.377  2.815  3.296  4.528 1.001  1200
## sigma_Elev  3.719 3.193   0.215  1.469  2.795  5.001 12.784 1.014   220
## 
## For each parameter, n.eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor (at convergence, Rhat=1).
## 
## DIC info (using the rule, pD = var(deviance)/2)
## pD = 19.0 and DIC = 106.2
## DIC is an estimate of expected predictive error (lower deviance is better).</code></pre>
<pre class="r"><code># Assess mixing/convergence
traceplot(fit.mixmod, varname = c(&quot;beta&quot;,&quot;sigma&quot;,&quot;sigma_Elev&quot;))</code></pre>
<p><img src="data:image/png;base64,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" 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" 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QG00sYAaB+AHhGA2i8BdC0ALTo8gBYPQM/oUoA+/xdAo5YLoBJAjwlA7ZcAuhaA5i5hNgAtHoCeEYBGBqC5S5gNQIt3HqDvLCiARgaguUuYDUCL93aANgE3gEYGoLlLmA1Ai/eGgDYg6GGAJm0sgO5cLoBKAD0mALVf1gY0YWsBdOdyAVQC6DEBqP0SQNd6U0BfRw1AbQA6DUDtlwC6FoBmLmE+AC0egJ7RvQEtuEUAmrmE+QC0eAB6RncHtNgmld47z8UB6M4ANAxAzwhAj1+UXR6A7gxAwwD0jAD0+EXZ5dUDdM9TxQC6HIBOA1D7JYAetCi7PADdGYCGAegZAejxi7LLA9CdAWgYgJ4RgB6/KLs8AN2ZWD8oAHpEAGq/BNCDFmWXB6A7A9AwAD0jAD1+UXZ5ALozAA0D0DMC0OMXZZcHoDsD0DAAPSMAPX5RdnkAujMADQPQM7oSoK//AdDtAFQHoEcEoPZLAD1oUXZ5ALozAA0D0DMC0OMXZZcHoDsD0DAAPSMAPX5RdnkAujMADQPQQ8YcXQCgWYvaTx+Api10LgANA9AzxswCNGfF7wboXvsANG2hcwFo2DmAZvyTL+WGVpXOoFHBLCsFaPouA9DJ8gB0ZwAa9p6AHiFoMM0ANDcAzVzCfABaPACtFYAWKAfQtSs1AejcMQHQ5QB0EoACaMaiYgFdvlYjgE4PCoAuB6CTABRAMxYFoKvjA+gJAWitALRAAJowPoCe0KmAnoEogC4GoHtqANDN4QG0eCcDesp7MgF0PgDdE4DqAPSAABRAMxYFoMuLBNBzStzcQqcGgAJoxqIAdGWRAHpKyYAWOTcAtEFAJycGgKYEoDoAXQtAo7sFoB2AxgagOgBdC0A3s3soHtDIXfqGgAoAjasOoPMLBNAwAC2d3UV3AjRNUABd7xRA+x8mAzq3RAANA9DSASiArncSoB2AVghAS/f2gMZvUeqiAHRlkQB6Sm8A6HAWAOhSALorANUB6FrXBHQ4N88HtAPQrEUB6MoiAfSUALR0AAqg61UBdGv4WoBOjjWArgWgmwEogK53M0BHBxtA1wLQzQD0SEA3LZosEECjA9DtALR0AaB2XwFoZjGATjbw6oA+vwLQMACdBKDHARp9o0qA7jnGbwpoB6BhADrp5oCK8L/y7QHNP8gAGhmAntXFAY2ZsgAaF4DuCkB1ALpWe4Buz1kAjQtAdwWgujMA/fs//1F60NgAtHQACqDrAWhas4B+PcJ+BtC8AHTzNsoMuXldAN3VLkC7qwI6nb7HAPrxAFAAHV1to52Abm4zgO7qeECDa7wboH/9OgT0R+kxowPQrRunrhKA2uXooQF0XDlAlf/yvQB93gH9JuXn639eX5/nJ4AWGGAYgHpAlQTQaQCa1hTQf35//PLn63nQ/qH754mP4AG0wADDIgFVo6tvd1dAV34OoJHLeTNA//7t8V2+Hsj/9C/Zc8pD+MwAdPM2FwB08QoAGrmc9wP0h/+PeSh/TgC6f4BhAHoZQHdPv+GwAPrqQECfdz1fd0Sf90RfD+jPCUD3DzDsuYf6nQSgFwC0oKAtA/q84h0BlR/6rudfv/I2pswAdPM2ADpc4HsCqm4FqL3r+dm/lmRfTDolAN0/wKDXDgJQO7gE0Gk3AtTeWwg65G1M5oX3r0f/KpJx9JQAdP8AgwAUQDcC0LTm30j/ovP5nyedX483fRFJASiAAqi+MYAutvSnnN/9X3S+59uYAFQCaASgbrUAdGk5bwfoP7/3av79W+/neXdAzwO0P7eTJ/D4iALo5m3uAKhwKxRxYwCdu2YVQNVpgEr51T/9ae+KntblAO1Wv9++zYGAurk1BVS5xQIogALoanyg8iQABdBwHdcXZZfXAejScgD0rAA0bcDNrgzo6GR4M0B3agqgOgBd696AzqwOgL4RoLsEfUdAhVBnAXqfT6RPOibmys0CmnQ2zmbxBFAAXQhA05oB9PUi/H0ATTgo5soAmg5oxt56W0AX1/ZtAB0eydsB+vkAUAA1178ZoH4xzQJqf3xrQMXwmrcC9HUH9MT3foYB6HTtUgcYBKAXBnTPM/wAqjvo05jO++v3YQA6XbvUAQYBaHOABlu1CWi+oBcFdG2YlgE9883zYQA6XbvUAQbdDVB9u+KABgs8ANDgTw8BdHNlwrWaubQBQO3H2TUQgE7XLnWAQYmAzryRbqG7AbrwUs5oWXcGNBgMQFeafRGpkadA3w/Q5zG/EqD+h7F7a4xCNqD6lATQyHIAdaO5lVIqB9D+Vu8EqP1I+vN7Q0CX/1mxJgF1f/URDagafAOg1wJUVQG0uxegr08CbeNBPIBO1y51gEFNAaqUBFAAvSGgL0F5HyiA3gpQPRSAbnVdQPvvWwCUv0QCUHf1GwGqJIBudxqgc1MoEdCuDUD5SyQAdVcHUAAtAah6H0Dr/CVS1rtLKwC6No0AVC80C9CIc0oC6KUBfR0wAB01+0b6Cm9jagfQ5Xl0HqB2xQF0Zc0bA9Qs6maAKgBN66i/RLouoPGO7gFUr3llQPUoAOq/3wWoej9AJYCOqvaXSOafpBuX9IRqI4DGCgqgm70toHblAVTeHVD5WeTDRI4HVADoRgNAh5KECwXQGoAuXO0SgOobXw/QrjvpbUwl/hLpC0CPBXT7/ALQlgF9rRuAzq7MTNP9PQvo6DrHvJG+0Afave6DuuUc8BzoFNDXwX4nQLdOsOYANTfa3lHB+tq1nAF0bTEAuhfQ/gcDQNdWqD6g4y1qBdDJY+/894F+PB76H5iPAnTuDmvKaHOAdocB+hw7GdB+agFoQUC3Dm5ZQPW/+/jOgC6vEYCWeCP9629Cv9ul3h3QiX0RgKoBoOIoQPsvmgJ0Y0+9K6DBDn5LQOXi5/C/B6D9u/L7h/EnPYRvGlC1E9AOQK8LqN0aP2uHgCoDqFvbCaDRTy1Js7siD1BrgM6P0S6ghXv9YeiPuwIa/AhAAXR+THPtCaDuWZtsQOMFfRdAwxl7E0D7h/HfbgtoeIYkAtqj8w6ADtAE0EsAKgA0rvqA9g/jf/4/7QIaPs+k/6O/fB9ABws2k908A1cGUDXzZT6gryvfE9BAheYAdYeqGqCDKWRGAdBX+vOd2gXU3OxugE6mXhqgagbQ4beFAA3PhYWtHwHa7QDULAdAZ2sAUH1NcwYCaF//Cc0AOr5xdUBnRY0BVC0CKma/Kwjo7OafBejM40q/SgC6DqiyX6UC2slygA5vfcqr8I8Sb6v/ANDpjWsAGlwrF1AJoJcEdGWiXhJQdRtAa3xAaEwAOj+WX/4coJOTNriGvRxAbwno4iGoBuiioADagKAAOj+WXz6ASgBdvCDcomhApdlRs4B2BwIqLwpo/4yl+QvMF6bfwwuO7bm5Cc/zTA5ndzygg4UfD6gA0PWVOBNQMbhag4CaA9IQoPKSgA7+VeOvns46n1K/WQ9otKBXBbRrAVB/KwB9X0BVFKDyIED7H14Q0I8Blvq7Mp8RmloqoKPT/YqAzp2Hk7H88gsCam92Z0CD7a4D6HB3Bbe+EaDmurIuoHacaEC7VgB93tsMPw70q/9b+K9T/m3O1gFVA630IQ3nHYCO1/zKgE52xSygQxp2AireGdD+BgC6pxYAXbkPsgBocJ6nAfr6hMm7AKo1mKz5tQEd7ItZQIcnLICaq74VoP/8PnoI/3rwDqBzAegKoP7Z3DsDKsaAhmvQDKB61QD0qOdABy8i9Zx+vO1zoBmAmgsvD6iqDOjz1icDunBwAbQZQKeTeLBSgwtaAfT1d5fm/ubrbUyvV+E/c/6OaH9ZgIYH3gNqLz0GUDsRIgEV7wqoSATUIbQbULfVFQENd8pNAR3Pj8HC3hVQ89kfth+9qOe9DzQd0PAxtAfUzfPp7U4DVHpA+9OlLqD+TB0B2t8VHALab39lQJ+zOxlQIW8HqP+9BqB6uKsDqj/7Q/eC8yvrk0AK9F6AqjhA/ZAFARXxgAZj7ga0qwPo3DkHoDmAGssAdLGFT2Myf8+pH8l/nXP/E0DHgw3+ewygcqxG+E7/dgEVo9tWB9SszP0A7coDOnrq9JaAttEioLM7dQyok0D/MBZQ8z8bgAZnob0AQBeuWAlQt/2bgAo5A6j+v9elkyHLAyqC52+vAKhdKwDd6qqAzuzVwoCaCbcIqDgS0H6CTQGVbpa57a8A6HDu7wL0OZDf/nYAHY3ZBKBCb28UoO53/dy+kwDqAlDdMYB25sJjAZ3eIdLLWgfUApMMqFJyG9CQzJ2A+lXqpzeAlgFUycYBVYNpc09AX098/ij7zxrvq3FAB2oAKICuAmpFMqsbA6hbemFAF1BaANQYCqCzASiAtgGoxRBA9Y1DQM3RKAjonEpTQLvrAaoA1AegtrcANDxvRq0C6vbYzQD1byOrAOjMCXQsoG5llX6G9AaANtd7Adr/f3FAg6XVA7TflFsCuvzA9wBAZXiQigI6C18OoIuCRgBqfw6gtTobUPPVxQENJq67PAZQ+a6AdgNAZ+5vm2+vDOgcfACa0SUBnX8EsgaoaBDQ109vDKjf/DVAPSp5gCq7J6UE0CYB1Xv4LQF9/UXnGR/C5Lo7oB2AZgAanNX+jaRuj45HAVCZDaiWLRlQNThvBoCam90d0L9/e7mp/yL+pL/i7Ls/oO42UYAqv5ZuA/0mAOjRgOo9GcIIoHY9lgBVtQF1gp4H6Ff/yvs/v5/9InwI6PiU2AbUTJr9gMq5edkqoKoUoBJA5QDQ8aS7IKB22QcA6rf9/QA1n173/M/TzpM+CVQ3ADTYEwBqtxBAAVQmAGr3T31A3eeKvx+gnw/zD3H2dH6c8g8a6+oAOj5DVwGd0uBuNPzRHKCzFro2AQ1uHQmoDCUE0HBMAO06v3+OAFS8KaDPh+6vZ0Cf/+mf/jznX0PSFQBUXhrQLrju9QD1hyBcJX/WNwTo4AbBHAm+rAeo1J4MAFUNANr1L3K2Bqg/vQdTxZ5JFtDXMT0HUPOvcj7/08v5roDKNEDFWYAKtwmpgHpJ6gLq1lxOAVV6FRsANLjFbQH1v28vDagB0q6Y20QPqD59zwXU/GtyLQLqH/UMKgmovuYRgKq9gAq7CdGAypsC2t0Z0Mm/T6kqAqqvcAygaieg7pfnAFB1OqAfD/3Pw3+e+FbQqwJqJ1tZQOUIUL2BhQD1b3I5BVBpH19GAWqO4jsA6tZ2N6Cqv1s2XJthAJrR7HOg3/1ToE9Om3sR6WRAzdxpCdB+nmQDKgYsAmglQKW/tx2sI4DeDdDnfc8nnZ/6j5Beb2n6UXrM6FoFVJUFVAi5G9BuG1C/zyaAyoMAFWNALSq5gKrTABVCAWgxQPWUuwmg9t/k/NE/jj/zjzmLAepvAaB+i1cBfe26ZgC197DLAyqVeg9AVSqgsiigMgPQwX4KdnzrgJp/F/4F5+epf4gEoKcC2rUEaLcLULEHUBUuJljqWwCq7K4+F9DBnGse0P4fgjcvwZ/3+F1eElB1EKD6bNsP6Gu5ADoG1C9RVQbU/WAXoPq6RQDtJcsANBi7CqDjBwIxgHZnAdpK5QGdQ+1igOozWo9XAtBuF6DC7JIJoGb3twaoVfRugOprFQG0ywR0cH8YQFtoHtCFw98CoL2G1QHt3Hi3BHSqQLcHUHPs+5W4MqDiaEBfVwbQrQD0LEBV04DKmwGqzHN7ACoBtGQAegVAu3VAxQyg/WwSwZRfBLQbAKpnswRQ91UsoGbF/FU8oOH+fh9A7dxwb4hKAFQBaIH2AGpOx5sCKiIA7S4CqLvDsQSo2ScXAdQf8mKAmjl8WUCNiHIZUBUL6Gh/A+hqSYDai88HVF4TUHu7ooB2AOp2/aUBdZMPQAfdElC7hw8DtD8DHaAiHdDXkT8J0NcKlABUDvbeOYAOcXKbnQuoeWw52lj7raXgFECFUMGeKgaoKgqoMltSCVCzw4OdD6DDLgRoFwPoYEF3BdQcrxBQz2oOoPYUWQBUHQOoCP65MvP9HkDFbkCnb8iNBdSNOaocoPrtfMuA6iMyD2iXA6h9QhRAB5UC1N9iH6D6pguAmsO+DmgwwwF0CVC/k+xBFO8JqHI/C3f20oFQLQHabQGq4gAVmYB2ACorAjqaisuADi4xh/F8QPVp2wag5iSR9ndNYUC7OUDdWX0ooMNJVxJQvYL9rhAtAOrOFdUEoCoC0G4e0PEZCKAyEVCxAKgIblcWUFUK0OBkbAVQMXjHaAVAu21AFYDKUoAOb3A+oH7CLgDqRgTQmEoAak+ZNEA1G/OA9qdUPqDuYW8coO638gUBNTO7KKCvExGYUy40AAAgAElEQVRAiwEa3mII6GtalAZUbQIqALRgxwOq/GxdAbQ7FFB1HUADMk4EdExNVUCDE7oIoGZoALWAmt22Dqj9L4AO8oCKWwHaj1cMUDEANFylKaBub9oL1gC1S7wVoApAAbRgAJoN6GvJ7umiNED7ybsHUBkLaLA8fSqarbMXFAV08CtFyAmgeiPd3ngtSf+dX2lA5eD4TgBVtwd0dH4cCKjl6xhA7doFpzuA+iygSjUEqBoBaqAYACrNaT0HqIFpD6B6tcsBqvIA1SeClIUB9fu7GqCBnrsA7brwhG4K0MEJsgCoqgKoyAdUv51a+EMcDajZTGH+og9A+2YBVeFhCzoWULPkLEC1TABaHlBxOKBdI4CqgCQ5nODyJEDNjyaAmuk+A6hRryyggx0BoEptAGoPXHVAuxqA6gvcaE0AGqBwYUC7Lg9Qu3AANQdkH6DSnr8zgHblALUWA+gU0H7qpgCqggP9xoD2K7oEqKoGqBwAqvea2RtZgAZnVyKgnTm3QkDtHZ+6gJqzGEBXAXV6AmiprgGosICKXEDlvQE1FJhl2Z2p7JPJy4C608PCmAmo7O+4zgPaFQHUbYMIDrmaHDLrrN4Wa9BZgPp7yHZ9twD1sykRUBEFqALQsu0A1J4B/o0rdr9PAZVu2TsA7QB0GVDxboD2/2uAMhs6A6iaB1Qf9sMB7b91gPYEGlFPANTfUwfQ/G4IqAJQtzNuDWi3B9DunQDt9L9dB6DlaxdQq8oMoG7K5wPqCEwBNDgtzNVEeMYOAXV7LApQtx3HAOp2+HGAdscDKt3rm60BKkoAak6E/oTtJ8NBgEoA9V0CUH1eZAMq5wC1UlYCNNhjDQGqd0bDgKqhYFcHNDha5oBUAdS8PBALqATQcoWAugcEKYD2D6lPAjQ8sqZKgKojAe2KAWqfeHg3QM0MLAyoFjT4cTjxLKAOpQ1AZVFAuyhABYAWD0DdvG8XUCfeKqDKrvMRgA48LAPoa9JlAKrHSQfUruYOQMMzJJhIwo+5BKiZZmcD6vdTOqD6qTEALQOom6SZgIYn9DsD2s/PGUC79gD1p429u7UbUCkHs64uoF09QIUbMwZQGQWoXr4duQ6g7jAAaHRFABW1AdUuzABqbPDNAqoMDiuAduHb0c8EtGsEUPdOiGRAzWNja9scoH6XzQPq75LqFSwAqBHwwoB2BQG1wwDo3poGtD9VmgBUVALUf+pTPKB+MZcE1O6udUDNAbsYoHZSujex1QJU5AOq75gAaKEA9ChAu3KAOsxkNqDBNmwCap8tkYUA7YJd1gSgdvfOAarcdw0AaveOA1TZaQ6gJ3U6oPYYRwOqrI77ATU/fW1t64DaXzMpgKptQP151B6gepOOAbRrEFC3P0JAzZfFAVUDQIc7HEBXahZQdSSgYh5QM4X2AdoFH4RTBVAZDajMAtTsanvOO0CDR7fSAtpdBtB+m4U73luACvsCWQygshCgog1Ag3WSdiEGUH0yAWhhQPXkPA1Qh10EoLKfDk7BRUDdedyvykGAmoOyCqi0q2h2qn+qdQyovuYuQN0OjwZU7QRUT4LJ+exmShKgejG5gHaFAHVLvSyg3RjQYBMB9CBARduA2ufGpDkhhoBawbpuA1DNrQFUuNsdD6i701wZUH1kcgBVewHV698AoOGQpwIqiwJqxgbQQe0C2i8jVEtkAypbA9Q+u7oEqLlmEUA7sw4ezExAVWVArYB2FpjXkE4EtMsF1F3lSEDFLkA7+xE7K4AKAJ10IqByBKiZP+bkzQZUv/bcHQSotBa67Z8BVLwroHIIqMNxAVB33y0X0K47EtDBKbIIaCgogOZ0L0D1LLO/6csCqtoB1CihN/DNANXn4Ragfs/baTEGVNiPC1bS776igBqkA0C7aED1xE0F1HN4OqA9i2NA3bm8B1Clwh1u12kTUL073h5QFf7b3sPmABUpgCr72Q/mwdYE0C4eUD0lCgGqEgH1V4sCVHRdcUDdY+JNQNUIUHtPxX2/DajKA1QoeyzdURsCahcrNwF1+0evqv41VhxQOZi1aYAq+6smWMISoP4I3grQ19q9K6DuSFlAZwT1gEon1wGAqpYAVY0Bal3bCaiF0QIqJ4AO7vIfCahwD0/PBlTjMp7U0h3UBECVvclVAZUA2gegFwNUhoAKafbaJqD6XJ4HNIRRCL/ZbwXoYJ66Q5cGqNgHqIgDtAPQhloFVI2uXBtQI8kMoPIgQPXqNQZolwyo3achoEamOUBVEUDVLkBVUUD1DKwKqNsg+60GVPidewKgZoesAmr3Ug1AxRsCqlYAHQl6FKBdF+B0VUDtRQcAKp0Fw7urchFQA4E9caMADXf4FFCz2+xEOBRQ97yiXbadJFuAyjlA7ayfANqNAO3EMqBmedUAFacAKgB03DUB1ZQ1BKgdQYaA6ou2AFUBoF0VQOUMoCIEVGQB6nZ9FUC7EwG1HqQBqnIANfNNRgEqBoCKZEA7+36GHECFeenYAKr3IICWAdTNUfkmgAZncxKg5nxtAFBp5ToB0M4dk9sAKpYBDa5lnm2QI0DtrCgAqJs2MYCKnYDqHRQA+rodgCYDKhsFVE+ZdEAtYPOAhm9D6ezbf+oBqmIBNVbUANTsH7kJ6OtifUk8oDICUDfd1gF1x7sxQN3VzOzcAFRayYQ75TIBNYvWv/5mABUAurdcQC0c5QBVVQFV5QC12qmSgNqHcY0C6i67LqD9xFVFAQ3uXB4BaLcAaJcFqDuYGYB2AGrLBtScPeoKgPZyKQOoPvqXALTrFgC1p98QUAWg7w6oGeVAQJUDVPSfFygAdAFQq2AcoKIKoKIWoOJ4QEUMoF0RQPXhMFt8MqD22BYH1MocDaiyCygJqGgAUHkeoK+VBtAlQIWZy2cBag7uYYA6J28GqP1NlAyo7KIBlWUA7dc8CVBRFFBn0gRQO8mk+/b+gEoz7Aqg4daW6l6Ads0D6ub2FqD2FF4H1J2uJwIq3bNb/p8GGQHab1I0oObg5gIa3MJu9gBQfRe0FKDBcb4xoMFEWgXU/AKcACr9ATkT0OBeeaneE1B7KpnLlwHVV5kFVF0HUOnO/jhALSQFAe2OBtSd6GbXrwEqlwC1UyYV0G4LUFUaUOXuuEsZoOJ/PgJUH9x+XD/LIwC127oJqBW0IKDml/AeQK/6HOg/vz+efU+92U5A1QqgXXDTgoDag54BqPF3BlDz5TqgqhKgsgqgbk80Cajb5f02dF0VQNUOQN1sNDfOAbTzgNrHWTsBtYTVA1TZ54ImgMpbAvr1VPOXP/V/X/38R9rtdwCqrzwG1J4XGYDKIaAiDVB3t+tcQFXjgIpEQNUSoNKf6OYAnA+o3XXZgOpX0s3r6SGgWgrlz4ngXpmUc4B2DQMqJ4DKRED7JV4fUH238/HTv/769ZEnaPOAmrVQEYAaNvydhbYBFUcCqixfwmIWAahbzKmAqr2AmiM0AFRuACqiATW0ZAGqAuWOA9RszwTQ/rTYDegAjSJVBPTDqPnL/zJyfuj7o/E1BKh71skCqjygKhHQ/uqJgKplQPX89YCGM7ItQNUCoHrfqXlA/dk3AtQdqxxARRqg3Tygw+O8AKjYANSCVAdQM0uGgOqf1wJUJQEa/r4fAmoOy25AlbwkoM/7nS82Px8P5+bzofyPlEXsArR/BSQPUDsn4gBVzQKq8gE1p9gmoF3n/ln2BUDV/QF1e/kEQPVcTQPUHl1ZFFAVB6i50TqgYgNQNwVvDOinecD+8XoUry96Pqj/lrKICaCeTmH+Ato0BFT/15xuBwBqEVBRgMprAaqnoYwF1O3jMaBm+/ybXJcB1edXu4BKfec8G1B9+1KAOl8GgOr9ugSoDAC1qgRPU9mp5xZ5BUDtHwTOAWp/TVwI0CeW+lX35z1R98D9M+0x/BagoYL6WKUD6ibCDkA784R9NqDqAoCaiToEVGlA3TbGA9q1BaiygMoFQEWA7ABQvbPMHokHVO4CVBwGqGgJUL2yAaDSPX0g7wbo37+Zh+vh3c6vtZeRHjPNAmoeyI/uRsoRoNLMshlA9X0pf8tlQPUQhQA1j1uTALVmRAJqlhwA6map0APtA3QwfnlAzTjm0b6MAVQMAHXXtifZMqDSPqUwB6iwTFYGVJQA1D4MaANQy98CoOZ9K0UAdXs9BNQcj/sA+nwMXwRQI2ccoPK1r4oAqhc1C6jQR+doQMNfvzGAmofMQs8oe1KOAZUTQKVdEWVOSCmsAsbARUDtgZgDVEwAVTmAqiRAVauA2hv3gNp9309e/7svAlB96LcBlZ15hFIH0M69AqnV02dpAqD21zuAvgA1b5z/CB/CJ72RKR3Qzs3xAFBlr+Kfy8sHVK4Das+zHED1mZUNaLDkIaB2Km4Dqqd/UUBVDqDSPsW9BKiSlwPU3XkvA6haAdTYuQqocfJoQIV7TScKUDPKJqDSrOQCoML/BcKyNnnVfA50+nznx44XkdIBlfYhi73KHkDdlG0Y0G4G0OAXSX8q2kfQC4Damdufm/2pZIhUZlVrAapWAbVWzAPqd1azgEq3MbsAVTOAugkSAqrqAipWAe3Mv25UH1Aza8aAyhsA+nr/0vg9S1+Jf85pAbV3+28MqCoPqLAnbFuAdrsAVQCqAe2Cf/NiDlBVClD76NgsOB1Q0YWHUI4AVTsANdPrpoA+H8MP72++Lkj7U6QzADVDjAC1p3QxQEU8oLIeoF0n1wC1jDQPqB+0GqAyDVDrXzFA9UpPATVDJgDqnuxeA1RzFQ9oPxGOB9TccgNQMxXltQDVfwHv74R+DL+NaS+g0s5xc5UhoHbmbgOqrzcHqDwA0H5+xAHaxQGqxoBayHYC6tYuDVBpHtkF39uHfFmAmjO1CKBiCVC1E9DeJ380dwDa2d+wQ0DdT1cB7bpFQOV1ARXiDoC+BLVvoZf6T5LS/KwOaBfMgZ2AqllAnaCVAFWVAdWb3C9yG1CjXz6gah1QZYyRM4DqgfcCKtIBNYKaHRADqD6I7QDqzpDygMokQEUMoOb0nADaP8sWANo/sdEp5+4lAX3e6wwesn+mvYD0agyo8o6uAer3bgho8LCwE8uA2okxBVTjNg+oGgFqzoYQUHPwjwdUCjMXqwNqf8GkA9of4nhA9YJTANXbPwa069yGieCF69sA6o+z3mA5ANScM5GACtkOoGoEqFwFVOjfuVcEdGfnAGpn2iqgcgtQYc9Dfx6JEaDujBsCqswMlsmASg+oO42lA7Q7FVCRCahyx3QVULMvQ0DtGdtf/86AdgbQEKAEQEUKoDIRUHOcqwBqb28AVQuACgC9NKD9fFwEVBYAVA+SD6g8BlB5MUDNRiQBKloEVOwBVN9/iwfU/joX7v7qXkD1+i8BKiMANTNGAug1Ae22AJUFALVjLQBqTrTSgHadiH0Ir5IAdfetYwGVVwTUKpgCqFgA1GyiGegoQM2faaiigNozeQFQ+9vVrLbZfPdYagKo2ToAPQpQB44/N8aAinVAhZZIWkDVYYCKSEBlZ+/8lgTUAngOoOZloN2Ail2A6t1+IKD2FnoTzd7fBtSfDDGA2sNxCqBdJ5MAlaMXkcyMkQC6Bag9BV4XrQLq5t0KoO7uo7m1wa2TbuacBKjdjCmg+o0ehwDa5QOqUgCVsYD2RzEDUHuX0QKqddAD2905BlT6Q1QNUOHuKY4BlXZ6JQAqTwFUOUDNEjygcgNQ6Q9IJKDSAKp3EIDKeUD1eXkWoLI4oKIVQM2aDwANdqk5QaaA2neHnQOosJ/mOg+o/dkYUH36bwIqKwEqjQYRgJr77JbcbEClndYqH1A7pc3C9BE+DVBzXscDand+0a4CaPC/5lAHuKUCaohwJ1g0oNIBqmIBVXsANWdYCUBFBKBiG1C93PqAymxA/SdiHQeoHsQgIM1SJ4Dq3ZoBqD4AK4CqBECdKAmAmlNnC9BgrkQBKkQBQO0OiwG0A9ACgEo7yRcAlQUAlQNA1QhQ1XXHAOpOznlAZUuASimC79sB1O6DZUDtIH6nHQuoVGuAikhALUEhoPYxejVARQioEAMT7YOvdEAFgOpiANW75GaACtEIoPoUFDsBlRmA2mUC6NGAmsXap5cHgEoAHXUtQOUcoO6AVAPUToBZQPupGguoiAVU5ACqV38RUGkB7eybNysBambxOqDieoAaFuQGoDIfUGkMXAZUrgFq9yiABoDazXtfQNUEUP+regqofU5qD6ByDGiXBqj1tgSg+tzeD6ie0RUAVTOACvPpv3IF0M6+Z9QegnVA1QhQvesrA6qnioMuDtB+Oxyg9tGEzAXUXGsHoP2S6gIqZgCVbueGgOq1NUuOBlROAHUWAuhiK4B2FQHt2wFov8j+lAwAFa0DKlsEVJjxiwBqFz0LqHLn6xRQd+N6gJqNU6mAuvtiU0CFnuMACqC1ATWPKFoF1D2xeRtA1QUBtYuLAlSeAKjSc1qFgLrHQQuAamHNAnYDavdTTUD1AQLQqAygwn6O3Tqg5pfvGFA9D+UsoOG5twtQfcvBe/7kmYCqYwCV8YB264C6e27HAWpOuGxA3TSbACpkNUDlNqAqElAzv3cBqhIAtTswE1Bllwqg0UUDau+AzgNqpu86oP3RWwZUmD8siwS0SwVUngWocL8VRoD6jwSIAFTv/01AO/PQNhVQ+xLCBqAqFlA5BFRsAarEHkDdr/A7A2r22ABQaefLJqByJ6DukX0nh4BKAN0A1E6qDUCV+eMl8xCgv3QEqCgKqEgBtF9kDUC1ExZQO5sOBFQuAird/7lDcENA9SipgColh4CKSECVuboEUGFn9ZsDqh8ErQEa6CDFYGYPAFW1AZVCzAAq8wA123BBQJUxD0DjANXLiQW03459gKpCgKoDATVTAEDTWgLUnrwxgHaJgNoT2gHaLQMqtI0A6gG1r8ysASr0TG8OUOEAlUNAVXFARb+wLEC7YoAKfXbkAdpP6AMBFSmA+tkEoCGgr8u2AVVbgFozFgC1J3BpQPUoqYDKIwFVE0DdtkcDas07BFADoVmC/jiL0oDacTr7tLaMBdSNUg9QM7njAFVZgOrf/VmAykMBdSMBqCke0ODwHgWovXNyEKDK4XcDQIs9hG8dULECqHRv7JBDQNU6oOoYQO3E8YDqHXIJQKX5JQ2g0YB2K4Cq8oBKc6VIQOUeQGUA6GvBM4Dq1d0NqD+nQ0CNNAZQ955p+yBSVALUQHM4oPY38jygdluDf825LKBS7QFUH5dsQN3M2gGoiga0c6+hLQLaSQDNrwigMgHQ/nSfB1TuANQNMQuotNtREVBVBVCDipgDVO4A1B01B6g0bhQH1IBzE0DFEqDyNEDVMqBd54+/PgBjQPVVADS3TUDd7roUoDIFUDOVGgRUWN0jAVX7AJUZgDrbRoDaqXQCoO5UN/es9gBqpocFVIpEQOUIUFEcUHNMtgHVC5wAKhYB9RQC6GJVAZV1ALUyjQA1hi0D2nXRgNobbAMqCgKqDgW02wmotOe1Xno2oLJ/vbAxQO1T1YUAFdGA2jW3gJrnpvIAdTNIugVOATVXWwFUAOhiC4B27o8z8gE1k7wwoO58WARUT/hZQPXfTegpswmo3sp1QN0jVQuomgIqkgAVW4DKGUDNI8ULAqonS2uAdgcCKlMAVUmAdkcD2u/9x7o46d0eUHkyoHbFbgSocAOOAHUTdzegr4UpfabEAirMny/eCVC1E1Dz6LcEoMKMJcxsjgNUucM6A6j9h9ylW6BRUVUC9PkNgG4CKkNAX80Aaib9JqBmqa0AKs07hpoHVG4AGu7ZeUD7HTYF1B6ydwBUmnv68YBKM0ENoELOAaoGgJp18GjLBUDtoRoD6nb0BqB64ccCqvc5gO4EVGp6tgEVK4B2pwAqWgBUngKotPgMAO3M09bK/k6U0k+MKaD2OAGoBbS/ogNU2j1aDFAlzScrmf1+CqDBMQZQD6i7uzEF1DtVCtCuk6PT3J4/DQGq7HP3aYAqe/r3E3MRUDsFjWNmtDlA/TaPAO1X3Px22wRULAPqTujAxFMBtbtdTgC1SzeAmoW7K2wBqoSIAdQo2Rygagyoco+lcwG1k1H54zkBVIWADo4xgCYBqqoAGlgxB6g7SBUBVQUAVSUB1dN/G1DhAJVK5QKqWgZUbgDazQJqfwnFASqzAQ13yJ0BdV+6KQugRwBqrnEEoP3hLwqoiAVU/59SW4DKNgFVhQAVC4CKZEDdbu+X3QigdqPTANUbpPQ/LjsPqMwH1E2IFEAVgBZKAyqEGgCqfRAOl4YBVQNAw9eGzJ+RdMEtlgHt13gBUDOxLaBSv+NkG1CzYWaVlTwYULfa4ZSvCqhMBFTf8zWCNgSokWcMqJ3HuwD1v0kTATXzUE0BlR5Q6e7tWkD1bhYAWqllQPuDLdYA9X42D6i9L7YPULcS6YDqU6pfTXtOvyOg5vrFALWb5E7wfYDajyzdAaiWxIhSGFCZD6ioC6i5hw6gS4CqAoAabQC0PUDVAqAyH1Czd7cAlXa59ijZNTgb0MH0Og5QIceAhjt6BVAZXk8qlQ7o4HgK97KRjAG06+wn/QJoSUDtO+5lNKDhZDBLLgVoNwRUeUClnAX0dU17z2gEqDInXQKgdjfFAarXtjqg9n1TNQDt7FSqCKgQ9v11m4B2XQqgVhS7+3YDqolJB9SNuQposG4xgCrzkWgWUDkPqFt+BKAdgO4EVJUC1F7fLjkSUP30fwio4eMsQFUIqNtNeYD6N5VWAlQmATq+w2P2+BRQmQaoNNecAKrcuboD0C4WUD31SwPaVQNUhusGoId2N0Dtya2U42MVUH+mZwDq5l9lQM0bYbcBtU9DCfsOhEhA/cqNAbXn09GACuHWUi4DKnMAlQZQ/YMlQO10BVAAXSsCUCHsS5Oys++OXAQ0MGYNUDOJrgOoXAPU/P9ZgEr/Z2InAWpO6lhAhWFiN6DiJEDlEqDmxRR5YUDNogA0rihA9cy/BqBmwLKAmjWJAdTRmQao/XO5EoCKrssHVHbRgBo7lH6JbApoJ+MBdefiLKDyAEA7+48L9gtQK4DqOTADqJSrgKoBoJ35DHs5ANRMBQeoHlz4SZMGqFwH1EwiAM0vClD5xoCG+qUD2nUyCVC9EVUBlWuASkuOqWVAlT4C9wNUDgDtigIqlwAVAJrVDKB67i4DaibcANCeDaU/x9Iao6xJ5tkqYf9yOwNQjcOBgFohEwBVewGVW4CK/YDa1bCAqhlApV+ryoD6g10cUL2AIoBKmQuo8iw1AaiUC4DauzeLgNr3NAWABtPJnhoAao5UHqByBdDuGEDtC1TbgKpCgNo5GAAq7TOhSYD28z4NUNUPWhJQuyvdcY0GVBUG1Hwsymg9kgC1+zgTULcMKQeAqhBQpTIB7cwftZkb9evUEKAyAFTvbACd7SEmgHa7AJWbgNoT8E0AVdcCtCsGqF3lIoC6F5kcoP5QVgE0WIaUs4D2i1Ai/KuMZED9jTxqZQBVFlBxHKBd/wEQ7waoGADaRQE6mlQO0K5TGYBKO9IcoNI+JLaPdPIANUf5qoD6mxpA5Tygdq9GAyrsX37FAyrcEpMANc4sAWr92wmoPB5QtQSoMv9Glwe0CwANTp+qgNrdvwWovbmbqkmAKr15ALoKaOiCrAtoiE4pQMUUUA2WHtADKrcAlZmAWt4iAFUBoOaWHlChZgCVwV4tBaiFrRagyq3YIYDKBUClBVQjIs2ngcQD2tkPM7k4oMrt8Qmg5ustQP0SinUlQPVEWgJUA7MKqMgG1B9S6f8jVwEVc4DqG1YGVJYAVN4IUFkDUH8uGkDVLKD9CoWA2jV2gOot7mRFQN2KRgKqkgDVc6IGoPbICn8KmHUYAOqGANBhQ0C7MaCys3/tIZoEdLCaqp9m6YCaAe0zrWuABufVhQG1l1UAVNhfR2aFcgGVOwB1a2wAVSoFUD0JlFW4EKDCKBOcO3OAul9wLQA6mJr2awAdNgbU/rosBKhoH1AzjF1uMqBmnfRV6wJq+TkPUGleddoE1K6LOwZbgNoVc4soCagaACr1dLeAml03AtQupDCgwi9vCmjwCMH8Ut4LqDB/35QDaLhY//U8oPK9ATW/ktcBlWuAyjlA/flUHdD+uyVApV0FAB0DGhCRC6jyz/7sANTxNw+oTAVUCDkEtJsF1K9iGqBGqU1A9arWADScVcEcqg2oDA4KgMoeUBkJqAwBdcfCtBdQMQR0YI6SG4DKHEDNw+FsQO1cXQdUtgVosCmyLKAyFVBp7r3qo7UFqLnLNwFU7gZUtQ+onADqrztI2NfRswBV/gktAI1tAqhoD1D7P1UB1ZPvWoDKSEAnR70JQIVIAVQZCIW+pYoHVNYH1HMTC6hUKgvQftXMgieHFUCPzwKqZ+ASoP6cTwVU2NMyFlDz+C4JULOGJwOq7yPVA9QehmsDagaVqYAaAMQSoHY2hrNI6ol6H0D7fZ0BqN7pKhwLQMvkAe2qAyqLAOovCgAVMYDKZEAHxBUC1D8amwLq4DwGUHt6RwKqN/BEQP2KnAeoNG9FkFUAdbPEHF77m849tRkBqF26B9RNT1kNUHPCvC2g2kRlADVzKAVQu/M2AA2fMloBNDxkC4Bqf2YANetzHKByFVA1AFQvJBJQMQuovTPRAKD6qR1DQXFA1RjQYEUWAVXBLLocoP7w6GFnANWEJgEaTM9grDVA3WQK97xbtxlAJYAuA2pnQDVARUlA3fpMAZXXAvSlRGBVGqDqEED1Ww0BNAJQ/es7CVD7SSDyBECDLdoGVJod/8aA+o0fA6rcM4ybgMpcQEU0oCofUL2oTUC77nxAZQKgojyg4e63X+0BVDhAzXtFiwLq1noAqLtvpP+u/ZqAdnUBdRNAAmh+D/NW6lVADX/xgPZ+6g9VnAXUPUiIANQ/oNgC1L8Uq282AVRaFdcA7U4GVMlTAXXrIsOvjgTUCXMHQGUIqJnro20OruwOj51f9QC1Nwh2K4BmZABVsWGoR7sAABzJSURBVIAqJSMBNd756Sn0FQB0FlC73ecBKt1pswqoagNQPRFLAaonYjSgevGVAZX2yYkMQGU8oP7WI0Ank8DuUjl7Yw2oAtBVQNVuQLsCgNozczeg5r1x0oybDaisA6jIBFQ/JVMSUHdUVPgJog5Qe5jmALWHdy+gHvGLAioXAZVKyQqA9psDoHWLAVSlAWo+d34AqDoHUL3o8LIBoCIXUCFCQM3N5wCV9kQdLKQAoPafkzAXNAaoWb4H1Oz8wS5tDlA//HsCKucBHQ60AmgHoPOAGqviABVXAFQcA6gc7SS3Vg0BGlyhFKD9PhR2gauA2tkTC6h9M2g0oNIDqvR0lpcBVFr29wKq4gG1K5oHqATQGoDqxVtAuxGgYg+gyv6hRSFAO/uRzOZV/AxALRR7ANVn5gyglszGATXn1hBQOfqdVA5QuQSolB5QVQtQv99kk4BKNQY03JhdgAaX67UC0BKA6hPNAaomgHoUh4D2S7o3oHIDUHMH4R6A+mMw3PvVAQ12635AR4cxA1B/JAaA+k1rD9AuAtBBbwtodwSgMgZQ0/mA6qnaf5MOqHuoWhVQc/4vA2rPg1qA6vXcC6jdNwGg0hOwBKg6EtDwka+5zP7NZSSgwdI7IWeOxiKgoklAZ8a1q/aWgHYOUBUJqFwH1B60/lwzk8ED6s/1ZUDHh+xIQO0nT24C2nWD9fTXLAmov8OyBqg4A1BVD1BNYQygbqObBTTY390SPnUBdXN2sIL2BqmAzo4rA0Afb/aPyll5qgCq7GQ4AdDRIyZzUVOADmajeWgeA6h7GHk1QM11lgHV69A6oO6yAwDV+/UYQP2LvgAa26PTn/JWBFD5xoD6mVUHUL/N9QEduum+OA5QJXcBGh4X1TcG1N6Fbx5QcQKgnXnTf7DfwyEANOwAQP19U7M0cSKg7jRfAFQWAVTvQL1j6gAqqwBqv4oDVM0DGmxYo4DqmVMAUL0x+wE154q/urTQA2jrgGooBoDKXYBqlJYBNW8jHwIqB5IAaL9qlwHUHaYIQP0uLQKovkoqoGoRUGknvz8mo8O4Caiy50ZZQN3VygI6WAsATW4KqDkFPKDuhNgE1IjjAZVxgMrBURoD6uZTbUAFgI4AHTwuGALqn6Hx6xcC6pZYGlBVAlBlGSwDqH/peR3QBXymgIo8QGXwJrAoQEdrAaDJDQDtOn8KGJreB1AJoOUADcYpBqjdQwuAGri6EVMbgJr5vQioXccSgI4tGq2l/84CKk8DNLy3DKBrWUDlGFApswE1s2gMqD4nKgEqzXO5g5u5fys5uGgI6GAbtgFVSYDqu0QTQMUmoObMrQqoX+cTAFVm8KYAlbmAqmCbVwFd9BNA12ocUJUL6OAwBIDaWeSuq5clCwBqD18xQAc30JMvClC5Aqg9XeyUGu/yMoDKbUDlrQGVfmJkASpbBjQ4PgB6KUDlCFBvzghQtQqo2ZnSnZ7mTtkSoOFRWQRUusM3BFQWAtScUqUAtdN9vMv1FUsDKj2gbg3qAarOBnS4wqmAylRAB8/02OVfCVB3/VqAyncH1D7yHAAqLgCovrgSoCpYl2RA/ek0qAag9hrHAGqe5V4CdLi/TwA0vJEFVP+qPQ/Q0cOsUU0COpkE4XLmNuFtARVjQPXP7OmVBqiMAVSGx2kK6HAdDwNUE1EQ0NGJEV5xBlCxE1B3/h8CqMwC1Lw+pvzTMVIeAGj/02KAukXfDVAFoKk93AmxD9Bw6pcGdHBm3xZQOQFUZQCqr/5+gMpSgJpFHQyorAeo3xS7gquA+hcgzQ1G4wDosDRAZQ6g/U89oNaPkwCVhwI6N1eXAH0t7nBAg28OAdQ9BB7s2iVAJztOv8MjADT48WGA+icezMa8F6BrWwCgc4DaL1Xwyno0oCYNqBPgbQCdaRFQKY8GdPhNU4DO/+Y5DFA/4hhQv64hoMFK3ARQu7YrKz3dAgA9ClAZ/KJLAtSPFQHo+Bx7F0BloEIEoJP1ngXU7BflUbGA2lNuAqgcAOq2uR6g9pO7B3t0DlD7/op5QMMRywG6RlEGoHOLKQOoXVb4n80AdAvQ8GH5WYCGY+0DVGpAhwO9JaDhpRGADk5F+zDCjCzcCewXeTCgYrRPtwBVpQFV7gZVAZ0vAFQC6BFFA2p3fWOA2pssAyrGF60A+lqQ8mfnCqDyTQGVo5Xuh+zsyJPtmQdUb2qw97MBDX/sAJXDS5X5s463AlQvBkCr1wOqNgG1u71lQK1vwa3Ghz8AVL9n9VKAijlA3V06GZ7/xwLq/pMEaHC04gC1j4MmSNqFACiAHt3DUbcKqH1upxyg7uyOAHRywscDOrecOEDlLKCD/QKgxQAdLK85QAcz+RxAw40B0HaqAqhMAdRpYDsQ0MlM2gB0tF8WAF3waXjFAwGdrkJrgI6XN51dbuSjAVWNATq+7rD9gAYbo28BoKslA/r6JgNQmQLoZC1rADp7lkYCKi8E6FwtADrvQE1Ag/3TIKCDnXAWoCrcGACNKBFQneZnG9DRj8eAynlAZ2oLUPcdgLohu/ntyQZ09hzPBNTN3k1Ag11gB8wHNLjRNqCjnQCgQVcHdDohegw3AZXjHw8BNZd2g5NzoS1ApXlQvg2okgbQhceQ/sPKAPSigI4v9YD6LTwAUFUQUPtekjKALt78HQH969fH4/HLn+77f35//PxHwu03AZ39jSovCmjwKvp1AZUAugLo9NLTAB3uhjMA9X4GgC7f/A0B/XjovtkLmgXUfhpjUUBVMqBuuwoAKk4D1O6yiwM6uw7ZgM7fuRoAKj2gygMa/CAJUMfwPKCj3XAIoANBFwFduvH7AWr99HdC7wro5PvhadZPtG1Ag+2aX8srAirPBTSUZwnQcNgF/dxVogCdvW0UoO5SC6jyq/YmgC7e+O0AfT1+//H876cXtCygMhlQ98PxQLsAHadn2AygoyvN3xpAjwV0uF+2AB0d2TAL6OJrIJuABpfWBXSyVYmAuvHNUc0A1HzYA4Au9/H46V/9F082jaAlAe1LA3R4o8H1DwB0cqX5WwNoeUAXtucegA5uuwno/Fa1AuhaxgG/LP+/210Q0CeW7rnPDyMogIYrNN/qL+PwGcXzAZUHA+rXMfyPbArQRT5aBjRpjtcBdHvc0YLN5IpcabtqFwL079/6B/A6I+g6oI9pAaD9rl8AdDJxTwVUxgC6mAd0/ofNADpY1AmABmsXC+h0kZmAzv3wdcuNF0HaBDRxjtcANKbxlUXK3Wa7hKsC+hL027mArk6jJUDFeYBu/HQPoNvDLwHqrjMFNLhFo4DOLHLyi2UHoK8AFEBLNQT09Tzo9x0P4aU/FcIrZAI6GWgeUCFKASrKAarcGIP3ZccDGjP8OiZ5gI45Ox/Q4dUjAJVbgKbimgaorAXoaN6vB6CDDnkOVH/7+FEA0GFZgM4tpyVA137qFtrNfb5o+F1dQMcHpXlAVzfXXwdANxsD6j5lH0DL9vkI74K+3tX00/9sFVD7+HIMqDwe0PUuAajZjQ0Buth4G9sBNFzaEqCyEUDDpQNoqV7vA/0efP/VP6t5DKDTw7hxKiknwPiQAOjcdXYCOl3jxU2ZuXQCaHDcxoAubae/4er3cyuRCejsc+9LgA6/WHwmYhNQeQSgg6WnARo76MyCcwCVVwK0fwd9+DD+qwag9r8tAuoXAaBycK5N13jzBwB6P0BjhwwWHy4sFVAprwVoL2h4H/R1n7QsoLZSgM6cQA0DOhkz/G4MaOQAA0CXrwOg8x0P6MJNAXS2awH6euXo++CCTwDdl1voeIEbgEZnT7qN69wX0NWrmyO7fIXDAZ0++e+/BNBJFwN0bwC6Psbi93sB3bxOY4DKUoButLnAEwBdGmMW0IVVA9DsAFRf/3qAzowZfgeg7QE6f/UYQBeGnwy3Duhi1wQ0/cwE0KXeDtDZMcPvjgVUXhbQjSvErFpQ0htzogH1w28NdxVAuy71d9d0wQC6GYCmjRl+l+dnKqDD8c8EdG79qz9xMikZUJkCaMwC3wfQpLU2AehCABqH02ZJgI5HHN4PPBLQ+fVvHlBzmxYAjR5iAuhw7IqAThcHoOvdCdC0RSQHoDPrX3yvHw5o+rJuC+js8gB0tQMBVXUB3T1XosZc+C6looAOF3g8oMU7GND0DgE0vNHJgKbeAkAXAtBu+F0DgI6/PAjQjZXfFYCOb5QEaPh+gsq/6hYC0IXeHtCZNcjq6oDWDUDHNwLQ0gss2R5AR1fYAWjygQbQOEAXFv9+gG799k9alv/SArp9MwDN7qaATgLQuI4DdDEA3QNo8GX8tAPQ7ADU3B5A+wB0reYBHSwFQCcB6EIAOl2DrAB0rSqAbj+Bn9UJgPr/XQpADy0F0I1FAWhcpQCdWSCARt7oooDGBKCHNgB036QC0LiiAZ0bce7GwWUAGnejawGadBsAPbQA0L2TKgLQ1wAAGgfo/IgA+naAJmb239JkqR6A5rb9FICaBzTjQBtAwyUAaPxaLQE6XOQZpx+A7r0RgB7alQEdLuGdAU1dq+2rAWjUUlMAzRgAQF9dB9B9i9qY+QBqKg5o+lrF/eXMGX7eFtCsAPTVVQCtXFVAj58rlwY0IgCNWWp1QNNvA6CH9ijx4D0u825mAN0HaKW1mlmFUktKKgLQjKVeFNCcAPTQ+s09GNDJTwA05joxIxYE9KTqbAGAHhaA1mry93QuAI25DoDmB6CHBaC1AlBdzJrOXeddAK2xVAA9LACtFYDWH/H6gNYJQA8LQGt1N0CPHQ9A9wSghwWgtSoL6O5FXC4AzW/z08Azl9ritNNTIOItG3UC0FqVBLTGIloPQPOrM8NbB/SUALRWALovAM3v7QA9bx68J6BHDAWg+wLQ/N4J0B5PAD2o8pu7GIDuC0DzA9DDAtBaAei+ADS/dwP0xGkAoLUC0H0BaH4AelgAWisA3ReA5vdWgMrYfxqrSgBaKwDdF4DmV+t9Jm1Ou1NnAYDWCkD3FQfoAStywQD0sAC0VgC6L+5d5vdugJ44OIDWCkD3BaD5vRegpwagtQLQfQFofgB6WABaKwDdF4DmB6CHBaC1AtB94Wd+AHpYAForAKWzAtDDAtBaASidFYAeFoDWCkDprAD0sAC0VgBKZwWghwWg1QJQOikAPSwArRaA0knV+shwpt0kAK3WCqC7l81MphNi2k0C0GrV/KeXmMl0Qky7SQBaLQClm8W0mwSg1QJQullMu0kAWi0ApZvFtJsEoNUCULpZTLtJAFotAKWbxaybBKDVqgtotUUTUXQAWq2agBJRCwFotQCU6O4BaLUAlOjuAWi1AJTo7gFotQCU6O4BaLUAlOjuAWi1AJTo7gFotQCU6O4BaLUAlOjuAWi1AJTo7gFotQCU6O4BaLUAlOjuAWi1AJTo7gFotQCU6O4BaLXwk+juASgRUWYASkSUGYASEWUGoEREmQEoEVFmAEpElBmAEhFlBqBERJkBKBFRZgBKRJQZgBIRZQagRESZASgRUWYASkSUGYASEWUGoEREmQEoEVFmAEpElBmAEhFlBqBERJkBKBFRZm8HKBFRuUobBaBE9DaVNqppQI/s4KcLGO6yw9164xguMQA1Xf5IMtwtR2O4tocDUNPljyTD3XI0hmt7OAA1Xf5IMtwtR2O4tocDUNPljyTD3XI0hmt7OAA1Xf5IMtwtR2O4tocDUNPljyTD3XI0hmt7OAA1Xf5IMtwtR2O4tocDUNPljyTD3XI0hmt7OAA1Xf5IMtwtR2O4tocDUNPljyTD3XI0hmt7OAA1Xf5IMtwtR2O4tocDUNPljyTD3XI0hmt7OAA1Xf5IMtwtR2O4tocDUNPljyTD3XI0hmt7OAA1Xf5IMtwtR2O4tocDUNPljyTD3XI0hmt7OAA1Xf5IMtwtR2O4tocDUNPljyTD3XI0hmt7OAAlIsoMQImIMgNQIqLMAJSIKDMAJSLKDECJiDIDUCKizACUiCgzACUiygxAiYgyA1AioswAlIgoMwAlIsoMQImIMgNQIqLMAJSIKDMAJSLKDECJiDIDUCKizACUiCgzACUiygxAiYgyA1AioswAlIgoMwAlIsoMQImIMgNQIqLMAPSvXx8/3Dd///Z49r3aSM++BZfUGu7v337+YzzuT/+qNu5wOPn1WniwU0sOF7Mt5YabjmYvrjJlpsNVnTJ64d8nl1SaKdPhqs4UO+bakSow3NsD+tqHbh9/PHSTU6ZA//xuFl5/uOdQXjQ9SV65ywqPOxjOnCd1hovalmLDzYzmLq9wDKfDVZ0y04VXnSkz21Jzpug2jlSJ4d4e0I/gkH7aYzw6Y4oNNJxC1Yb7CJbozwp3Yelxw+H8WfF4/PJn4eGitqXYcDOj6epMmZnhak4ZD5pdeNWZMh2u6kwxrR+pIsO9O6BfoyP6OpSvy76t3yxvoNdCX9NUH69aw/Vz1U2J1yzpH6N82GEKjzsc7rV5/engxi04XMy2lBtuOpqu0pSZDld1ytjh3AGrO1Omw1WdKbr1I1VmuDcHVP/WNfv4I9i1xR/Ef9jnWtzDikrD6V/sVrTgQcynubTsuMPhXoOYuxNf5qtyw0VtS7HhZkZzl1eYMvMbV23KPH/tmQU8F95/VXWmTIerOlOkHWrtSJUZ7r0BfT1799+ChzDBnfvSr+x8uIP06X4XVxiunzT/wT8p+eV/u77uKv4oPO54OH+i2C8LDhezLeWGm45mv64yZWaGqzllnmjY4T71aFVnynS4qjPFLnbtSBUa7r0BfR3MT7uPg4P8/NL8dizW4Gz4UW241+OhH8GrOuHc+OgHLjrueLhg4XJyyd7hYral3HDT0ezFVabMzHAHTRkzWuWZMh6u6kzp2zhShYZ7a0CfO+67dPs4mEHD15WL5JZuf/XWGe7zNSsWlqdPyKLjjof7mvwur7RXl7alznCeskOmzMdohlSdMsE9sdEKVB2u+kzZOlKFhntnQJ/77fmLJ9zHweO00k+Cvh7sfgv+W3O4+QnxHPn1e7b8uMM7vD/MK64/gkuKDvdqcVuqDGdGkwdNGTvcIVPmM3iherQCVYerPVM2j1Sh4d4Z0I/hjgwepwV3OYrl3yXyrfZw84CaX7nlxw2Gey3QvjtEX1ZnMxe3pcpw/s7KIVPGDVd9ynyEbyuarEDV4WrPlM0jVWi4NwbUPIj4DA+p/VkFQN3b+tzLgtWGmwXUPtFTftwhoP91+O66Kpu5vC01hvNPkR0yZcJn5CpPmXlAq82UEaA1Z8r2kSo03PsCah8oLezj6e/lnfl3Rdt3UVQbbg7Q51lhf9GXHtcPpx+S9bvVvruuxmaubEuF4dxox0wZP1z1KWPf3T58BaXaTAmHqzxTIo5UoeHeF9APM00Ougf6Yd4u/Jo5/bKPvQf6Ffx5S717oP1pYR7tmnfXVdjMtW0pP5wf7ZApMxjuiCnjnmGdrEDN4SrPlIgjxT3QfU2fXq4KaPBmXfMO4kMBHZyWVR/CDx5+Tn7RFxetOqBfj+ErZIMvqm7cUVPGvbV9vAJVh6s6U2KOFIDuyr8L7JhX4Ydvmqj2erFb3hDQcLZWfRX+I3hzin4aqvhw69tSerhgtCOmzGjjjpkyo3eAVpwpwXA1Z0rUkeJV+F19PoZ9H7wzrfz7QMPfcfrQVRxutLzXwyX/IK38uAvv2/+qsVe3tqXscIPR6k+Z4cYdNmX8YmvPlGCxNWdK1JEqNByAun1c9S+RpmdDxeGmny8XvGe5/LjDN9K7E/Crwl7d3Jaiww1Hqz5lRht32JRxlFSfKcFwNWdK1JHiL5F2NbOPq/4t/PDxWPG/SR82AHT8UQnlxx3+KWf44Y6lN3N7W0oONxqt9pQZb1zVKTP3h6oVZ8rs34lWmylRR4q/hS+Ufy6k5qcxffmPbf2s8qlIYSGgw5cIZI1xw+Hcwt0ZUnC4mG0p+vlICwuoMmUmw1WdMoFf9suaM2VmuKozxbZ2pPg0pjL5fVzz80Dd2zb8+zfqDReIFn6QkKn4uCGgduH90gtvZtS2FBtuZjRbjSkzHa7qlHkt073Tx41SbaZMh6s6U1xrR6rMcAAavBrn7/qX/igR+7mZulofwG0bPikZZv8Muei4g2cMgodPpTczbltKDTc3mnRDFJ8yM8NVnTLhwt3HN9ebKZPhqs4U1+qRKjIcgAb72P3pR+kH8K/8HzaP/1Cv+HDjPw0aM1D130T6qrWZsdtSZrj50XQVpszscFWnzOgP7WvPlPHf9cuKM8W3fqT4N5FKFO7jI/5VzvC3XaXhvGjhv3MTMFB23PG7QD7qbGb0thQZbmG0vgpTZmG4qlNGL9zoVX+mDIbTVZopvo0jxb/KSUR0XgBKRJQZgBIRZQagRESZASgRUWYASkSUGYASEWUGoEREmQEoEVFmAEpElBmAEhFlBqBERJkBKBFRZgBKRJQZgBIRZQagRESZASgRUWYASkSUGYASEWUGoEREmQEoEVFmAEpElBmAEhFlBqBERJkBKBFRZgBKRJQZgBIRZQagRESZASgRUWYASkSUGYASEWUGoEREmQEoEVFmAEpElBmAEhFlBqBERJkBKBFRZgBKRJQZgBIRZQagRESZASgRUWYASkSUGYASEWUGoEREmQEoEVFmAEpElBmAEhFlBqBERJkBKBFRZgBKRJQZgBIRZQagRESZASgRUWYASkSUGYBSS/3z++PnP/qv/v7vObfXt/JLIaoagFJLOfo+Ht8zbm5uBaB0UABKLWXp+3rkAGpvBaB0UABKLbYPUKKDAlBqMQClSwSg1GIASpcIQKml9LOXTwj7fvSXfQVfv14n+uXP1yU//Ut/93Bf+1sFz4EObqwv/3xd8sufx24Y3TMApZaaAvrXr+YbQ+IT0P9r0fx82F4ezgD692+DG78u/9+/Bgsn2heAUktNAHV+Ptz7m/69v+hb6Gf/0H0KqPPT3Ph5uU/fbSXaE4BSS43fxvQir5fuS5upH7Trx98vHr/bL/qLxm9j+tCy9tJ+kxbQH+YSni6l3QEotdQY0C/3bOVTyV7SD3fJlyG1v5s6Zvf1/fNiczfTfPUC1DwT8OluTJQfgFJLjQH98I+0zUUfM3cdn7jOARog+dn/5AWoufGTVF5Hot0BKLXUCNCnjI65J3kvDj8mL//0D8znAA2uqm/8vNx6DKBUIgCllpoCGvYi7yN89efL/WgG0IBL4yWAUuEAlFpqBGjwGvwMoOFP5wF1fxGv78oCKBUOQKmlpoCOmfOA2runP/8x/xwo90CpegBKLTV9CD/+WCUP6Kd7SX3hRaTgOVD9ij2AUuEAlFpqBOjr9aHRS0YO0EDDr/kXkYJX4T/sq/AASiUDUGqp8duY/L1M/zamCaCvx/Kx7wMFUCoZgFJLBYD2vvXPc/b3QT8e9o301sAPw+an+8NMe6u5v0R6/QBAqXAASi1l6dMvsP8I36g0eWt9+CN9ob3Vyt/CAyiVDECppSx9+q/Wh+9k0n9DFLyN6cP+5D+Zp0rtrVY+jQlAqWQASi3l6Ost1C8B6Q9d8i8H+TfSa1y/+z+LN7da+TxQAKWSASgRUWYASkSUGYASEWUGoEREmQEoEVFmAEpElBmAEhFlBqBERJkBKBFRZgBKRJQZgBIRZQagRESZASgRUWYASkSUGYASEWUGoEREmQEoEVFmAEpElBmAEhFlBqBERJkBKBFRZgBKRJQZgBIRZQagRESZASgRUWYASkSUGYASEWUGoEREmQEoEVFmAEpElBmAEhFlBqBERJkBKBFRZgBKRJQZgBIRZQagRESZASgRUWYASkSUGYASEWUGoEREmQEoEVFmAEpElBmAEhFlBqBERJkBKBFRZgBKRJQZgBIRZQagRESZASgRUWb/H/mf98WeCjZ1AAAAAElFTkSuQmCC" 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" title="plot of chunk unnamed-chunk-23" alt="plot of chunk unnamed-chunk-23" width="672" /></p>
<pre class="r"><code># Check histograms
par(mfrow = c(1,2))
hist(out$sims.list$beta[,1])
abline(v = 0, col = &quot;red&quot;)

hist(out$sims.list$beta[,2])
abline(v = 0, col = &quot;red&quot;)</code></pre>
<p><img 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" title="plot of chunk unnamed-chunk-23" alt="plot of chunk unnamed-chunk-23" width="672" /></p>
<pre class="r"><code>dev.off()</code></pre>
<pre><code>## null device 
##           1</code></pre>
<p>There are many ways to extract different the same informationg for the output object. Before we just grabbed summary data, but we can also grab the MCMC iterations and perform our own sumamry analyses.</p>
<pre class="r"><code># extract mcmc samples
all.mcmc = mcmc(out$sims.matrix) # combine chains together
dim(all.mcmc)</code></pre>
<pre><code>## [1] 1200   65</code></pre>
<pre class="r"><code>colnames(all.mcmc)</code></pre>
<pre><code>##  [1] &quot;Res[1]&quot;     &quot;Res[2]&quot;     &quot;Res[3]&quot;     &quot;Res[4]&quot;     &quot;Res[5]&quot;    
##  [6] &quot;Res[6]&quot;     &quot;Res[7]&quot;     &quot;Res[8]&quot;     &quot;Res[9]&quot;     &quot;Res[10]&quot;   
## [11] &quot;Res[11]&quot;    &quot;Res[12]&quot;    &quot;Res[13]&quot;    &quot;Res[14]&quot;    &quot;Res[15]&quot;   
## [16] &quot;Res[16]&quot;    &quot;Res[17]&quot;    &quot;Res[18]&quot;    &quot;a[1]&quot;       &quot;a[2]&quot;      
## [21] &quot;a[3]&quot;       &quot;a[4]&quot;       &quot;a[5]&quot;       &quot;a[6]&quot;       &quot;beta[1]&quot;   
## [26] &quot;beta[2]&quot;    &quot;deviance&quot;   &quot;eta[1]&quot;     &quot;eta[2]&quot;     &quot;eta[3]&quot;    
## [31] &quot;eta[4]&quot;     &quot;eta[5]&quot;     &quot;eta[6]&quot;     &quot;eta[7]&quot;     &quot;eta[8]&quot;    
## [36] &quot;eta[9]&quot;     &quot;eta[10]&quot;    &quot;eta[11]&quot;    &quot;eta[12]&quot;    &quot;eta[13]&quot;   
## [41] &quot;eta[14]&quot;    &quot;eta[15]&quot;    &quot;eta[16]&quot;    &quot;eta[17]&quot;    &quot;eta[18]&quot;   
## [46] &quot;mu[1]&quot;      &quot;mu[2]&quot;      &quot;mu[3]&quot;      &quot;mu[4]&quot;      &quot;mu[5]&quot;     
## [51] &quot;mu[6]&quot;      &quot;mu[7]&quot;      &quot;mu[8]&quot;      &quot;mu[9]&quot;      &quot;mu[10]&quot;    
## [56] &quot;mu[11]&quot;     &quot;mu[12]&quot;     &quot;mu[13]&quot;     &quot;mu[14]&quot;     &quot;mu[15]&quot;    
## [61] &quot;mu[16]&quot;     &quot;mu[17]&quot;     &quot;mu[18]&quot;     &quot;sigma&quot;      &quot;sigma_Elev&quot;</code></pre>
<pre class="r"><code># all.a = all.mcmc[,grep(&quot;a&quot;,colnames(all.mcmc))] # alternatively out$sims.list$a
all.beta = all.mcmc[,grep(&quot;beta&quot;,colnames(all.mcmc))] # alternatively out$sims.list$beta
# all.sigma.elev = all.mcmc[,grep(&quot;sigma_Elev&quot;,colnames(all.mcmc))] # alternatively out$sims.list$sigma_Elev</code></pre>
<p>Compare with lmer…</p>
<pre class="r"><code>mixmod = lmer(rich ~ scale(logprod) + (1|elev), data = div, REML = TRUE) # standardized predictors
summary(mixmod)</code></pre>
<pre><code>## Linear mixed model fit by REML ['lmerMod']
## Formula: rich ~ scale(logprod) + (1 | elev)
##    Data: div
## 
## REML criterion at convergence: 86.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.3761 -0.5986 -0.0094  0.5593  2.2647 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  elev     (Intercept) 3.32     1.82    
##  Residual             7.42     2.72    
## Number of obs: 18, groups: elev, 6
## 
## Fixed effects:
##                Estimate Std. Error t value
## (Intercept)      10.833      0.983   11.02
## scale(logprod)    4.752      0.956    4.97
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(lgprd) 0.000</code></pre>
<pre class="r"><code>apply(all.beta,2,mean) </code></pre>
<pre><code>## beta[1] beta[2] 
##  10.904   3.689</code></pre>
<pre class="r"><code>cbind(fixef(mixmod), apply(all.beta,2,mean))</code></pre>
<pre><code>##                  [,1]   [,2]
## (Intercept)    10.833 10.904
## scale(logprod)  4.752  3.689</code></pre>
<pre class="r"><code># coef plots
mixmod.beta.freq = fixef(mixmod)
mixmod.se.freq = sqrt(diag(vcov(mixmod)))
mixmod.beta.jags = apply(all.beta,2,mean)
mixmod.se.jags = apply(all.beta,2, sd)

coefplot2(mixmod.beta.freq, mixmod.se.freq, col = &quot;red&quot;, varnames = names(mod.beta.freq), xlim = c(-10,14))
coefplot2(mixmod.beta.jags, mixmod.se.jags, col = &quot;blue&quot;, varnames = names(mod.beta.freq), add = TRUE, offset = -0.1)</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk unnamed-chunk-26" alt="plot of chunk unnamed-chunk-26" width="672" /></p>
<p>Maybe should have used a different distribution given the response is counts of species richness (e.g. Possion or negative binomial)?</p>
<pre class="r"><code>mixmod.pois = glmer(rich ~ scale(logprod) + (1|elev), data = div, family = &quot;poisson&quot;)
summary(mixmod.pois) </code></pre>
<pre><code>## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson ( log )
## Formula: rich ~ scale(logprod) + (1 | elev)
##    Data: div
## 
##      AIC      BIC   logLik deviance df.resid 
##     95.5     98.2    -44.8     89.5       15 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
## -1.461 -0.629  0.022  0.535  1.935 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  elev   (Intercept) 0.0158   0.126   
## Number of obs: 18, groups: elev, 6
## 
## Fixed effects:
##                Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)      2.2557     0.0971   23.22  &lt; 2e-16 ***
## scale(logprod)   0.4921     0.0942    5.22  1.8e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(lgprd) -0.288</code></pre>
<pre class="r"><code>#################################################################
# JAGS code
{sink(&quot;genmixedmodel.txt&quot;)
cat(&quot;
    model{
    # Priors beta
    for (i in 1:K) {beta[i] ~ dnorm(0, 0.0001)}
    
    # Priors random effects and sigma_Elev
    for (i in 1:Nre) {a[i] ~ dnorm(0, tau_Elev)}
    tau_Elev &lt;- 1 / (sigma_Elev * sigma_Elev)
    sigma_Elev ~ dunif(0.0001, 20)
    
    # Likelihood
    for (i in 1:N) {
    Y[i]   ~ dpois(mu[i])   
    log(mu[i]) &lt;- eta[i] # in jags you have to specify the link function
    eta[i] &lt;- inprod(beta[], X[i,]) + a[Elev[i]]
    
    # Residuals
    Res[i]    &lt;- Y[i] - mu[i]
    }
    }
    &quot;,fill = TRUE)
sink()}
############################################################

# Initial values &amp; parameters to save
# inits  = function () {
#   list(
#     beta       = rnorm(ncol(X), 0, 0.01),
#     a          = rnorm(Nre, 0, 0.01),
#     sigma_Elev = runif(1, 0.0001, 20))  }

params = c(&quot;beta&quot;, &quot;Res&quot;, &quot;a&quot;, &quot;sigma_Elev&quot;, &quot;mu&quot;, &quot;eta&quot;)</code></pre>
<pre class="r"><code># Start JAGS
fit.mixpois = jags(data       = jags.data,
           # inits = inits,
           parameters = params,
           model = &quot;genmixedmodel.txt&quot;,
           n.thin = 10,
           n.chains = 3,
           n.burnin = 1000,
           n.iter = 5000)</code></pre>
<p>In case of lack of convergence - update on the fly!</p>
<pre class="r"><code>fit.mixpois = update(fit.mixpois, n.iter = 10000, n.thin = 10) # </code></pre>
<p>Check output…</p>
<pre class="r"><code>out = fit.mixpois$BUGSoutput
print(out, digits = 3)</code></pre>
<pre><code>## Inference for Bugs model at &quot;genmixedmodel.txt&quot;, fit using jags,
##  3 chains, each with 10000 iterations (first 5000 discarded), n.thin = 10
##  n.sims = 3000 iterations saved
##              mean    sd    2.5%    25%    50%    75%  97.5%  Rhat n.eff
## Res[1]     -2.238 1.675  -5.515 -3.453 -2.190 -0.957  0.788 1.001  3000
## Res[2]      1.819 1.511  -1.530  0.897  1.984  2.875  4.333 1.001  2300
## Res[3]     -1.266 1.620  -4.873 -2.233 -1.127 -0.128  1.469 1.001  3000
## Res[4]      0.856 1.510  -2.504 -0.065  1.021  1.910  3.364 1.001  2200
## Res[5]     -2.102 1.668  -5.653 -3.112 -1.996 -0.936  0.950 1.001  3000
## Res[6]     -0.967 1.164  -3.337 -1.756 -0.910 -0.118  1.100 1.001  3000
## Res[7]      1.202 1.210  -1.724  0.516  1.345  2.064  3.134 1.001  2300
## Res[8]     -0.861 1.132  -3.132 -1.629 -0.815 -0.033  1.127 1.001  3000
## Res[9]     -0.718 2.348  -6.077 -2.109 -0.472  0.947  3.095 1.001  2200
## Res[10]    -1.485 1.605  -4.899 -2.478 -1.484 -0.427  1.677 1.001  3000
## Res[11]    -0.286 1.185  -3.029 -0.957 -0.163  0.538  1.694 1.001  3000
## Res[12]     6.054 2.273   1.123  4.590  6.217  7.691  9.999 1.001  3000
## Res[13]     1.046 2.492  -4.096 -0.586  1.084  2.817  5.530 1.002  1800
## Res[14]    -2.010 2.824  -7.979 -3.793 -1.738 -0.061  3.076 1.001  3000
## Res[15]     2.154 2.102  -2.157  0.755  2.181  3.650  5.958 1.001  3000
## Res[16]    -3.969 2.839 -10.029 -5.834 -3.897 -1.939  1.134 1.002  1400
## Res[17]     2.001 1.655  -1.551  0.997  2.114  3.174  5.015 1.001  2900
## Res[18]     0.613 1.714  -3.045 -0.457  0.711  1.793  3.727 1.001  3000
## a[1]        0.073 0.278  -0.433 -0.070  0.040  0.198  0.707 1.003  3000
## a[2]        0.087 0.284  -0.451 -0.052  0.069  0.230  0.654 1.002  3000
## a[3]        0.284 0.316  -0.151  0.080  0.228  0.430  1.040 1.002  3000
## a[4]        0.095 0.376  -0.457 -0.112  0.013  0.234  1.062 1.001  3000
## a[5]       -0.080 0.335  -0.875 -0.223 -0.034  0.090  0.521 1.001  3000
## a[6]       -0.453 0.428  -1.482 -0.670 -0.374 -0.140  0.076 1.001  3000
## beta[1]     2.226 0.246   1.689  2.123  2.236  2.347  2.679 1.003  3000
## beta[2]     0.389 0.197  -0.081  0.287  0.424  0.521  0.698 1.002  1400
## deviance   85.802 4.050  79.177 82.782 85.397 88.490 94.602 1.001  3000
## eta[1]      1.599 0.351   0.794  1.375  1.647  1.865  2.142 1.001  3000
## eta[2]      2.085 0.182   1.735  1.964  2.081  2.209  2.445 1.001  3000
## eta[3]      2.211 0.173   1.877  2.095  2.211  2.326  2.555 1.001  3000
## eta[4]      2.081 0.183   1.729  1.959  2.077  2.204  2.443 1.001  3000
## eta[5]      2.396 0.151   2.086  2.296  2.398  2.494  2.685 1.001  3000
## eta[6]      1.331 0.314   0.642  1.137  1.364  1.559  1.846 1.001  3000
## eta[7]      1.537 0.248   1.053  1.370  1.538  1.702  2.044 1.001  3000
## eta[8]      1.305 0.314   0.627  1.110  1.339  1.532  1.814 1.001  3000
## eta[9]      2.677 0.157   2.389  2.569  2.672  2.779  3.000 1.001  2100
## eta[10]     1.989 0.225   1.464  1.861  2.013  2.137  2.389 1.001  3000
## eta[11]     1.640 0.223   1.196  1.496  1.642  1.785  2.083 1.001  3000
## eta[12]     2.759 0.142   2.485  2.661  2.759  2.857  3.039 1.001  3000
## eta[13]     2.933 0.132   2.672  2.844  2.940  3.025  3.182 1.002  1800
## eta[14]     2.934 0.148   2.634  2.837  2.931  3.035  3.218 1.001  3000
## eta[15]     2.816 0.125   2.568  2.731  2.823  2.904  3.052 1.001  3000
## eta[16]     2.984 0.143   2.699  2.887  2.991  3.083  3.259 1.002  1400
## eta[17]     2.387 0.151   2.078  2.285  2.387  2.485  2.678 1.001  3000
## eta[18]     2.421 0.151   2.113  2.323  2.424  2.522  2.711 1.002  3000
## mu[1]       5.238 1.675   2.212  3.957  5.190  6.453  8.515 1.001  3000
## mu[2]       8.181 1.511   5.667  7.125  8.016  9.103 11.530 1.001  2800
## mu[3]       9.266 1.620   6.531  8.128  9.127 10.233 12.873 1.001  3000
## mu[4]       8.144 1.510   5.636  7.090  7.979  9.065 11.504 1.001  2700
## mu[5]      11.102 1.668   8.050  9.936 10.996 12.112 14.653 1.001  3000
## mu[6]       3.967 1.164   1.900  3.118  3.910  4.756  6.337 1.001  3000
## mu[7]       4.798 1.210   2.866  3.936  4.655  5.484  7.724 1.001  2700
## mu[8]       3.861 1.132   1.873  3.033  3.815  4.629  6.132 1.001  3000
## mu[9]      14.718 2.348  10.905 13.053 14.472 16.109 20.077 1.001  2100
## mu[10]      7.485 1.605   4.323  6.427  7.484  8.478 10.899 1.001  3000
## mu[11]      5.286 1.185   3.306  4.462  5.163  5.957  8.029 1.001  3000
## mu[12]     15.946 2.273  12.001 14.309 15.783 17.410 20.877 1.001  3000
## mu[13]     18.954 2.492  14.470 17.183 18.916 20.586 24.096 1.002  1800
## mu[14]     19.010 2.824  13.924 17.061 18.738 20.793 24.979 1.001  3000
## mu[15]     16.846 2.102  13.042 15.350 16.819 18.245 21.157 1.001  3000
## mu[16]     19.969 2.839  14.866 17.939 19.897 21.834 26.029 1.002  1400
## mu[17]     10.999 1.655   7.985  9.826 10.886 12.003 14.551 1.001  3000
## mu[18]     11.387 1.714   8.273 10.207 11.289 12.457 15.045 1.002  3000
## sigma_Elev  0.439 0.373   0.030  0.199  0.348  0.562  1.437 1.002  1900
## 
## For each parameter, n.eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor (at convergence, Rhat=1).
## 
## DIC info (using the rule, pD = var(deviance)/2)
## pD = 8.2 and DIC = 94.0
## DIC is an estimate of expected predictive error (lower deviance is better).</code></pre>
<pre class="r"><code># Check mixing/convergence
traceplot(fit.mixmod, varname = c(&quot;beta&quot;,&quot;sigma_Elev&quot;))</code></pre>
<p><img src="data:image/png;base64,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" 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" 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+5+vxTQkskGoB0FoF4AeiQAVeUAmj/bALSjANQLQI8EoCoAzR4KQM8IQNcC0LUANDcA3RGAegHokQBUBaDZQwHoGQHoWgC6FoDmBqA7AlAvAD0SgKoANHuomwJaYz+fGICuBaBrAWhuALodgPoB6JE6B3QA0KwAdLubAiriaw2gOVcHUBmAJgLQ7QDUD0CPBKCqHgBNnCMZAeh2AOoHoEcCUBWAZg8FoH6HKUssdQ3QCUBzrg6gMgBNtAMzAG0HaAtBAXQtAF0LQHMD0O26B9RfDICuBaBrAWhuALrdDQCdFv8FoPEAdC0AzQ1AtwNQPwA9UveAynkTAfT13wAadB6gI4CGXQPo4deuAPRIAKoC0NyRADSoCaATgK4FoGsBaG4Auh2A+gHokQBUBaC5IwFoEIDuGAtAT+xRgG4uHkDPqS9AI7cB0P0B6FoAmtuZgOphAFRVCOjiRgC6PwBdC0BzA9DtANQPQI8EoCoAzR4JQP0AdMdYAHpiAJrbdYDeR1AA9QPQIwGo6hGA7rHs3QGt8XMqHoDuGGsJ6KGxAXQtAM0MQLcD0KDrAT0wOICuBaCZAeh2ABoEoEcCUBWAZg8FoH4AumMsAD0xAM0MQLfrCdCffQagANqstwP06KkNoNv1BegIoADarB2AVhMUQLOHAlA/AN0xFoCeGIBmBqDbAehiJQC0vDsAOgHozgB0OwBdrASAlgegKgDNHgpA/QB0x1gAemIAmhmAbndbQKOrDaAAmg5AMwPQ7QB0sRIAWh6AqgA0eygA9bsDoJGjBKBHAlAVgGYPBaB+9wB0cZgA9EgAqgLQ7KEA1A9A9wwPoOe1BugIoMsAdDsAXVx0X0AnAF0rBeh88v789wSgflcAqgcDUADdMfzyTADQZgFoZgC6HYAuLgLQ8gBUBaDZQwGoH4DuGL4moPPeA9B0AJoZgG5WYS+nAtAdw9cFNLb71gLQOQBNBKCbAejyIgAt7z6AOvMHQBMB6GbPBnRzDQC0cvcAdN7nALoZgHrFtvTpgG6sAoBWDkBVAJo91A0AXW4qgC4vAtDyAFTVA6AjgFYNQGMjLi8C0PIAVAWg2UMBqB+Abgagp9YtoNlHHECvCEBjIy4vAtDyAFQFoNlDAahfI0BnIQE0ujQAXe3tAD26NQDqBaCxEZcXAWh5AKoC0OyhANQPQDd7B0D/+v232oOUBqCZAagXgMZGXF4EoOXFAP34+Pij9jhFAWhmAOoFoLERlxcBaHkJQD8+/vav2kPl92xAw6X8XANAqwagsRGXFwFoebHnQP/8dSb04+//rj1aZk8HdAyuA6B1A9DYiMuLALS8xItI35LQj3/8p/aAOQFoZgDqBaCxEZcXAWh56Vfhv5Shv9Qecn8AmhmAegFobMTlRQBa3urbmL4ufiT/doDyRvqqAWhsxOVFAFre1vtAvwE0DEDVGADqdwGgr28B6P5OBvSTe6CRAFSNAaB+lQCdAHT30noG9FM9B3rh2+oBNDMA9QLQ2IjLi8oOXtltKgM6dgpoB68gCQDNDkC93g7Q7T9SWRXQ/Bu9B6BfXbyHSQBodgDqBaCxEZcXAWh5yd9Euvxd9AJAswNQLwCNjbi8CEDLiwPaw+9ximcBupz+ANo6AI2NuLwIQMuLAdrHJ4kIAM0OQL0ANDbi8qJzAQ2342mAdhSAZgagXgAaG3F50aWAlpxOzk07BVQ9jv/id+GXAagaA0D9LgLUERRANzoJUPk60gvQaz8ZFEAzA1CvOwC6FABAM9agS0DtJ4J+XvvZygCa2XmA2rMOQHUAuuM2bwDof//5egvon7++Xor/vPT9TACaGYB6AWhsxOVFpwE6vgWg3/Ovb0pAXw/ir/tdTgDNDEC9ADQ24vIiAC0vAujn/CtICtCfu6PXvZCUA2j25A0C0OyRANSvIaDBCeB+F0AzOgPQHzJf9zkVoD93Qe/xaUz5s9cPQLNHui2gjQQF0B03eT6gf/0+v26kAf0G0CAAFQAaC0B33ARAT2wL0Ne/AOot0v0ntSoACqDeRQBa3uZD+M+OnwOtCOieP9xuRs8+4lEbAbR1ABobcXkRgJYXfRHpdZ9TAfpzf/S6zwQF0Mz2YAagABpcBKDlxd/G9IsG9PWe0OveSX8bQLfGBtBLAtDYiItLBICWF/tNpPnd8zOgX9d+rPKNAF0f/JmA+ucegF4O6DRt/igH0Mqt/Crnx+UfrAygmQGo1/mAZs7BbgAtOXo9ADr9/F/OLU76MJHXA/cO/ATQ3ADUC0BjIy4usf+bFYC+SnycnSL04r/rAaCZAagXgMZGXFxi/zcrAH116w9UBtDFIv1/4+sCoG8N6BheYv83KwB9BaAyAC0YCUC9dgLq7CgABdCWvRWgwzAePbPNAgF0rldAR+fLcwEdx8VxAtBDuYB6r7738Do8gOYFoH4AuhwbQOsGoDIALRgKQL0AdPsmyzMBQNvlA2p3PICm1iz8IrouAAqg3kUCQIu7xXOgao8D6OaahV9E1wVAAdS7SABoca0B/TR3ZAs+kwRA89oGdD4XAfQ+gL6+BNDk4h4O6FfwZECuoQCa1x5AJwAF0OAiAaDFNQT0z1+PPp0KoHldCmiHggLocmwArdsmoJ+lLyLNvw3q3HZ+iSrvo50eBOgIoOcXO6gAWgvQecLm9XBAox+eXAzo94LL7A8XfRKgy7+/fA2gGRu6OdQbAbpn494O0IK92ADQnPP+LEC/7V+DLwY0csPcj7cH0Lz2AVrl/tf7Abq9ddUAdRgE0NXFPRhQ9aeV/L7yHsMDaF4A6gegy7EBtG7NAFV/3NMv80989groYrrJwdMTIQXo6F8HQOv2ZoCq666PDaB1uy2gr//oDtDUTADQSzIH1Vk1AAXQmt32ITyARtcs+CLsbQEd7WViZTcnvwGgiwD01W1fRALQ6JoFX4QB6Bagie8A6CIAfdUO0PjbmJb3Slc6FdDdrgCoANBYALpVfUBF3htB7wRo9I30ecsC0LwA1A9Al2MDaN3aARr7dLy//StrCQCaF4D6AehybACtW0NA3Y9iqv9hIhpQDROAAmgYgC7HBtC6NQVUNPw4OwCNrlnwRdjbAyovOB9Q5zutAPXfVB8PQAuHStbRJ9LHxgbQnM4DdALQZeuAmm8BKIA26b6ATgC6NuhlAWgYgBYOlawjQJcpQPUOB9DtNQu+CANQH9Bw0twMUHk9AN3drT5Q+XgAmheA+u0BdApvEV/UeYD6LwUB6OriAHQ1AM0LQP1uAOjyE4EBNGNxALoagOYFoH4AGtYfoJE1yljcfQD9zPss+To9CtDF8gG0dQAaBqCFQyVrBmiNV6QANC8A9XsrQD1MUwFo4VDJANQsG0D3B6CRAHQrAM0o8leNAdRfDoC2rQjQ+M7pAdDlgQPQBwM63wfN+vS6RV0C+jrcFwG6NdPaAbp4rw2ALgPQrV5n6wige/sRNPPjl4IAdHGLzVXzvwgrB9TfuwAaCUC3AtC8fh7FZ/0Jj7AbAKpe+gRQAAXQzQA0s6+PQ+9+ugWgE4BuDHpZABrWF6DqVALQZD8P4o/cBQXQxS02V83/IuytALWb2hOgYxtA3TeDpusN0BFA1zt2FxRAF7fYXDX/izAABdDuAC2eOW8A6LEAdHGLzVXzvwgDUAAF0JoBqFk2gKbLAfTYgajYXQANdm02oO5NAHQ1AJ17BKDhXH4QoMcETa5e7noD6CIALRspHYCaZQNounMBTaxf7hkLoIsAtGykdLcAVP0ngDYCdHsCh3tXTlsA9TodUH01AN0bgM4BaHrV/C/CUoBuzWAABdAwAH0FoGbZAJoOQAE0DEBfAahZdh+Ars0lAM1cEIAGAWjZSOn6BvS1qwHUuQmAZi3IAXQ0l4lVQBPTAEAXAeirbUD/+t+v+7PGUUBnjQA0sWr+F2EA2hOgP18A6BMB/e7l78InAB0eC+j6SgBo5oIAdLEZi8lxLaDjEwH9PPAh8lUD0HBoAM1aEIAuNqME0PhCAfRVBNDwb3Fc8OSn6h6ATgDqDQqg8e/ZJd0P0NhSAfRVBNCfO6C/vD5H6Rf59XV+7gFUdADoTKgA0Cig+SdHr4BubgiAbvUWgP73n/NneH7Lh+5fFz6CzwF0bfLuCUBXOwJo7tkBoAC6d3E9AvrX7/Ofgvvz1/nvGf1w2vVDeAANVs3/IgxA3xpQ/4A5l50FqH8iHAR03nVdAvqH/Uc9lL8mAA2HBtCsBQFouBUCQOuWBPTnruf8N4n//PXQ34U71DsDGtscAM1cEICGWyEAtG5JQMWnvOv5569dv43pGKB2CnQI6HJ7egTU3hRAAXSjtwBU3/X8kn8P7rvv94EeBdRZ9vmAejPnhoD6PwQAFEA3egtA9Qvv3x/zq0hfx/60+6EANBwaQLMWpPcngJoBAbRu8TfSv+j8+eeHzu+PJ7+IBKC7lgugbw1oYrsA9FXqVzl/s7/RefnbmMwe0tMIQNOr5n8RBqBbgE73AdRc62aAmu2Yp88DAf3vP2c1//p99vO6O6ASUOfoNQM0508FAah4NKDxeVATULOnAPSZgArxPT/9qe+KXtbbAvo6s/oD1FkjAI0EoFu9DaCdpAHVu6gFoOrWpYC+DmILQMdDgCan5EFAzSoBaKTqgKqJ1SegyQ/vXxseQE/tFoCOAOqPCaDx75klOYBOG4BOALq2OABd7/GAulPnOKDOd08AVG4xgNoAdKO3AbSrT6QHUOcmAJq1IAANt0IAaN0igL5ehAfQZAAqVgGdADT+PTUugD4d0K8PAF0JQEUK0PnrdwY07ZcGdLgfoInNAtC56O/CX/neT7ezAJ0AdG25Yj+gI4CmvpcAdCEAgO5fXJeA/vX7hb/97lcX0MjxVpr0CGhs0gJo5oIANNwKAaB1S34ifQ/VBnRxwAF0z3IFgALoogWg2w8Bo4CuD76+uC4B1R9n10EAGtwCQPMW1CGg7vR5GqBbZ2AEUO/fzDoF9NI/4uH3fECdJdUA1AwBoKIFoOtzDEDXxhZvA6j+SPrrA9DgFtuABlMzDEAPAro2yQB0bWzxNoC+Pgm0jwfx9wF0nmIACqAAujr8ewD6EvSB7wNNATq8PtHh7oDKtQJQ//rdACp3ylAGqDxqAkAXi5tEj4De4jeR5i9rAPo6Zx4B6Aigi+vb/anXE0BzAXUW7gWgc3f9TaR8QGPvW5sv+TlpADS1XHM7D9Dp5oCaXXBnQO1YALo3fhNJGEDHfgAVAGr/A0Cj3wLQdwH0r9978fMkQGfeDgAq5PhvD6gadvKndANA9zr67oAu93y/gG7eNrq4XgHt4zX4FUBFbUDtubYZgG4C6l/UANB9RwpAw13fM6D5gnYK6B1+E+k5gNpFAajqmYC+/q8EUPlvN4DaVQDQueiLSN1/mAiARr4HoIvr3x5QfVFHgA7qPwF0Lv42pt5/E2kT0MjhuBmg4cdECgC9O6Dzrn8GoFMNQM2YDwO0nw+0OwLoYrcmAZ0AFEDNTabwKjIAlQFoUPRFpO7fB1oAaAyOYkDdY78b0MUp6f6TC2i4sgAau/4CUH3LXYC6owCoDECDngOoN3feANDw/iyALq8PoH4ACqAAqq4NoNsLAlA/AOXvwgOoujaAbi8IQP36A1RPIwBtUWVAl3KcB+jrFu8LaNbZUQvQCUDvAOgEoM0C0GDk+wKaMcsvBHR6P0DDaRhbdQBNB6AAWgDo6zMAxW0AtWgdAFSd5isbsgGomrYACqBnBaDByB0BOgDoYsD2gE7BFZM3kYsH0KBLXoX/uOxt9RuAThmAztd6NqAjgPrrDKAAGnQRoFd9QOj9AE1Mw14AnWYemgI6NQV0XL1CsM4ACqBBlwF6jaDFgC4nOoCqtbw5oOPaFYJ1vi+gajmbgDqTD0D3dNpf5fzbv+avXpj+5l5wbgAajNwG0PR6OzfMAjQ8i6sAOgDoEtBRXw9A93QKoN5fNf6e6bzoU+oBNBj5KkCnBoAmdzeAtgV0ccjCy4J9AqBrRQD99LCU/3XNZ4RGAZXcFQIau7Q9oK+VBdCgzdeKIpcDqH/FVoCGT7UCaLLoc6Dux4F+z78L/33Jb8RvACopew6g9n8BNH65AXTzOQexC9BwnsyA6rkCoGaJAJoMQBWgiQ/SXQag24CO7kVtAN0WFEABNOykv4nkP4R/PXgHUNNZgC5Pxn4AHW8A6NQLoHISPx7Q9X2jBnkHQH/I9F5Emjn97PE5UAD1vweg/jp3A+gAoGqQtwD0z1/NZ4C+3sb0ehX+6+OSv9R5L0B/DiWARgGNnlsXAzr/d1VAfWn87WauKY4AACAASURBVKkF6OhfsS9Ak09g2LEqAyq6BPTFpdMfs6j9vA/0PQAVkSdBAbQqoOMdAZ28K2YCGsESQA8W/TCRl5iqF5w/D+Ov+VPxABqM/ABAzeBrgEa/A6AAutycLgE1v88pH8l/X3P/E0AXI1cBdKoE6FQI6BB8EfZwQMU6oC6dALqxONEtoH1UCmjkWD4M0AUyALpcZ3XM3UMGoAJAqwagDwJ0aA6oWqVjgA76i/hgAOo9jhfdAjp6e+giQFNTPd4bAuoel6OALuiQl77eFv7egG7NQgC9FlCzywB0seodAfp64vOP3v6s8VMBtSeRuy5rgLpa3RlQs6V3BHQFiZ2ATmcC6q/OxYDOpwCAnhiABgOvASoigIbrcwdAh8gHlqpv5AEqn7CpCWiKSHVjAAXQGwI6KgUBNAaov0IA+lRA9XUBdDWeAwVQdeXOADXrved9oFqo6FiVAJ2OARry0Augo3C/vQTUXYMcQN0LATQjAD0E6LQTUPF8QAcBoGuA6sl7ANBR/y6nOhwAGm7Naj9DvDmgztQOAPWPMoACKIACaNDPGOcC+vqNzqv+ovHcWwKqFv8gQMcnAZoeuCmg89ejfhK0A0Bfe/BmgP7sjpMA/ev3l5vyN+Iv+i3OOQANBr4roItDKG4MaGrkxoBOALpcXrg1G9cfd57jGcUA/Z5fef/vP69+Eb4U0NhMfmdA7VUBFEABtGqJzwP9ud/588+PnRd9EqjsnQBVT2+1AVRfF0BdQGeWAFSEF54M6PQ0QL8+1B/inOn8vOQPGstaAyr/E0BrA+ruhz4BVef/cA2g9vC8GaDD/Oe99wK6dYw7BfTnofvrGdCff+anP6/5a0gyAA0G7h/QxX64E6ATgJqlJzbrXEDXD3KngKq/yvnzzywngAYB6K0AFeqkvQzQAUDNUO8EqPprcs8HdJ4MALq6zZuADs8BdALQPEDtGQCgrxSgnx/yz8N/XfhWUAANBgbQvYDKLVXLWwHUmyiTpa5bQJWYXQG6WIl4bwLof//5evFIPwX6w+nDX0QCUADVN2oDqDD3a2dApwxA1ZNM3QA69A3o+il81qvwny86v+QvIb3e0vRH7TF3986AhifWLQCdV9rbDxIeAC0GVM3bhwIqb1QR0LVz+Mz3gcq/Z/x6HH/lL3MCaDCwvFxcAejo/ko7gMaHPBNQZ2VWAPWWfWtAl1foFVD1d+FfcH5d+otIABoOLC8frwF0BND6gC6OM4C66++tR3gNDai9vBNA5z8Er16Cv+7xuzgb0J2CLgDV0+dEQJfIhPdL9eR6IKDDewMqvwOgPQPaSwAaDNwToAOALoZMA2o2GkAB9LQANBgYQLcANQcGQM8FNLH1/vAeoK+vjgA6AehWABoM3CugZn3OAVTo5wKXOc9J1wZUewegClBzzAG00+4PqL5/9ixAp5qAhmsDoAJA9fp76wGguZ0J6NgIUH3avg2gr7MhD9DFQQFQABXhrgbQ/PYAKnYBKnccgAKoWdgqoPYZPrsdLQAVuYAKpVWXgG4ImgRUzHMrcn0APdZRQO0BeAig0qOrAB0B9IaAOpsBoO8L6KRPhjxA9REoBTRyUT6gI4DWA3S8KaD600ktoGojmgI6Tu7+BdB3BlTOv9MBXVwGoPN+vAJQaU4moBYqsRNQRXV7QIdRL9pfytMBlTcE0OZ9yPkWAXQ+KaoB+lrgGP1dzrsAOqobnw2o+eXK+wEqWgAaIQRA7VAAem4NAPUOAoDq6+4DVO9iF9AhBqjdEQAaAiqnigBQD9DgzNTr760HgObWAFDvmACovm4GoK8DEgBqULwVoKIE0DET0HG0gE4AugLo8pgD6NGaAOoclO4Aff1Nif4BnWoAOpwN6LyN5wMqB+ofUGdrATQjAD0PUG+ABoC6lwJoBUBHd7k9Aap3aVVAzdUANCMAfTigYzVAJ/m+bABN7iRxHqDjXkCn2wC6uC2AHu1yQAcAVbd7c0DN/b4mgIZedQeoWSqAhr0hoPaovCGgr1PxAKDjCKB3AXS6BNAVQwH07HoB1PvGiYBOwbJ6AHR8F0BFC0CFHKEAUDVPPUDHCwCVW7AGaFJQAD27fgAN5esVUHPxGqDhhu4HdPQAne4N6HBbQKe3AHQE0KMBqAhuAKBFgOoT1i64DNDFWe3vJJEAVOj9dQzQqSNAJwCV3Q7Q+RgcAdSZPxUA1adyAKhel3kSZAOqfuUlCagrpfNNe7G907UDUHXPdi0D6NQLoKk1jgIqxyoFVJ7hZkedAqg26QigctdnAGomtbdPLOjOhiwB1edSNqByQwoANVvjAZoWcgLQHgC1DwfVEoc4oJO69kWA6nPzIkDdzbs/oNMFgI4ZgM53Cb3by2VkA7pY2k5A9SQD0L4qBNQ8pLgDoPqLWwA63RTQ5e+ClQA6yb1fAOg0j7UEVK1yOaBC3wUFULURAOq0C9ApAuhYBdBRn8sAKuUUOwF1HsNHAdV8qQv2ASoHAdC+AZ22ALUnwmmAqssAtGdAhbqPpq5VDOgkr9spoOJMQIN1KgbUOXNvBahaE9E5oGolugZUnv0AugtQNRHSgAr/UgBtBKhdYfe0zgF0DC7fBnRsA+hUE1D94wVAAbRxhYAOzwc0huAFgA7rgI769HLOjp4AHRWYZjMtoOMRQJ1lKkAnAAXQ87s9oHIl+wJUhL+KdBBQkQB09AB15aoEaOxEdd7Ycymg9uj6gI4Amg+oOnkBNLvdgLo/908BVP8DoGcBOrUHdKoJqLpWA0CnTgC1Du4DVO3/EkDlPvSvMJmtAdBUzwDU/B7MJqDTowDVC74/oKIGoKICoONrxQBUrlguoK+5CaBnAmoepQDoNqD6pOgK0Elfejmg6n+eD6iYegRUz00AzQF03AOomtYAulYA6LgHULPguwKqr90XoK/N6AtQvbMAtLsAVAQ3uAZQSUgWoM6CtwH1VqcnQDUe5YDKA1EV0J//6AzQIRdQ8wWANq0rQM10d/8B0L2A6iE7AfR1yHcAOpmtF/UAFQWAChfQ4cGAuusPoAfrDFB1WgvnHwC9FtBwvfWsqAyoeAtA9ccxnQeo3i8a0PmiyVwfQA+2C1CRB6i4OaDj/Lao0wEVFlChAJ0XVAKoehGqDqD+isvh5Lo2AnRK7as3BXSoCujYClApBoCeDOh4GFABoBcAOjYCdBzFXkDtYb0SUOEA6q2S8C+sBai7AkEXAjp/YwBQAAVQAD0OqLu/qwLqr0DQNqAjgMb76/ePn/7xn9zbnQjoWBXQ6fmAzifSkwCV27MO6HyQcgAd+wbUnkQAWlgzQH/Q1GJ+fqh+yVxGJqAWj1xA5WeuHQbULDkCqH/+BoCOI4DavV4bUHvmrgM67AB0uBugZnfqwX1AnSdrdwCqj7sD6LwX6wAqT0MAlVlAjZ/Zd0I/hjEPUKXdOqCTd2kngI7rgA7279leA6ia2cWAmjEtoHZdl4DKZd0fUIVSA0CnIkDNbNZXKQV0sJ/CdSNAp9i3j9Ue0O8fOH9Tl2TeB/342Afo+AaADo8D1N4hCgHVywLQ2wBqnykoBlROl1aAirsC+vPv3/+tLvv6+Nu/cpaxAFTuoVJAJ8lOTUDHZwIa7IkUoBrB6oCqEywEdLwY0KkSoPpw9Qno5L0VaQNQeY40AlTonTgEqs/j5AA6/+89Af1W9z9f/fefeXdBAdSuysmAetfIAVSkAdVXA1AABdCtNKDevc6vvGdBzwfUO0j6dL0doEOXgA5qN+4BVBLZDtCxOaD6kJwNqF27twR0cS9I3B3QTxfQb/twftlHpPMBdY5SPqB60WWATvrVTY1DHUAdoToEdDHnjwM6Ph9Q+eM1DqgzsbIBVSubD6idZABaJwuoY+ZVgMr9pgFVu17KlgOoIkI4//QOqKJCXe1aQPUK7gFU6NPZXXMN6OgC6q05gD4GUDst3xvQr933QJe9EaCDfsfWNYAGgjYAdLIrWA9QOwnstuwHVKQBFe0BnTIBFSGgzuZXAlRv5jWADgDq5ryI9Ie58POi50BLAVXX6RFQf6bI812Y//EBFXZyvzGg5kiIJaDzVQygZoceAHSSSzB36p4N6NgFoOYehN+9AXVfeXdfkd/TuYBO+rTU9QPo8BhA5S4vB9T+fwxQOWIRoGOvgA4PBdTMkxMB1SdrxVoC+jG/hd6o+UNp3iN4Dag8pJuADg6gYzGgdg+PGtChAaB2nLsBKjxARzNpewPUw1Adgt4AVf8BoAC6SAE6N7M5/0bnH5u3c6sIqFpMCaBjMaCKpqcDqieoMKc0gK4D+lrecwGNGOYchgWgasMyAJW74+GACsfQGdCvbD8DQA2SZYBO+r3yJwOqD2AHgE6KlQOAyq3MAnQ+PRoD6hyVKKBq7doDKvReuhmg+ollsQDUbsB+QKOC+oA6iy0AdHgLQGV//qpfjs/9MKbqgM53hQC0FqDz5z/UBlT/Ry6gox0xCagd6QRA5QF/AqDjBqCTOUf0kQLQPtoPqJzvmYCqaQmg3vqo8dz/7A5QubsOA2rBfCNAB+fghoDqM2I3oPInGYB22m5AR4mj1WMU3q9WAqharTqAmkEWgKpfAr8YUNEJoHJmut/pHtAhF1B54hQA6u6y3YDqwwiguzod0KnOq/B6CgJod4COepsBVASAzoh1D6h5EgJAt1sCKhKATm0BFYbRCKAap3aAqtdrxH5A7ZidAqpefzoXULUTxRqgzroHgM57bwh3lbeXREVAJwuoqAeo86cAhJpOZnc3B9Q5geRVDgKqlgegqToDVDinT0NAxwSg8soA6gOqV//ugA4uKfNxTgAq4oDaoxkCag53DFC7228JqLm34azeYmAA3QJUntergAr5GY2zpHFARbeAjp0BKsx+jgIqTgPUrvkCUHFDQAcBoABas24Atf/bC6DBp4lsAqpP4R4B9TZlBdApAHRKAiqnQxeAznsgAugUAXTYC2j4Lq73AFSe4+YCAN3MAmoOahpQPVO2AdXn3A5A1Ul/EqCiIaCKgmcCOtYB1BI2X+9kQMU+QIfIZ6n0CaizFL3PzZhlgNr7SAC6p1qATtuAivntbN0AKr8BoM4qtABUr1gfgNpVHuWT8pUBHdwXQ82Rt7v7OKDm6IpgrfQ+N4OqzQLQpj0YUP9ewFMBnSWJADrlAjqveTtAx/2ADgC6BuiQB6i6H7oLUAGguZ0O6HgA0HmeZQDqzuIUoJMepCag4acilgM6Aai7l/Smy93VA6DCnhZm9wNo1QDU/GcBoJM+8E0B/bkIQDMAFZcBOi4AFYcAHdwPXN0NqNnGdoDKPSn3BoB228MAdeGqBag5IReAKlzeDlB9MC4HVABoZUD1XAPQnbUCdNJzSzwAUH1XcwXQsQBQ9yqFgL5uBKAAakZLACpOBPQjfuzKA9AagI5jCKg57cT5gMr/LwNUyeHPw6qAjhmAjs4aAGgIqCUmBqiZ5BFA3YNtdzeAFgSgdQAd6wAqrgd0bA3oWAnQUQMqs0cFQPU+6BtQc194HVCzVnquDQC6sxMANc6No7gLoMMwXgWoN1I2oOrYVAF0ctQc7S5TV38EoOI+gKpRBjW9jgDqHrU9gDpbEQdUXQKg6hGuOhIuoGrn5gM65gEq7OnzHoDq/ZcAVFwJ6OQeq0aAjqcDKjdaT906gFpC7g2oOgPlBROAbhcB1MzuUwEVOwHVLyglAHWObjmgIgKommiiPqB6ewoBVQeiDFDn1IgB6p2e3kZcDqg8dAGg+k6UOrYaRw9Q9WxEHNBxtD/O9wLqvuNN6Sl6BVSkAFXHchhGuxEFgAoA3QGonCBLQMVBQMd9gM4HC0CfCajc1raATuKmgKrF1wLUHlAALe5aQI0LuYBqrPSy+gZUD6b/6wxARX+AzvehYoCa/+4BULkHCwC182IPoI6gAaDzZxKEgE6nAToCaE5rgNqZ8jqs+YBKMLsE1Lw44gA6OaLtBtQs+jCg85AhoAJA3ToH1LwAY/cxgB7vdoBO9pRvAKj54awfwd8A0MEhoh2gQwio3P5NQKduARV3BlTP7jxApwWgzu4uAVTsB1QPJo+UXB0AbVscUO9cfxUAOqmfiylAxSqgZv83B9Q5vVcBHWsAqk+LdoAKvZOFuAOg+vBpH08BdLwa0GkJqD545lDuB1T+/yagZoYB6MltAaqOcgmgUw+AmpnSEFB9Fj4OUPeVjlJApQbqCnUAHeQ7rDYBFdmAihigQu3vGoBOohWg5gQzRwpAz2gdUHOU44Dan8/VAB0PAurIBaCZgM4u1QJUz4v6gM4PgxWgYh+ghol3B3TS+9k/oABaXA+AysMFoGcD6lEl74CuADp1AugwL0YBapTZCehUDKh5MFYf0LEY0AlAry4CqFAfIfGqGFD57akbQOXYDwLUnARxQMWDAZ2uAdSs0wJQvd7FgDpbEAdUmKXmADqcDKicqQCaANTAAKD1ANUn6RsDOr0boOMKoPPCJnm67QHUTgM9mF3hWUw1EwG0UbmAypniAKqdywFUL90BVHQF6JQNqMZkE9B58LcFdNoHqJ2AfgA6ZAAq9JrJawz2o3qOAboUFEDzAR3mSSxCQOVpKIoAVeftJqDOR0fcEVD9/r4loEM3gM67KwDUfcARA1TvlCsBFdcBKht7AlTtfzWID6g+9gB6rEJAx+cBOp4I6PgAQOWKNQPUHdquw1mATuuAmrm5BehoHjPtA1TunU1AhwOATnIs9YO8AaCxA3eozgGdj082oKM6zdYBFRcCOlQB1FhXAqjee3sAHVsAauf7DQGNnIguoPOZ+g6AzpMUQDvtGKCvbxlCQkDHZwA6HgHU4WU/oOZEkNt/FNDFnN8DqCgG1NvaDUCnGKBC3b/cBNR4XR9QoSd5EtBJNAXU2bSzADX36OelW0AHAF2pGaBTAtDhOKD6tO0J0AlAATQKqMZuC9BBH+ZrARUKTQmoWicATdYOUMVdJUDHkwCVvF0GqNDXPwiovqtuz2Vn0u8AVNwCUOdx7hJQS4azfn0DOuQAOooQUDNFDwKqLQfQHR0D1DyXNUUAlQ9JigC1T5LtA9RMkgqADpcAOvqAGgXeClC1864HVG7+NJo7dnmACmdG5gEqd5k9ydYAHX1Ax8sBnQC0H0AnMwOuBnSwn3ByEaCjPgWbAyp2Azrv+y4ANTfV1zgPUPkNA6iQgLr72AF0PiyrgJodXQ/QabJr0hRQ+fMEQG8N6CTpE3qV04CKg4CqaXQMUD01TwF0dCb9FqB68yoCqr7fAFBhbqqv4QE6WhgfAqicf3sAnc4EdALQDUBnL84DVOOojooHqFqr3YAK/YLWYUCNUXrUEkD16aKxkSfKYP84bjeAuoI+G1DnerUAVdOjBNB5cccBncoBHQF0X/0DKr9XAqgw10wBqk4E887HBKD6lS21vBSgws7YLUD1PNT7Zeb7AYDaXdIpoPOIbQAVC0C1RiGgowfotBNQ/fCrLqD6VgBaVgioPLQ7AZ0CQIUCVN5SATp2AuiUBnSMASq3tTtAp2JA/ZNAOCugFqcAnWoDqifUFqDTyYCqY9ocUL0/DwFqn78qBHRQT24DaNXKAJ0PbRJQdctMQIUHqFmJLgAVcp0BtAag4yFA1d5NASq6B1SoKzUB1NnanYCqe7Zybo8W0HlCAehmHqDmf7sHVPlUFdCxNqDTbkDVPfcagIqmgLpXn3eNvIIG0u4SC+0SUPsutSaAem/cOQCoYqkuoFMhoCIXULkH9wA6AOiBOgNUpAFV01kDOhwCVK9DTUDHKwG1dwbTgOrdvgWo3roooO69f71HDwNqz/gdgI5rgDrvZLsE0Gk6EVC7mA4AnQD0MKCj/QijFUCtcQ0B1U/WJgHVJ9lVgE7uJ4rdCtD5sv2AyjNxC9CxV0CdgyTMEIcBde8zA+hGAOoBKt4YUDlxFaCTc25ObwKoWrsA0PEsQOcBcgHVb/fJBHSwjzVigI7THQG1E98LQEsAHeW+SgIqb1cOqLPkzgGVa+QCKlKATk0AFXsANafBBqDmoCwBndRccQGdxE5ApxDQKRtQdSQ0oPpXXCsAOtgPdmgIqFCAji0AlVfQW5ECdHIAVSsaAjqVAToBqD7lawCqb9cEUHE3QNWOUoCKTUDtTtwBqBBxQMcMQOWZ3QrQUU8dH1D5gFouNwvQaWavFqDyvG8PqNnNFQBV+8FOA5ECVF+2F9BJbTOA7moGVJ7aJwA6DmY+vdoBqPPJNPOjN3WP4SGAOndNDwNqX/+VgM67IwfQeZXdsaKAilxAxQJQfQ0F6HgxoCIf0CkCqMgBdKoGqJx0HqDOa3IeoCOA1k8BOsYBNQc5Aqg6xQ4AOmYDOihABwWoT0JrQPXpUQLotAKo0M4cA3S8B6AGr8kAOpUCao5iDqD6OYMcQPU+1UM4gKof5WrX6/cVHQR0SgKqdvOYBHSaooCKOKADgB6rPqDe7j0NUGeOqTUPATWPHQNApzxApxVAh2ET0GENUPfU13vwfEDtg444oPobcUDVQYkDqr6zB1B3aNPJgAoHUHus5Kt2o11qPqBTU0CFepLhAKAiG1ABoDcHdFIzUd0bzQJ0HjQAdCwBdEgAqjbHA1Q0BlT/OMkD1JzYK4AOEUCn9wPU/NiwgI4OoHr6LAEVtQGV19wPqFC/EpYAVO28KKDOnRQbgB4FVE/TDEDt7C4BVJi5oNYkAuiUD+gwaDqrAGqU2wZ0MM+klQOqT13Ng/ZIlADqyiCvEQFUjGMBoMbLrgC1l5QCOu+NbECVk9UB1fMvCuiwBui0CqizqsJcDKAXADqcDujUDlA5CV1Ap8kqJ/RDp9qATvcFdJ5u6vlODajad5mACrvWeqPFEtBh0Hf0CwEd7WA7AR2dJ1HOBXRaAXQYALRaZwCqr3QqoK/7NgGgansm776LA6g6J9Wrw4pRYR+1dAjoFAdUv4zQDNBJnWM5gFqjmgJqtm8FUNEboO4hrwjofIUIoAOA1uo2gIoiQJ0HursBVXeWLKDCnAdpQMXlgCrLzgNU3X4HoOLegA6nAjoFgKpdEQFULkLtvgigogTQCUCzigLqnNfqizaADubYWkD1zFgH1ExDs3oS0LEY0KkXQIdBnAiovrApoPN3fUD1CZ4A1F0Pp14B1fvpGKCT2mO5gI5LQEUOoKOe3AcBnc/QIXbcjvUcQMcKgJorlwE62L8Ing+o/I8FoGNngA4tAB3taOWAGmAqAjrvFg9QoVRdzla1FbcAVJwK6FQGqF7WJqBqbVcAlbMQQNsCqi4U2ps+AVXrXRNQ/bC0CqD22GwBqs+AOwA6DEcANdcoAnRcB3TIBnQcHUCtokWATuZptXVAzbQ7DOik1scFdNILAVA3B1CxCqgA0C1Ax1VAB3UX6zCg7rG5O6DCA3S4BlDRJaAiE1BnghQDOs9wAM3pSkANDPq0qQCoqA2oQqYCoEIusiNAnSdGNwFV7EQBVSedA6g7g+QcmCKAqh2dB6jajaNUQF9eHVB1dMsAlc/XlAIqnJdySwEVCUDl3UwXUDWdKwA6AmgeoPrU1F+pebwDUPPQyT0SeYAK/TNa3vQsQOU52CWgQv9YOwaoGSED0FH9duAOQJU8AaCKKgOoaALomA+o1ko9otZzZheg0zDEAZ0mEQV0UnssCuikZpELqLx5FUDnSRwDVB4N94jHANU7HEBLAHWn6U0BndoDOniAqlPa7Oj5BmrVbgSoNGPUn3Q5mEee9mZRQIWdCycCOj4TULEEdF7oKqBmpWKAzodjGh1AhV7WqD+bzKYfwQPoZE54mQOo/jmUA6icdlFAFSPuqsjjuAtQNUwmoFNfgLoT8Sigdl0CQEe1srUBNSQ0A1REzkQHULkSUUCdO0RlgJrNXwN0EnbPnwyoPAlOAFSv/BJQV9ARQNsAOiwA1ZePzglv2g2oGdwHVD14VCu6H9B5RhwEVCwBVTtUCPWxSC6gat0NoPJWHQBqXwXZBNTctQ0BdXhUl54HqDsDcwDVO8KZkpcDqu90xAAdPUDHmoAKAM2pIaCDPAPDibkfUDOgBXRYBVQIjcK4AaheyQSgyhIL6PhEQMfTAJ0CQKfReTRyJqD6wEQAFd49Z70Q/e0AUGEAlcfQACoMoPK+t7sUcTmgk3picwNQNTkBdEcFgNqzbgtQkQI0+P0Ge/FxQEV/gKrzdoYkANTu5yaATpuAzpSNDqD2oNcDVASA6iXJvTadAqh+za4MUHEGoPqQRgFVj4OSgJrJ3wpQMem/mQSgTlcAOg12xjq1BXSqAqjQgE7mWnpLGwEq+gJUXA+oPDIdATqeC6jIBNRsCYA2qA2gRqE4oKM+qb1V8QFVEyEC6GSG2QDUenMcUPlkWD1ApySg9mwTahX3AjqZMzoXUP0upL2ATo0AtXft6wM6Aeg+QIWcPyuAisF7GR5Al4CqnVAOqJ0xwbwUwrh3f0DtL9DtB1QtqQhQ4czPFKD6HoTdth2Aql8kOgCoFtR7iQhAAfR4twBUP1N9K0DNEhKAGm/WANVnlfsgsgmgk5yiGlCz18QJgLqPu8zCDKCiNaBimq4FdNCf7XEyoJO7FFEBULk0AO2mbEBFY0CFnhlbgOoDq+akWAAqEoCKKoBOS0CHTUDHSwH1eBEKUMtaK0DVph0FVN5YHZkxD9BhMFtaH1BnXVcAnXYAqm4rp/Ko3v/nAqrXbBNQed26gAr56UwA6rUAVJ168rt7ATUcqUtdQN3zSS21DFBDVAjotAKoPpcOASqigE4aUDvWAlDljpzYw6BP6caATvsBnc9Ge+8mBFR4w+0G1BxZB1CRBFQtehNQPdF2Ajo/mDFE6HffybP9MKDmXl4xoGb7DgMq0oB6W6IAVTtjG9AxG1ABoJN5RCdEDUBFDFB7V8mtEaAKwFqAqmcxdgBq9qAPqDgX0LE2oIMPqJGkIqDyqvrhv90PS0DN99YAHfTd/ixA9XHcDeg4mt3XCaDuhuwFVABoVs0A1edXFUDtA7GeAB0H89d+9C6Ss1AcAFRPKGeVnAAAIABJREFUarNb9J1IteoFgOqF1AJUHYhKgE7rgJodYQHVP1PsqboFqDgIqNgH6HgDQM1EHaOAqg3cBtQZAECbAKou6xJQe44VAqr/BEwGoGIPoMHqme9FADVnqgVUv2VsUq8jyGM6GiCzALVnvLMhK4CaFW0H6OgDalduFVA9gYoBVTMkCai5RAM6LQEd9N2JeoDasygfUP0EJ4AerQagYxmggZ8uoGoyNQLUeWw46qf1dgBqXhe6AFC7jzoHVGQDKh4GqFAfy7AOqDDmKUDN3j4BUJEA1OzeBKCjAnRcAKrXBEDHHEAlbk0A1W/HmKxocUCnOKDOs6biCKDmh0R9QB1BNwB195EHqDxZ9I2uB1QP2BegohxQs1OOAWp2ZNeACgDNTwIq2gIaUFkI6KA+FccCOr8CPKl7PqcDas6qvYDOT56eBKg984oB9QVdAXTSgCokVgDV2MYBNS+ra0DNLBJVAJ3kyW8A1ZttZklHgKrvVwVU1ABUnV0AKmsFqLnvMJlHmaYkoOqfHECnaVLXqQ2oGMcAUOECKocqA1QtqgdAhzMBNcYYwexmj/q1fX1ol4CKEkCF/jIO6CB/3+IyQOfDJFYB1Zt5GFB1TENABYAeqBageg+KckDNUEcBVQ8yBIC6gAo96y8EVFwO6FgAqN47ZtVDQOX4LqDCAqr3ipkPLqDjYUDV0TVTZ7Jb526IB+ioAdXfqQnojyiVA9BqgI5NARXrgIpCQKdMQIWZ2f4xUCPuBNSceiGgLkkhoAoAtfgGgJqDfhDQaQ+gzmneDFCzQqWAqu8cAtQsPg6onpEA2qAAUDkt3gpQeYqrM135FwHU7I69gJo9eCKg6v7INqCTHj8CqHm0mQmo2AOobheg3o5wAFW/cZADqHzwkgbUjODuawfQ0c56/erOFqDmQKppY490BFBhdnZHgJob7gJU7g4AdQC1J2sC0GkYYoDKqXcSoPpUvwmgUxxQfwvqACqSgE7qJJcS2mcZ1gCVhzoPUOe0rgGo/t2jqwF1rrwK6DiY1XR/Dqk1ANCd9Q2oeedndUCFus4VgBos5MoUA2pOrZaASjpqA6pvGwdUFAA67AfUW3e1uVmAzluRBlTEAdUD6/0aB9QO3x+g88U7AJ0CQJ37+hUAFQC6t2aA6qKACn1SR3IAnVeka0Cd09qYEgA6qXm3BNSe6MNeQIUeWtQEdFoB1Dz29AF1VtK5v5kE1PlHeDcqAlSIMkBFIaDSjDJA9VHYDeiofpNJXAyo+b7ZcjNJANT28eECqs616oD6Y24COuUBKjx+1I9ODah+nrYEUOedK/sAHdcAtQMsARW3A9Qe5maAOoKJCwD1f0ZsATotAR1vC+i89hFA9clkd4YQANoc0HDMLUDFmYBOuYCKZwE6DuqvcFQDVF6gftjYrdwHqJkDIaDSpo4BHd8V0PHNARU7ARUJQOVJeBmg8wKLATUTz/7jAGpW0WyvGs0DdKoPqL8D3UNRBOh0EFC5w3YAKuwFk2VjP6DOHOgAUG8Tl4BODwdUPYO7D1DzQxpAswCd59iFgKoFVgJ00i8WW0C9lS8DVNQBVH9vFVBhj6W+7Rags6CrgKrdsgmoLg6o5+cGoPKHo7cjbwCoMICKQkBFDFD7c8mu1hag/gFxduQaoGaeAuj+YoDKt1c6R30L0KE3QNW5vgKoORW3AHXXPgRU2LfbAKjtFoCai4sBNXtzC1B9J17v2w1AxWFAwzxA9WkEoFVSgArz7mA5M4oANedELqDeVcoBNQvIAVRP6TxA7VCnA+p+owBQfZ8oB1D5HxbgcwD1b1YGqB3OB9Rc7gKqUbQNdvRVQNX90UaAmm2wa3UKoHqi6xMNQCMlABXeUV8H1Lyr4QCgznU2AbXjeIA6C8gFVM+7CKDehNwCdAwANVuxANTjMANQd0vLAVUbawEVK4AK/R/qPwsB9f10qYsBKobhMKDue21SgOp9shdQO28fD6iYAHRHVQD13xkfO/+9jgHqjONMKeEuIAaoWAXUKOUAqu+Lh9uSA6haTgpQc8stQCf1dgMfNX0KisOACuOo3Uzvx9JgPlioDqB2uScBOpYBam6vAQ0+kO8EQO022G0/C1BnJwFotGxAx0xAI8UAdR+9VQVU9AWo+rnuAjocAtTZA1FAp3VAxW5A9U4uAzQ1JcoBNSsqdgEqSgB1NukhgAoLqH6OUwzqeYwFoN5OmtcYQMMyATU/yYW6ykxVE0Dl1fSvUA9DBqDCBVS9V3UnoO7pvgtQ8/bFPYAK/cDIB1Q9Pm4EqBj1WxYXgNqVWwA6+b+8ngeo3pdHADVvLXMX6i9mP6DCTOHDgOrrZAHqPSawgKrrm1VuBKh7BAC0btmACh9QubcPAyo8C7Qn87zcAag/3iCnx3FARQCoVc8DdLgRoKO5YgioYtQZMQ6os4VmLwk/vcbWhvSMSAEqAFQdzxig8il97yw6H1C7IwA0A9CpCqD6pDYX7AbUHadHQAf9jJIwJ2Y3gIpDgDo/GZZr6VYEqOgaULX31Pmhr+yuXR1Axz2Amqe2DgAqPED1z9ctQNUkMYCqJzMA1AVUlADqHsstP48AOl/Be0iqvul0OqCazGHUfwQyANT3sABQvYOOAqq4TAHqHtYKgE7xs9krC9DAzwWgzn000ROgojdAhXABnRcyZQJq1whAswAVdQD1JuvkHi4f0DEF6CjOANRdr12A2pWwe2IFUPmrdQAqIoAaE+bSgE7TcUD1GLsANVdeBVTkAjqaNVDfOQ9QiWMKULluo/qbdADq5QGqd4g+j7oDVF4hB1Chp34JoGZlvS8XLyIZQJ053heg9rcORzO4++ehNwG1drYC1B4emb7zvwfQ6SCgdswdgDpXbgWosN+4DlBvcm0Dqn9IA+gaoPrxTgVAxSqgaioEgKorqKdfdgCqv3s+oGox9uG8XcghQJ2FRQGdd84KoOZ6QrQHdNtPH1D3GxFApyigUx+AjubR/V5A9Y+wSwCVZ1YAqL5OClCRBFSMBgcA3QZ0dM+0xoCKbUCD8VoDKpaA6ksbAWr2Tymg7t1x87Rub4D63wgAlQ9pU4AKhx29unaamvU9Dqi3ghuAjqNIAqreQLwOqDAbZnaJ+Up9kqRzoZq7uwDVJ5APqPOU6D5A5eD6HXAAejag3gU3BtRe6gOq13kd0EG+77U9oJqVTUD1eEcAnUc+A1C9ifpKZnWXgAp18jsrXgtQfRIZEDcBHRKAum9omfwnQWoCKo/v4CwwANSfXPoB+iRSgAr1QxpAMwBVDzLsQxZ73bUWx1jjYMaygHpvpFdXsE+49Aao8EYd9J/4cM+7UkCXqpUAqu75yG9uAeqMdR2g6hv6lI8PtAdQtdQ1QMVhQN3VjwKqptsxQGffBvPL+XZnFAMq7NMNYhtQ5463MIDqpQNoJqCa0RaA2pPmMkCFN/eDDawF6OQBKgB0N6Bq1wSA6k2NAWqXuUT2IKDO9mpA9YoWAurtwU1ApyOAOn95Zx3QyT4SANBLAF0c5GxA9TMyuwAV+ldw6gMqx/J2kf3OLkBFU0CFeoLQHpkzAFWrXBlQf4WCXXMSoItFeYD69xbda/sPY+4I6Aig8WZAxW5ARQtA7VL07dcBlSvpngbe4uKAmjP1foCa/eMbEgV02gDUwmQAnW+7Dqg5Bs0AXSzrGKDqZ0VdQCOL2gmov84K0PEwoN4oxwD1FhUCKtQzHQtA1Wn/5oDOnQpocJT3Aeov1Zm94XCD/NBlH1B3bsUA1VucD6h/qRk2A1A9lysAOiUBnYQG1OwGF1Bv5QE0uqKRRR0F1DxwqAVofJ97gIoSQOX/qr1tHwcB6Aqg4t6AuheI4B3oFwNqFnUUUG8bVwE1m1YN0JXjPOk3DjQHVO+aBKCL4foD1P5xgiWgYg3QaRh2A6qnorNuFQF1FgOg4jJAXZ+2ARWjeYo+Cqh/wdQToO6i5B+ZPwdQeanz3WaAqnU+EVD/o93Sa3cmoPHlTPrP4MxWBoCGn8uTB+h0BNDJA1RsAKqXNj8LC6DibEB9QZ8NaHBPKAaoOAao2l/rgIrjgAa7ph9A1V1Qbz+nAHW355GAptY+A9DFyACaTG+uMwPjgAoA3QJUrZb9lgZ0ueVmoWrOK0DtTd0bmN1zAFAzaA6gDjT+FZdr6VcBULOi64DqLyKAro+q/j0GqIdSIaDqVtmAiiig6bWvC6id7gAqG81bImKA6qsIH1Ch9mgA6Paw+wE1524K0MVwC0CF+dR4O94xQN175v5Yes/oNybPp8pyyy2gejq6W5EPqAZYcukBKhaABqu6BFSPdR6gkUUdBHRrVPXvVYC+hu4NUH81F4DqO8sA6pYDqGxw31EcA3RX7qLPADQkcRVQb5FLQP2nNo4B6k1HZ6nuDTxAhbPiC0AVl4WA+i9IOGO5jp4N6HQXQP05nVzQAtAhAmj8DDwAqHf+xAH1j9UmoOZHLoDKdgMqgjMNQIPVUv9aQJcb7twmto+zAZUXpgAVLqDuaWjHE/qXTczNnHt/w5ALqKgIqP4MtSig5ovmgC5W0Qd0muKHclEmoP6I5os4oCsP/haACvNXi2ND5QDqPCADUPnVFqA2y8E4HgVU/zuo87RjQNNjZQMaKx9QfUELQO3aXAuoiBxrM1w5oP6Y27ex120PaDii+eIooCIPUH3kF4C+lgWgMnMwxvjPv2jNAXVO3m4AXRsrAqh/iwJAzb+7AXVu7AAqNzwXUO8ls6sAVf+RHrEU0L23iAHqrbY+PFvph84zoPOON4CO3QOqfk44x2L0Px/w52sAXXscE2Z/8NYHdD5gJwMqCgD1LzE7MQHonvsqUZoyAHVvVAFQ4V1vfS3dcfsFNDV1tm9iC1a7HFC99AJA3WOw+hRoBFDhARqu+xLQYRDbgAoBoB0BOuwAdDlpygHVN08+07bYjCUi5wPqXpABqHM1+a6auoCKza3Ui0yc+XmAZs6/+oCafbZRCOhkV0W+A60c0NXtAdA29QvosAXomAQ0nMrBncp7AupPXBEHVGwAOk1ZgC7J9B6u5fiTKA2oetoTQE1xQP0H0Cur3wjQ8DEYgHYD6OROj/2ARh47+jeuCmh4SVNAvSHPATQgs1tAvecD9wyb6efi6vUBla//TckzadSbaD/a4AigYh3Q4NYGUPNchVplANWVAGp3Z31AxSqg4hCgUyVAl+kpvgbo1oJaAeqsYARQ54I0oJtrmdlzAN31lO9xQEfh7o35E71TaxisfQzQ3XNbTQQADYsCmnF7y0HWBHaPWHgQDaDeUW8LqH/dGKDTznsYGtDko8QOARW9AzoGf6IzWi6guat/BqBiG1B7oOTPvYOA+ndetm4OoGERQLNqB6j80ly/KqDu3B+CV93jbzza+wjNAmq+WKxcNUD1tb0reCMWAipSgG6vZWZZgK4PmDP92gC6ZynmAY/ET227ewxXARULQL2ZvGvt3YfwmYDK0wBAbb0CuigB6Bo1qRuHgG7csdKARjdj0XmA2mt7Kxq5TQxQb8XWAM1cy8zkOVwF0MxhsxcWeWBSsELmKXANqI/5NKWfDAvPsCxA/eXo2x8GVAS/qfzegBbc/qaA2hsOTQGN7JLagK6eNncANLUJM6DyUO14CjR72KOARi/ZzgXU/vRwl3QioIvXH5IBaKKjmwugQZuA7ngyYBXQcJOfDOjUDtCCZaXv+2fUBtCV3Rhdjrc67r+pADTRQwFd3DiyPPXPtAA0trS9flYCNHZpJUBjzy2sALq2lm0BFfJjoC2g9fy8EFBzxsg/iR0FNHEiRQB9XSBvn/Gkbhmg+nr2jFfHDkCP1CWge7KAThsnUx6gBs41QHNWdHG7ZoBmruOZgFZ+CrRs3ZsAKhbPJ+QAqlcjE1B/dZx/dqy9ugs9mZe/APRIzrt7DgNqHhrsB7T8nNIPQMR7AhqeiuIiQBf3v5z0nzZ7FqD6EYh9C2dwd7gQ0MLDcRDQ5Z/uA9C87KkGoGq97P93AKhcpQ1AF9fZB+jmVXZ0FaA1bvSGgAoL6OvRG4BW3NzMt0GFj9zFRYCKbUB3ftCOXq+OANWr1ALQOq0cxADQin6WFa5A4QodATQYH0Av796AHjinPEDXZ08ZoMlXU+sCumcX9Azoyus5DqCLd61fUSVAhQbUbPgC0PjtloCamw+p02bHyrj/7Ln+pH8GKECDKwBoaScDWvhSqr2x/t/tV8X3/hbS3LmA7toF/mrsA7RoFUtaA1S/42zqAtCC+/7RLKBmQUWAOityIaDhDQG0tCsAzRkwvLH+n23RygBNPRYrB1T+O58tQn+dvwvsCvqXlvweRYU2AZVvl1z7tOCzqgbo63+dHV4J0CNrUwho5A/R3w/Qzw/dL/k37hLQ2MGsDajz6sW2aDnk7QB0/8Ji6ae8ROEeSAF6bK3KWwF0tIB2cAe0FqAyD9DEN5K3CNfrwJqUAzpPw8Xtbgbo14dfrqFVAc26ehLQ+MGMApo1YOTWewHNyfGpzb26QVd44sQBva70Vszvn1cfDNiBnyVPP6dLHoLeARXmjLk/oH/++hH2939nLaH+5u4tG9DaK2Be/637hN8pgIrVl6/X6w/Q5Lfmp5GdX1a8uuDp5zaD5Mt6bE3Uy+p5N9FPx98b0P/+0xfzr99//vsf/8lZxJWA6pyL3H9ad+g1qHTOJza2Ycq+e6Vs9cfEUwsdpl+Ha3SosjsF0GSNDtshQJe3uxOg3wsuX6T+kbOI7gDNP5qltTorW/v0RoBqM3oB1EPzQYDmnnH6+rEnv+4E6OfyAfvPndCsp0GvAzSS/A2x096D2MtZScncT17roScCmvcmk/CWi8tuBOjP3c3fFhd+5T2G7wzQM/0EUMrtYkDbLLf8jIvd8EaA/tzbXD5c/157GWnxktNHd4CeOBx+UmaD29UrU6u6Zx2AXte5fhJl90RA63YjQB/3EJ6Ibt6NAH3ci0hEdPPuBGj8bUzLe6UrASgR1etOgEbfSJ/3q0gASkT1uhOgUky/v/0rawkASkT1uhWg7kcxXf5hIkT09t0MUNHPx9kR0dt3P0APBaBEVC8AJSIqDECJiAoDUCKiwgCUiKgwACUiKgxAiYgKA1AiosIAlIioMAAlIioMQImICgNQIqLCAJSIqDAAJSIq7O0AJSKqV22jAJSI3qbaRnUN6Jmd/HQBw912uEdvHMNlBqCq2x9JhnvkaAzX93AAqrr9kWS4R47GcH0PB6Cq2x9JhnvkaAzX93AAqrr9kWS4R47GcH0PB6Cq2x9JhnvkaAzX93AAqrr9kWS4R47GcH0PB6Cq2x9JhnvkaAzX93AAqrr9kWS4R47GcH0PB6Cq2x9JhnvkaAzX93AAqrr9kWS4R47GcH0PB6Cq2x9JhnvkaAzX93AAqrr9kWS4R47GcH0PB6Cq2x9JhnvkaAzX93AAqrr9kWS4R47GcH0PB6Cq2x9JhnvkaAzX93AAqrr9kWS4R47GcH0PB6Cq2x9JhnvkaAzX93AAqrr9kWS4R47GcH0PB6BERIUBKBFRYQBKRFQYgBIRFQagRESFASgRUWEASkRUGIASERUGoEREhQEoEVFhAEpEVBiAEhEVBqBERIUBKBFRYQBKRFQYgBIRFQagRESFASgRUWEASkRUGIASERUGoEREhQEoEVFhAEpEVBiAEhEVBqBERIUBKBFRYQBKRFQYgP7568cf5j/++v3jp9+ajfTTL84lrYb76/e//zsc92//ajauP5z4fi3c2ak1h9uzLfWGW46mL24yZZbDNZ0ycuG/LS5pNFOWwzWdKXrMtSNVYbi3B/S1D80+/vyQLU6ZCv33n2rh7Yf7GcqKJifJK3NZ5XG94dR50ma4XdtSbbjIaObyBsdwOVzTKbNceNOZEtmWljNFtnGkagz39oB+Oof0Sx/j4IypNpA/hZoN9+ks0Z4V5sLa47rD2bPi4+Mf/6k83K5tqTZcZDRZmykTGa7llLGg6YU3nSnL4ZrOFNX6kaoy3LsD+h0c0dehfF32y/rNygZ6LfQ1TeXxajXcPFfNlHjNkvkxyqcepvK4/nCvzZtPBzNuxeH2bEu94ZajyRpNmeVwTaeMHs4csLYzZTlc05kiWz9SdYZ7c0DlT121jz+dXVv9Qfynfq7FPKxoNJz8wa5Fcx7EfKlL647rD/caRN2d+FZf1Rtu17ZUGy4ymrm8wZSJb1yzKfPzY08t4Gfh81dNZ8pyuKYzReih1o5UneHeG9DXs3f/P+chjHPnvvYrO5/mIH2Zn8UNhpsnzf9sn5T8tj9dX3cV/6g8bjicPVH0lxWH27Mt9YZbjqa/bjJlIsO1nDI/aOjhvuRoTWfKcrimM0Uvdu1IVRruvQF9HcwvvY+dg/zzpfrpWC3vbPij2XCvx0N/OK/quHPjcx646rjhcM7CxeKSo8Pt2ZZ6wy1H0xc3mTKR4U6aMmq0xjMlHK7pTJnbOFKVhntrQH923G/C7GNnBvmvK1fJLF3/6G0z3NdrViSWJ0/IquOGw30vfpY32qupbWkznKXslCnzGcyQplPGuScWrEDT4ZrPlK0jVWm4dwb0Z7/9/OBx97HzOK32k6CvB7u/OP+2HC4+IX5Gfv2crT+uf4f3D/WK6x/OJVWHe5XclibDqdHESVNGD3fKlPlyXqgOVqDpcK1nyuaRqjTcOwP66e9I53Gac5ejWvZdIr+0Hi4OqPqRW39cZ7jXAvW7Q+RlbTYzuS1NhrN3Vk6ZMma45lNGvk8q9LPZTHGHaz1TNo9UpeHeGFD1IOLLPaT6ew0ANW/rMy8LNhsuCqh+oqf+uD6g/1//3XVNNjO9LS2Gs0+RnTJl3GfkGk+ZOKDNZkoAaMuZsn2kKg33voDqB0qJfbz8uXww+65o/S6KZsPFAP05K/QP+trj2uHkQ7J5t+p317XYzJVtaTCcGe2cKWOHaz5l9Lvb/VdQms0Ud7jGM2XHkao03PsC+qmmyUn3QD/V24VfM2de9rn3QL+dX29pdw90Pi3Uo1317roGm7m2LfWHs6OdMmW84c6YMuYZ1sUKtByu8UzZcaS4B3qs5dPLTQF13qyr3kF8KqDeadn0Ibz38HPxg766aM0B/f7wXyHzvmi6cWdNGfPW9nAFmg7XdKbsOVIAeij7LrBzXoX33zTR7PViszwfUHe2Nn0V/tN5c4p8Gqr6cOvbUns4Z7QzpkywcedMmeAdoA1nijNcy5my60jxKvyh9AuAut+8d6bVfx+o+zNOHrqGwwXLez1csg/S6o+beN/+d4u9urUtdYfzRms/ZfyNO23K2MW2ninOYlvOlF1HqtJwAGr2cdPfRFqeDQ2HW36+nPOe5frj+m+kNyfgd4O9urktVYfzR2s+ZYKNO23KGEqazxRnuJYzZdeR4jeRDhXZx01/F95/PFb9d9L9PEDDj0qoP67/q5zuhzvW3sztbak5XDBa6ykTblzTKRP7RdWGMyX6e6LNZsquI8XvwlfKPhfS8tOYvu3Htn41+VQkNxdQ/yUC0WJcdzizcHOGVBxuz7ZU/XykxAKaTJnFcE2njOOX/rLlTIkM13Sm6NaOFJ/GVCe7j1t+Hqh524Z9/0a74RzR3A8SUlUf1wVUL3xeeuXN3LUt1YaLjKZrMWWWwzWdMq9lmnf6mFGazZTlcE1nimntSNUZDkCdV+PsXf/aHyWiPzdT1uoDuHX+k5Ju+teQq47rPWPgPHyqvZn7tqXWcLHRhBmi+pSJDNd0yrgLNx/f3G6mLIZrOlNMq0eqynAA6uxj86sftR/Av7K/2Bz+ol714cJfDQoZaPo3kb5bbebebakzXHw0WYMpEx2u6ZQJftG+9UwJf69fNJwptvUjxd9EqpG7j8/4q5zuT7tGw1nR3L9z4zBQd9zwXSCfbTZz97ZUGS4x2lyDKZMYrumUkQtXerWfKd5wskYzxbZxpPirnERE1wWgRESFASgRUWEASkRUGIASERUGoEREhQEoEVFhAEpEVBiAEhEVBqBERIUBKBFRYQBKRFQYgBIRFQagRESFASgRUWEASkRUGIASERUGoEREhQEoEVFhAEpEVBiAEhEVBqBERIUBKBFRYQBKRFQYgBIRFQagRESFASgRUWEASkRUGIASERUGoEREhQEoEVFhAEpEVBiAEhEVBqBERIUBKBFRYQBKRFQYgBIRFQagRESFASgRUWEASkRUGIASERUGoEREhQEoEVFhAEpEVBiAEhEVBqBERIUBKBFRYQBKRFQYgBIRFQagRESFASgRUWEASkRUGIASERUGoEREhQEo9dR///nx93/PX/31f5TcXt7KLoWoaQBKPWXo+/z4reDm6lYASicFoNRTmr7vjxJA9a0AlE4KQKnHjgFKdFIASj0GoHSLAJR6DEDpFgEo9ZR89vIHwrk/5su+na9frxP94z+vS/72L/lfH+ZreyvnOVDvxvLyr9cl//jPuRtGzwxAqaeWgP75q/oPReIPoP+PRvPrQ/fyMALoX797N35d/n//6iyc6FgASj21ANT4+WHe3/Q/5ot+cf2cH7ovATV+qhv/XG6Td1uJjgSg1FPh25he5M3SfUsz5YN2+fj7xeNv+ov5ovBtTJ9S1lnaX4QG9A91CU+X0uEAlHoqBPTbPFv5o+Qs6ae55FuROt9NDdl9/ffPxepupvrqBah6JuDL3JioPAClngoB/bSPtNVFn5G7jj+4xgB1kPyav/MCVN34h1ReR6LDASj1VADoj4yGuR/yXhx+Ll7+mR+YxwB1ripv/HO59hhAqUYASj21BNTtRd6n++rPt/lWBFCHS+UlgFLlAJR6KgDUeQ0+Aqj73Tig5jfi5V1ZAKXKASj11BLQkDkLqL57+vd/x58D5R4oNQ9AqaeWD+HDj1WygH6Zl9QTLyI5z4HKV+wBlCoHoNRTAaCv14eCl4wMoI6G3/EXkZxX4T/1q/AASjUDUOqp8G1M9l6mfRvTAtDXY/m97wMFUKoZgFJPOYDOvs3Pc873QT8/9BvptYGfis21mMOuAAABYElEQVQv84uZ+lax30R6fQNAqXIASj2l6ZMvsP/hvlFp8dZ691vyQn2rld+FB1CqGYBST2n65G+t++9kkr9D5LyN6VN/539TT5XqW618GhOAUs0AlHrK0DdbKF8Ckh+6ZF8Osm+kl7j+Zn8tXt1q5fNAAZRqBqBERIUBKBFRYQBKRFQYgBIRFQagRESFASgRUWEASkRUGIASERUGoEREhQEoEVFhAEpEVBiAEhEVBqBERIUBKBFRYQBKRFQYgBIRFQagRESFASgRUWEASkRUGIASERUGoEREhQEoEVFhAEpEVBiAEhEVBqBERIUBKBFRYQBKRFQYgBIRFQagRESFASgRUWEASkRUGIASERUGoEREhQEoEVFhAEpEVBiAEhEVBqBERIUBKBFRYQBKRFQYgBIRFQagRESFASgRUWEASkRUGIASERUGoEREhQEoEVFhAEpEVBiAEhEVBqBERIX9/wEF+uLbYSGC7QAAAABJRU5ErkJggg==" title="plot of chunk unnamed-chunk-31" alt="plot of chunk unnamed-chunk-31" width="672" /></p>
<pre class="r"><code># Check histograms
hist(out$sims.list$beta[,1])
abline(v = 0, col = &quot;red&quot;)</code></pre>
<p><img 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" title="plot of chunk unnamed-chunk-31" alt="plot of chunk unnamed-chunk-31" width="672" /></p>
<pre class="r"><code>hist(out$sims.list$beta[,2])
abline(v = 0, col = &quot;red&quot;)</code></pre>
<p><img 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xNhgoCWH0ddT7qc3S6lLmfHNAEtP466XFCZMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OugSUMAS0/DjqElDCENDy46hLQAlDQMuPoy4BJQwBLT+OumoHdPV28mn+YA+atgho+XHUVTWg67e+uQ31oGmLgJYfR10VA/rj89u1P/6adQseNG0R0PLjqKteQH997xfz59ftf3/8e85NeNC0RUDLj6OuegHd3ORyl9Rvc27Cg6YtAlp+HHXVC+jq9gX79knorLdBPWjaIqDlx1FXtYBun25+ufniet5reA+atgho+XHUVS2g22ebty/XN/MOI3nQtEVAy4+jLgElDAEtP466vIQnDAEtP466HEQiDAEtP466nn0a0+2z0gkeNG0R0PLjqOvJJ9LP+yiSB01bBLT8OOqq+FHOfTH7Pvw56xY8aNoioOXHUVfVi4msrvrpYiJMEdDy46jL5ewIQ0DLj6MuF1QmDAEtP466BJQwBLT8OOryEp4wBLT8OOpyRXrCENDy46jLFekJQ0DLj6MuV6QnDAEtP466XJGeMAS0/DjqcjERwhDQ8uOoy+XsCENAy4+jrkAXVL455PTmQdMWAS0/jroElDAEtPw46vISnjAEtPw46nIQiTAEtPw46nJFesIQ0Klx2cr+APS4Ij1hCOjUOAGNyBXpCUNAy4+ziOpyRXrCENDy4yyiulzOjjAEtPw4i6guF1QmDAEtP84iqktACUNAy4+ziOqqGtCfX49H3dcZR5B+2/etEdDy4yyiumoGdHU8bWlzeht05vWU7fvGCGj5cRZRXRUDujo+69xcDsM7jYkJAlp+nEVUV72A7n6jx+7I++500P1Tz92Z9U6kZ5yAlh9nEdVV87Pwh8sxrS9nMG18lJMJAlp+nEVUV82rMX06/v/laefK1ZgYJ6Dlx1lEdVW/Huj2/y/RXM97DW/ft0VAy4+ziOp6RkAvn0GavKDyLfu+LQJafpxFVFf1CyqfXsrvCSgTBLT8OIuorpoHkT4d/7/7Et57oIwS0PLjLKK6al5Q+XDW54/P51+ONPhrPibY920R0PLjLKK66l5Qef8c9HLkaDXzTHr7vi0CWn6cRVRXxU8i7c6k379kP3Zz94mkWU9A7fvGCGj5cRZRXTU/C78v6CNXVLbv2yKg5cdZRHXVvZzd1SXpb39R/DT7vi0CWn6cRVRX7euBXn4x0qzj7wf2fVsEtPw4i6guF1QmDAEtP84iqktACUNAy4+ziOoSUMIQ0PLjLKK6BJQwBLT8OIuoLgElDAEtP84iqktACUNAy4+ziOoSUMIQ0PLjLKK6BJQwBLT8OIuoLgElDAEtP84iqktACUNAy4+ziOoSUMIQ0PLjLKK6BJQwBLT8OIuoLgElDAEtP84iqktACUNAy4+ziOoSUMIQ0PLjLKK6BJQwBLT8OIuoLgElDAEtP84iqktACUNAy4+ziOoSUMIQ0PLjLKK6BJQwBLT8OIuoLgElDAEtP84iqktACUNAy4+ziOoSUMIQ0PLjLKK6BJQwBLT8OIuoLgElDAEtP84iqktACUNAy4+ziOoSUMIQ0PLjLKK6BJQwBLT8OIuoLgElDAEtP84iqktACUNAy4+ziOoSUMIQ0PLjLKK6BJQwBLT8OIuoLgElDAEtP84iqktACUNAy4+ziOoSUMIQ0PLjLKK6BJQwBLT8OIuoLgEljEUF9C1X7nTZ25k5kBQCShgLCmh2PgX0fRFQwlhUQJ88UEBjElDCENBAE5JEQAlDQANNSBIBJQwBDTQhSQSUMAQ00IQkEVDCENBAE5JEQAlDQANNSBIBJQwBDTQhSQSUMAQ00IQkEVDCENBAE5JEQAlDQANNSBIBJQwBDTQhSW4D+vM///2C7Rhk37dFQANNSJKBgH59e/v0gi0ZYN+3RUADTUiS4YBufXnBxlyz79sioIEmJMnQe6Dr44ULvz19a67Y920R0EATkmTkINLqkNA//nru1lyx79sioIEmJMnoUfhf3w8N/fjCQ0r2fVsENNCEJJk6jen4bujrDinZ920R0EATkuTOeaDH56Ef/nzO1lyx79sioIEmJMlkQNeX34T1kmeh9n1bBDTQhCQZD+j6fBxp/zT0Fac12fdtEdBAE5JkJKBXZzL9/PqSA/L2fVsENNCEJJk6D7TzpHMloFQnoIEmJMnoJ5F6py9tX8S/4nQm+74tAhpoQpIMB/RFR92v2fdtEdBAE5JkKKAv/wjniX3fFgENNCFJXA+UMAQ00IQkGQ7o+nD4/efX134Y3r5vi4AGmpAkQwHdvQu6D+iPz6+9NKh93xYBDTQhSQYCejlvfvPST8Lb960R0EATkmQgoOvOUfj1Sy8Lat+3RUADTUiS24Bun4B23vncvop/3VNQ+74tAhpoQpIMngfa/dz7+oVXBLXv2yKggSYkyWBAuy/aNy+8LL193xYBDTQhSQSUMAQ00IQkGXwPtPuifeUlPE8ioIEmJMnwUfjLm6CbV/6CY/u+LQIaaEKSDAS0czWR3R9f+GEk+74tAhpoQpIMfRJp89b1wkuL2PdtEdBAE5Jk8LPwu49wHr30wnb2fVsENNCEJBm5GtPxqsovvi6ofd8WAQ00IUlczo4wBDTQhCQRUMIQ0EATkkRACUNAA01IknsHkd5eeR6Tfd8WAQ00IUmmfq2xgPJUAhpoQpIMBLR/GqiA8iwCGmhCkgwEdPXSTx912fdtEdBAE5Lk3gWVX8q+b4uABpqQJHcvqPxK9n1bBDTQhCQRUMIQ0EATkmTwJbyA8goCGmhCkgxeD/R1l1Dus+/bIqCBJiTJ8PVAyz0FXZ3Phsr45Z72fVsENNCEJBk6kf7H50LPQa/OyJ/dUPu+LQIaaEKSDB5EKnMiff8DoTk3Zd+3RUADTUiSegH99b0/dn+7857Z2vdtEdBAE5KkXkA3N7ncJXXWLwix79sioIEmJEm9y9mtbsu7bfOst0Ht+7YIaKAJSVItoIOnk848Q8q+b4uABpqQJNUCun22eftyfTPv/QD7vi0CGmhCkkz8UrkPf/5e55/OJKDMJaCBJiTJYEAPx5F2Ac3/tfBewjOXgAaakCRDAb38TuPd54hyC+ogEjMJaKAJSTIQ0N3ZRh///vF590vhH7i48vBpTLM+JGrft0VAA01IkuFf6fFl9zmiXUB3L+IzPxg/eCL9vBrb920R0EATkmTwV3rsnjkeA7rNYO6BpJsz8vfvCsxh37dFQANNSJLR64EeA7p9Cpr/Cz5WV/10MRGmCGigCUky+FHO3XGjU0Bnnnl0zeXsSCaggSYkSe2APsS+b4uABpqQJHdfwq9eeH16+74tAhpoQpIMHkTaPec8BnTuqZu3N+YlPIkENNCEJBk+jenTKaCzr0DX44r0zCGggSYkydAnkfZnz+8Dup59DeQOV6RnHgENNCFJJj7K+dDllF2RntkENNCEJBm8mMi+fQ/20xXpmUtAA01IkpHL2R0T+sgZTC4mwkwCGmhCkrgiPWEIaKAJSRLoivQ3h5ze7Pu2CGigCUkioIQhoIEmJEm1X2vsJTxzCWigCUlS7/fCO4jETAIaaEKS1AuoK9Izk4AGmpAk0++BPnA5ZVekZy4BDTQhSe4dRJr5rmWXK9Izj4AGmpAk9wK6fR6ZfzERV6RnDgENNCFJ7p7G9MBT0B2XsyOZgAaakCQJAXVFep5DQANNSBIBJQwBDTQhSe4F9OfXMr/SY3s783+/vH3fFgENNCFJ7gR0dyT9oV/p0bkhAWWagAaakCT3T6SfeerRCAHlLgENNCFJ7gd0fvcGbybnY032fVsENNCEJLkX0OwjSALKXAIaaEKSVLuc3e6z8ALKHAIaaEKS1Avo/jno+Qi+90C5S0ADTUiSigHdfwzpdAhKQLlLQANNSJKqAd3/avhDNwWUuwQ00IQkqRvQ/TXt9i/jBZS7BDTQhCRJuKDyY4fj12/7XwYvoNwloIEmJEn1gO5fxn8SUO4T0EATkqR+QPcv4//4PwLKPQIaaEKSDL0Hun3OeDx4votp5ieRutaZH2my79sioIEmJMlAQHuvtjdFPgu/exkvoNwhoIEmJMlAQFe96y+tylyNaSWg3COggSYkyW1Ar34L0sYFlXkSAQ00IUkGDyIJKK8goIEmJMlgQLsvtldlrkifxb5vi4AGmpAkg++Bdp6Croschs9k37dFQANNSJKBgO4OmR9ftnf++Ar2fVsENNCEJBk6D3TdPYG+zG/0yGPft0VAA01IksGLiWxKfACpAPu+LQIaaEKSjFyNaX/m+4vzad+3RkADTUiSypeze4x93xYBDTQhSQSUMAQ00IQkGQno7ioiH/78vX7dOaA79n1bBDTQhCQZDOjhina7gL71PpX0bPZ9WwQ00IQkGQro8Yqg24Cu3l5aUPu+LQIaaEKSDAT08HuMfnzenQG6ciI9TyOggSYkyUBAN/tPbx4C6qOcPI+ABpqQJIOfhd8dOjoGdPt01MVEeA4BDTQhSQavB7p7znkM6PYpqMvZ8RwCGmhCkoxeD/QUUNcD5VkENNCEJBFQwhDQQBOS5O5LeBdU5lkENNCEJBk8iLR7znkM6Pb5aJFfKpfFvm+LgAaakCTDpzF9OgV0d07o686kt+/bIqCBJiTJ0CeR9mfP7wO6u7TyCz8Ob9+3RUADTUiSiY9yvv6KyvZ9WwQ00IQkGbyYyO6Fe4B+2veNEdBAE5Jk5HJ2x4S6Ij1P9IqAvuXKnu+54yyiulxQmTCeH9DsfAooe0OnMb34eeeFfd+WVwR0KQMFNKbBE+lfex36C/u+LQIaaEKSDH6U83WnzvfZ920R0EATksQzUMIQ0EATkmT4k0iv/EVIHfZ9WwQ00IQkGToK/+NzkOeg9n1bBDTQhCQZfA+0z+XseA4BDTQhSQSUMAQ00IQkEVDCENBAE5LEJ5EIQ0ADTUgSASUMAQ00IUkElDAENNCEJOkGNNBnkA7s+7YIaKAJSSKghCGggSYkyXBAf3ze/2bOV7Pv2yKggSYkiYAShoAGmpAkAkoYAhpoQpIIKGEIaKAJSSKghCGggSYkiYAShoAGmpAkAkoYAhpoQpIIKGEIaKAJSSKghCGggSYkiYAShoAGmpAkAkoYAhpoQpJcBXSICyrzHAIaaEKSCChhCGigCUkioIQhoIEmJIkLKhOGgAaakCQCShgCGmhCkggoYQhooAlJIqCEIaCBJiSJgBKGgAaakCQCShgCGmhCkggoYQhooAlJIqCEIaCBJiSJgBKGgAaakCQCShgCGmhCkggoYQhooAlJIqCEIaCBJiSJgBKGgAaakCQCShgCGmhCkggoYQhooAlJIqCEIaCBJiSJgBKGgAaakCQCShgCGmhCkggoYQhooAlJIqCEIaBX7kmwAAAVYUlEQVSBJiSJgBKGgAaakCQCShgCGmhCkggoYQhooAlJIqCEIaCBJiSJgBKGgAaakCQCShgCGmhCkggoYQhooAlJIqCEIaCBJiSJgBKGgAaakCQCShgCGmhCkggoYQhooAlJIqCEIaCBJiSJgBKGgAaakCQCShgCGmhCkggoYQhooAlJIqCEIaCBJiSJgBKGgAaakCS1A7p6O/k0f7B93xYBDTQhSaoGdP3WN7eh9n1bBLTKhNkyZ2xKxYD++HyzR/74a9Yt2IVtEdAqEwpoRfUC+ut7v5g/v27/++Pfc27CLmyLgAaa0OpLUi+gm5tc7pL6bc5N2IVtEdBAE1p9SeoFdHX7gn37JHTW26B2YVsENNCEVl+SagHdPt38cvPF9bzX8HZhWwQ00IRWX5JqAd0+27x9ub6ZdxjJLmyLgAaa0OpLIqCEIaCBJrT6kngJTxgCGmhCqy+Jg0iEIaCBJrT6kjz7NKbbZ6UT7MK2CGigCa2+JE8+kX7eR5HswrYIaKAJrb4kFT/KuS9m34c/Z92CXdgWAQ00odWXpOrFRFZX/XQxEaY8ENBnf95bQNlzOTvCyA5odj6Xk6XlbGlbXFCZMB4I6LsfuJwtbYuAEoaABprQ6kviJTxhCGigCa2+JK5ITxgCGmhCqy+JK9IThoAGmtDqS+KK9IQhoIEmtPqSuCI9YQhooAmtviQuJkIYAhpoQqsvicvZEYaABprQ6ksS6ILKBT8nwiIJaKAJBTSJgBKGgAaaUECTeAlPGAIaaEKrL4mDSIQhoIEmtPqSuCI9YQhooAmtviSuSE8YAhpoQqsviSvSE4aABprQ6kviivSEIaCBJrT6kricHWEIaKAJrb4kLqhMGAIaaEKrL4mAEoaABprQ6ksioIQhoIEmtPqSVA3o/pLKnZNBf32fdx6TXdgWAQ00odWXpGZAV9eHjwSUKQIaaEKrL0nFgF4OwJ+ehAooUwQ00IRWX5J6Ad29ft9dj2l9KaiAMkVAA01o9SWpeTGR4+eOdp/pPBRUQJkioIEmtPqS1Lyc3fm9z9WxoALKFAENNKHVl+Q5F1Q+FlRAmSKggSa0+pI86Yr0q/2xeAFlioAGmtDqS/KkgB4uBSqgTBHQQBNafUme8h7o7+PvhBdQpghooAmtviT1jsKv33q/Ve7H57cP/yKgTBDQQBNafUnqngfa/QUem/059QLKKAENNKHVl6TiJ5HWV1cB3QgokwQ00IRWX5Kan4XfFbT7HHT3nFRAGSWggSa0+pJUvRrTze+GXwso4wQ00IRWXxLXAyUMAQ00odWXREAJQ0ADTWj1JRFQwhDQQBNafUkElDAENNCEVl8SASUMAQ00odWXREAJQ0ADTWj1JRFQwhDQQBNafUkElDAENNCEVl8SASUMAQ00odWXREAJQ0ADTWj1JRFQwhDQQBNafUkElDAENNCEVl8SASUMAQ00odWXREAJQ0ADTWj1JRFQwhDQQBNafUkElDAENNCEVl8SASUMAQ00odWXREAJQ0ADTWj1JRFQwhDQQBNafUkElDAENNCEVl8SASUMAQ00odWXREAJQ0ADTWj1JRFQwhDQQBNafUkElDAENNCEVl8SASUMAQ00odWXREAJ4x//eMuVOeNyBi5nS9sioIQhoIEmtPqSCChheAkfaEKrL4mAEoaABprQ6ksioIQhoIEmtPqSCChhCGigCa2+JAJKGAIaaEKrL4mAEoaABprQ6ksioIQhoIEmtPqSCChhCGigCa2+JAJKGAIaaEKrL4mAEoaABprQ6ksioIQhoIEmtPqSCChhCGigCa2+JAJKGAIaaEKrL4mAEoaABprQ6ksioIQhoIEmtPqSCChhCGigCa2+JAJKGAIaaEKrL4mAEoaABprQ6ksioIQhoIEmtPqSCChhCGigCa2+JAJKGAIaaEKrL4mAEoaABprQ6ksioIQhoIEmtPqSCChhCGigCa2+JAJKGAIaaEKrL4mAEoaABprQ6ksioIQhoIEmtPqSCChhCGigCa2+JAJKGAIaaEKrL4mAEoaABprQ6ksioIQhoIEmtPqSCChhCGigCa2+JAJKGAIaaEKrL4mAEoaABprQ6ksioIQhoIEmtPqSCChhCGigCa2+JAJKGAIaaEKrL4mAEoaABprQ6ksioIQhoIEmtPqSCChhCGigCa2+JAJKGAIaaEKrL4mAEoaABprQ6ksioJT3lkdAA01o9SURUErLzKeAhprQ6ksioJSWvdsENNCEVl8SAaU0Aa0w8BVbmi1zxiUSUEoT0AoDBTQmAaU0Aa0wcDlb2tayFVBKE9AKA5ezpW0tWwGlNAGtMHA5W9rWshVQShPQCgOXs6VtLVsBpTQBrTBwOVva1rIVUEoT0AoDl7OlbS1bAaU0Aa0wcDlb2tayFVBKE9AKA5ezpW0tWwGlNAGtMHA5W9rWshVQShPQCgOXs6VtLVsBpTQBrTBwOVva1rIVUEoT0AoDl7OlbS1bAaU0Aa0wcDlb2tayFVBKE9AKA5ezpW0tWwGlNAGtMHA5W9rWshVQShPQCgOXs6VtLVsBpTQBrTBwOVva1rIVUEoT0AoDl7OlbS1bAaU0Aa0wcDlb2tayFVBGPft34ghooAkFNImAMuL5v1RMQANNKKBJBJQRz19AAhpoQgFNIqCMENBIA5ezpW0tWwFlhIBGGricLW1r2QooIwQ00sDlbGlby1ZAGSGgkQYuZ0vbWrYCyggBjTRwOVva1rIVUEYIaKSBy9nStpatgDJCQCMNXM6WtrVsBZQRAhpp4HK2tK1lK6CMENBIA5ezpW0tWwF9/579iUwBrTBwOVva1rIV0Pfu+R9pF9AKA5ezpW0tWwF97xa0gAQ00IQCmkRA37sFLSABDTShgCYR0PduQQtIQANNKKBJagd0dX5D7dP8wU3tibsW81amgFYYuJwtbWvZVg3o+molz21oU3vijux8LmgBCWigCR8Z+OyH6gtVDOiPzzf3zx9/zbqFJd6htSxqAWUOFNBAEwpoknoB/fW9X8yfX7f//fHvOTexxDu0lkUtoMyBAhpowiUNfKF6Ad3c5HKX1G9zbmKJd2gtC3o4C2iFgcvZUgEtY3X7gn37JHTW26BLvEPve/LLmwUNFNBAE3rtn6RaQLdPN7/cfHE97zX8jPtlMff80x8jCxoooIEmFNAk1QK6fbZ5+3J9M3UY6YH75YF7njj+8Y9XbwELlVmpxwkoYQgomTIr9bjQL+FpS/ZLeHiR0AeRaIuAsjTPPo3p9lkpHAkoS/PkE+lnfhSJpggoS1Pxo5z7YvZ9+LPedCyegLI0VS8msrrqpzdAmSKgLE3oy9nRFgFlaUJfUJm2CChLI6CEIaAsjYAShoCyNAJKGALK0ggoYQgoSyOghCGgLI2AEoaAsjQCShgCytIIKGEIKEsjoIQhoCyNgBKGgLI0AkoYAsrSCChhCChLI6CEIaAsjYA+XdXfTrhofisnmV62mgX0yV79SItMQMn0svUsoE/2wn0dnvtmnPtmgoC2w0IY574Z576ZIKDtsBDGuW/GuW8mCGg7LIRx7ptx7psJAtoOC2Gc+2ac+2aCgLbDQhjnvhnnvpkgoO2wEMa5b8a5byYIaDsshHHum3HumwkC2g4LYZz7Zpz7ZoKAtsNCGOe+Gee+mSCg7bAQxrlvxrlvJghoOyyEce6bce6bCQLaDgthnPtmnPtmgoC2w0IY574Z576ZIKDtsBDGuW/GuW8mCGg7LIRx7ptx7psJAtoOC2Gc+2ac+2aCgLbDQhjnvhnnvpkgoO2wEMa5b8a5byYIKMDyCChAJgEFyCSgAJkEFCCTgAJkElCATAIKkElAATIJKEAmAQXIJKAAmQQUIJOAAmQSUIBMAgqQSUABMgkoQCYBBcgkoACZBBQgk4ACZBJQgEwCCpBJQAEyCShAJgEFyCSgz/Dz69vWl9u/+PX97ezb87frtUbvlcm/aoNHzB0/v/7x1+CXn/zAEdAnWB0f8B/+vP6bw/5uczmM3ytTf9UGj5g7tv+ODAX0+Q8cAa1vfX7E3+zzzVuzy2HiXpn4qzZ4xNyzGnxsvOCBI6DV/fj89vbx78ND/9PV362bXQUT98rUHdYEj5g79u9j3DbyFQ8cAa1uddqb2/17/dJi1epzrKl7ZeoOa4JHzLRdKIcC+ooHjoDW9vPreVevr9/e3v5LuvsXs0ET98rUHdYEj5hJ+3eB/8PAe6AveeAIaG3bfw0/Xf7Yf/Rvd3mTr1En75WpO6wJHjGTdm90fhs6iPSSB46A1tb5x/Bmr2+2D4X9P6jNvVKduFem7rAmeMRMWu8yOfTIeMkDR0Br67zrv92t/cf9+u3DPx8PGzb2TGviXpm6w5rgEXPfSECf/8AR0NpWncOmq6vdunq7aGs9TNwrU3dYEzxi7hsK6EseOAJaW3dXXu3W3dkYhy/sTrxo6nDJxL0y8Vdt8Ii5bzigL3jgCGht/d3aO4nv59fz3/343Na7fRP3ysRftcEj5r77AX3SA0dAa0v8d7GxVHgGOs4j5j7PQN+18weWt3sxcbc2dsqjgI7ziLlPQN+1bkATjw1u2loOjsKP84i5z1H4d60b0E3a2WmNLYeJeyXxDnu/PGLuG7pjXvLAEdDaEj8f0dhFInwSaZxHzH0+idSMiU/o9j860dSLVZ+FH+cRc99QQH0W/n0av0ZM50yUTWtXbnM1pnEeMXcNvkZ3NaZ3afwqhbvTog8Pg3VzH252PdBxHjF3DQbU9UDfp5vrZJ9Phz5c2PCgtdeq4/eKK9J7xNzTDehLHzgC+gTXv6nlsscv66G94wHj94rfieQRc8dwQP1OpHfq6ncFdvb48XfcNPlKdeJe8Vs5PWImjQTUb+UEWAwBBcgkoACZBBQgk4ACZBJQgEwCCpBJQAEyCShAJgEFyCSgAJkEFCCTgAJkElCATAIKkElAATIJKEAmAQXIJKAAmQQUIJOAAmQSUIBMAgqQSUABMgkoQCYBBcgkoACZBBQgk4ACZBJQgEwCCpBJQAEyCShAJgEFyCSgAJkEFCCTgAJkElCATAIKkElAATIJKEAmAQXIJKAAmQQUIJOAAmQSUIBMAgqQSUABMgkoQCYBBcgkoACZBBQgk4ACZBJQavr1/Y+/bAPvloBS2s//dfrT5m3nw5/D3/fr+1tu2Y5Dx29hbBt+fH77dPfWL4P7X/469dPsbvry14f/EO53T0ApbPX25fCHQ0R2Pv499I0VAzq6DSkBPQ++MhnQw1+eqymgjRBQyto+4zv059KukYLWC+j4NiQE9Dz42lRAL/08ZFNAGyGglHXuz2pbkG+79x/Xuz+UneROe8e34cGAjk66u/0vpxk/3f123gsBpaxTf7b92D5Z2x/A2Yy9iM+WGNDbbagU0N0T0OO/EevTE08BbYGAUtapP9tUbYu1j9cDr9VHJAb0dhsqBXTzdr7Z7USHlgpoCwSUeQ5HtY9Pt7a16Bzf3sZqc3wf8Fs3XteDP50H7/5yG5rtN67Pby+u37ov+Xsjzm7eA+1+29Q27AO6f8Oyc5PdH+kyeGfVPYQ/UcR1J7orAW2IgDLH+WDJoQ0TAe28fD65HNL5chx8Cui/fT99/TTBp6ERZ1cB7X/b1DbsAnr87s6Gd36kbkDX/QNQiUVcHW9ZQFsgoMzQOdi8j8NEQA/HVbrx6h6p/va7F9D/efzyh3/53v2O6xFn/YBefdvUNmy38p9O333Y8s6B+t23XQ3uhjmtiIcn1MnfzrIJKDOsji1ZH58j3gS08xbir++X0O6tL9Xdf2s3f5cOnk4C+jQw4qwf0JtvG9+GfS5337O5/ASHH+H0he4RqMsfdkPSinh+NS+gLRBQ0m3zc/mkze5PUwG9PIU7duT05uDA88dDHTdvnT/t/nA94qx/CzffNr4NpzSff4LLGQKH1/uXwZcDQ9tvPW7o/SKe7gYBbYOAkm59OfZyeKI1HdDLC+JPxyFDTyMvpwCdEnaOz/WI66GXZ6D9bxvfhss/Abvsfrm8Y3kZdXsU/rg1KUU8trYzindNQEm3urwVeTgf6F5AT6+huwd3vnX+rt+lmz9dj/h9NfT4fzffNr4NlyeIh2eY53cszz/STUD3wxMDuum8XSCgLRBQknVyeT5D6G5AtxFZHd9mXHUPyaQE9HpEd0u6R+Gvv218GzrngV7OaLo4vjt6Grw5/0VaQLv9FNAmCCjJuu9EHp66pQX0+Gq5e1z7y++kgF6NuN6S8wZdfdv4NvQD+vHv7oflrwPa/bukgK56byUIaAsElGS5z0A72To/4/uSFtD+iO6WXF1MpPdt49tw/Qy085L+pHsU/tDOtPdAdy/1uyf8C2gLBJR0nfdAD8eoJwJ6+N7rgO7sy3Sob0JAuyPOhq/GdP628W0YeA/0unKby9Pl/tZMF3H3fLX3LFlAWyCgpOschT++Kr8cxF5fnYO5Phb2eJj8y9XTxcvH08cDejPirBfQ228b3YbuUfJ9Xc+fXL/YnN9gOP1om4SX8J3D+78HN5l3SUBJd3Me6K5C54u39QN6+I59vHZPzr5d3glNDujNiLOb80D73za+DZfzQI9VvDzPvDqN6RLQ3Y92L6CXM7C6XxLQd09AmaH7SaR9LteXdx0vAT28SN5/7y5em7f+h3/2RfqUehpTd8TZ7WlMvW8b34b9gaHjh5z2/79/2f/t+L2nE+n3g1fHtO4PUO3+ZqKIA09kBbQJAsoM15+F73zhv389X/P9kKTuGUKHZ2ert97oOacxHec7PdEbO42p9ws1hrah+1n4w1yXU5UuT1v3g7t/cRXQm+ebve91NaaGCChzXF2N6ZKOL6dT0g+nrX/pfu+pNr++976QENCrEWMBvf628W3oXI3p9BNcX/HpPPic5f/0/VTjkYBe5hfQxggo8/SuB7qzOtTp/JmefU16V6O7HJw+9qz3yfXpo/C9EWMBvf628W24nD3fOWJ+OIv0/BbBefBh6JfTKQfjAb06HV9A2yGg1BThd7KX2oZuEdejv9148Nt5rwSUmt5rQFf3b1JAWyCg1PROA/rr+/3fkiegLRBQSPPz8nvh1/d+T7PfC98IAYU0l4D++n7v99wLaCMEFNJ0noHeJaCNEFCATAIKkElAATIJKEAmAQXIJKAAmQQUIJOAAmQSUIBMAgqQSUABMgkoQCYBBcgkoACZBBQgk4ACZBJQgEwCCpBJQAEyCShAJgEFyCSgAJkEFCCTgAJkElCATAIKkElAATIJKEAmAQXIJKAAmQQUIJOAAmQSUIBMAgqQSUABMgkoQCYBBcgkoACZBBQgk4ACZBJQgEwCCpBJQAEy/X8stbHd1e1uYgAAAABJRU5ErkJggg==" title="plot of chunk unnamed-chunk-31" alt="plot of chunk unnamed-chunk-31" width="672" /></p>
<pre class="r"><code># Extract mcmc samples
all.mcmc = mcmc(out$sims.matrix)
dim(all.mcmc)</code></pre>
<pre><code>## [1] 3000   64</code></pre>
<pre class="r"><code>colnames(all.mcmc)</code></pre>
<pre><code>##  [1] &quot;Res[1]&quot;     &quot;Res[2]&quot;     &quot;Res[3]&quot;     &quot;Res[4]&quot;     &quot;Res[5]&quot;    
##  [6] &quot;Res[6]&quot;     &quot;Res[7]&quot;     &quot;Res[8]&quot;     &quot;Res[9]&quot;     &quot;Res[10]&quot;   
## [11] &quot;Res[11]&quot;    &quot;Res[12]&quot;    &quot;Res[13]&quot;    &quot;Res[14]&quot;    &quot;Res[15]&quot;   
## [16] &quot;Res[16]&quot;    &quot;Res[17]&quot;    &quot;Res[18]&quot;    &quot;a[1]&quot;       &quot;a[2]&quot;      
## [21] &quot;a[3]&quot;       &quot;a[4]&quot;       &quot;a[5]&quot;       &quot;a[6]&quot;       &quot;beta[1]&quot;   
## [26] &quot;beta[2]&quot;    &quot;deviance&quot;   &quot;eta[1]&quot;     &quot;eta[2]&quot;     &quot;eta[3]&quot;    
## [31] &quot;eta[4]&quot;     &quot;eta[5]&quot;     &quot;eta[6]&quot;     &quot;eta[7]&quot;     &quot;eta[8]&quot;    
## [36] &quot;eta[9]&quot;     &quot;eta[10]&quot;    &quot;eta[11]&quot;    &quot;eta[12]&quot;    &quot;eta[13]&quot;   
## [41] &quot;eta[14]&quot;    &quot;eta[15]&quot;    &quot;eta[16]&quot;    &quot;eta[17]&quot;    &quot;eta[18]&quot;   
## [46] &quot;mu[1]&quot;      &quot;mu[2]&quot;      &quot;mu[3]&quot;      &quot;mu[4]&quot;      &quot;mu[5]&quot;     
## [51] &quot;mu[6]&quot;      &quot;mu[7]&quot;      &quot;mu[8]&quot;      &quot;mu[9]&quot;      &quot;mu[10]&quot;    
## [56] &quot;mu[11]&quot;     &quot;mu[12]&quot;     &quot;mu[13]&quot;     &quot;mu[14]&quot;     &quot;mu[15]&quot;    
## [61] &quot;mu[16]&quot;     &quot;mu[17]&quot;     &quot;mu[18]&quot;     &quot;sigma_Elev&quot;</code></pre>
<pre class="r"><code># all.a = all.mcmc[,grep(&quot;a&quot;,colnames(all.mcmc))]
all.beta = all.mcmc[,grep(&quot;beta&quot;,colnames(all.mcmc))]
# all.sigma.elev = all.mcmc[,grep(&quot;sigma_Elev&quot;,colnames(all.mcmc))]</code></pre>
<p>Compare with results from lme4</p>
<pre class="r"><code>summary(mixmod.pois)</code></pre>
<pre><code>## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson ( log )
## Formula: rich ~ scale(logprod) + (1 | elev)
##    Data: div
## 
##      AIC      BIC   logLik deviance df.resid 
##     95.5     98.2    -44.8     89.5       15 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
## -1.461 -0.629  0.022  0.535  1.935 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  elev   (Intercept) 0.0158   0.126   
## Number of obs: 18, groups: elev, 6
## 
## Fixed effects:
##                Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)      2.2557     0.0971   23.22  &lt; 2e-16 ***
## scale(logprod)   0.4921     0.0942    5.22  1.8e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(lgprd) -0.288</code></pre>
<pre class="r"><code>cbind(fixef(mixmod.pois), apply(all.beta,2,mean))</code></pre>
<pre><code>##                  [,1]  [,2]
## (Intercept)    2.2557 2.226
## scale(logprod) 0.4921 0.389</code></pre>
<pre class="r"><code># coef plots
mixmod.pois.beta.freq = exp(fixef(mixmod.pois))
mixmod.pois.se.freq = exp(sqrt(diag(vcov(mixmod.pois))))
mixmod.pois.beta.jags = exp(apply(all.beta,2,mean))
mixmod.pois.se.jags = exp(apply(all.beta,2, sd))

coefplot2(mixmod.pois.beta.freq, mixmod.pois.se.freq, col = &quot;red&quot;, 
          varnames = names(mod.beta.freq), xlim = c(-10,14))
coefplot2(mixmod.pois.beta.jags, mixmod.pois.se.jags, col = &quot;blue&quot;, 
          varnames = names(mod.beta.freq), add = TRUE, offset = -0.1)</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk unnamed-chunk-32" alt="plot of chunk unnamed-chunk-32" width="672" /></p>
<p>Finally for a coefficient plot of all three models together…</p>
<pre class="r"><code>coefplot2(mod.beta.freq, mod.se.freq, col = &quot;darkred&quot;, 
          varnames = names(mod.beta.freq), xlim = c(-10,14))
coefplot2(mod.beta.jags, mod.se.jags, col = &quot;red&quot;, 
          varnames = names(mod.beta.freq), add = TRUE, offset = -0.05)


coefplot2(mixmod.beta.freq, mixmod.se.freq, col = &quot;darkgreen&quot;, 
          varnames = names(mod.beta.freq), add = TRUE, offset = -0.15)
coefplot2(mixmod.beta.jags, mixmod.se.jags, col = &quot;lightgreen&quot;, 
          varnames = names(mod.beta.freq), add = TRUE, offset = -0.2)


coefplot2(mixmod.pois.beta.freq, mixmod.pois.se.freq, col = &quot;darkblue&quot;, 
          varnames = names(mod.beta.freq), add = TRUE, offset = -0.3)
coefplot2(mixmod.pois.beta.jags, mixmod.pois.se.jags, col = &quot;blue&quot;, 
          varnames = names(mod.beta.freq), add = TRUE, offset = -0.35)</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk unnamed-chunk-33" alt="plot of chunk unnamed-chunk-33" width="672" /></p>
<hr />
<p>For more on fitting models in a Bayesian framework in R, I highly recommend ‘<a href="http://www.highstat.com/BGGLM.htm">A Beginner’s Guide to GLM and GLMM with R</a>’ by Zuur et al. and ‘<a href="http://www.mbr-pwrc.usgs.gov/software/kerybook/">Introduction to WinBUGS for Ecologists</a>’ by Kery. The Zuur et al. book uses R2jags and comprises lots of useful examples based on real ecological datasets. The Kery book provides an excellent walk through from the basics through to more complex models (based on R2WinBUGS, but the code can easily be adapted to R2jags/JAGS).</p>
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