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<body>
<h1>Resampling Methods - Chapter 5</h1>
<h3>Loading Prostate Cancer dataset into R</h3>
<pre><code class="r">library(ggplot2)
prostate_cancer <- read.table("~/Documents/github/stat_learning/data/prostate_caner.dat", quote="\"")
colnames(prostate_cancer)[1] <- "idCode"
colnames(prostate_cancer)[2] <- "tumor"
colnames(prostate_cancer)[3] <- "age"
colnames(prostate_cancer)[4] <- "race"
colnames(prostate_cancer)[5] <- "rectalExamResult"
colnames(prostate_cancer)[6] <- "capsularInvolvement"
colnames(prostate_cancer)[7] <- "antigenValue"
colnames(prostate_cancer)[8] <- "tumorVolume"
colnames(prostate_cancer)[9] <- "gleasonScore"
prostate_cancer$race <- as.factor(prostate_cancer$race)
prostate_cancer$rectalExamResult <- as.factor(prostate_cancer$rectalExamResult)
attach(prostate_cancer)
</code></pre>
<h3>Plots of various predictors colored by whether it is cancerous or not</h3>
<pre><code class="r">ggplot(data=prostate_cancer,aes(x=antigenValue,y=tumorVolume,col=tumor))+geom_point()
</code></pre>
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" alt="plot of chunk unnamed-chunk-2"/> </p>
<pre><code class="r">ggplot(data=prostate_cancer,aes(x=age,y=tumorVolume,col=tumor))+geom_point()
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-2"/> </p>
<pre><code class="r">ggplot(data=prostate_cancer,aes(x=antigenValue,y=gleasonScore,col=tumor))+geom_point()
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-2"/> </p>
<pre><code class="r">ggplot(data=prostate_cancer,aes(x=age,y=gleasonScore,col=tumor))+geom_point()
</code></pre>
<p><img 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2t+od92/nW+ouhkgb/YpvNKay/rvAVgokcFfKqkzqM1cGLmWVlZVFBQ4EQO9yYNDg4mk8nk3pm4qfSIiAgKCgqizMxMN83BvcVeyH7W8hOU3eBiUYmK7JP02jX+FBJk+8N4Qvrxeu6PGCkNB+0qGtiohG5qV6io+DfHw+jH5DAxvlFoOc3plU0Bdr93eWZfmr8jlgrLeEIV3da+gPomOt42wsLCiD/p6emKeWk9orDMR9Qrz1wdaG9um08DGjuuV13nfSH7eb/HUGqJbF1F8ySvRuEVNsUXFBbR+MkPUVp69fb38L23083jRtik4YGZa/2o2L+BGM8/VQ91SqH28effGVMUcm7Ekn2RtDMzWAz5SOtofp8sig+ttEmemmeiMbc/SqU5Z8T4niMn03sPDbdJwwMLP9lAy957XYwPjIilr97/DzVtGGGTLruEaNbvcdK46p3CBsHl9O9Lsm3S1HXALPE9+0cspRVX7zyOSCqi0S2KFNl5Z/XXtJBz46tobs9sahZha282l9HEKY/S0ROnRLo7Jo6hGXfdrCjrd2mH9r0DkdJ4Hwrzr6QnemdTTJCtlyLTeUYEBARQQkICpaSkOEzl4+NDSUlJDqdhpDoBu58pdYUgFwSMJpAe0t5SJb/YJPr1QIZlWO75+RT/EMpH5D60PaP6x1+eLn9vkY7K5S5V+oE9WSAHL3ks0Z6swHPBncdJR/Jna/LUpNK/b192IMnBnee+xfLj7766pJZYt2D40IqToYqZ/f7HHktw54krVq1TpOERRX68A1bdcQD4/G/rsuUpdfveI1nIXZW0jtadUdZr6cajluDOafdv/UnOYvP985q1lmFzQTYt37jXMiz3fJ/MR+3y9kWUZaq9NUPOU9v3cWmbk4M7p7HeJq3z7My03vnxoZ9PK5dx38EjluDOeVesWm9dhKW/ehuurn9RuS/9JW3j6LxLAAHeu9YXaltHAd/imqPjynIztUlUNpG2iCy3KS0myPZIR56YGFoz3t+nihoE1ww7SsPjEkNsy5bT6f2dIOpe00iXKLVAuLvzr4lp0qyqqGOMWTHLpGaNiQO23LVo3kTutfn2qaypLx/Bt49SlmWT4TwDEQE1Dlyv1pFlitTdW8VLMbnmZzE8rrkiDY9IaNLMZnynlo1shnmgQzTXtWaefjWLq0h7oRFx0jbnJ217cme9Tcrj+Dsm0HbbbBlR4yena5IYTwEBNTtKtdnbz8N+WC4P38YV8Jsndcatnm3NSqTzRmaz+j9w29JcH/L396fycuUfkOslu78Ebp7n+hcXF7t/Zm6Yw4Xs20SU0B8Hz1BFcR5dFZtMl3WUfrjtulZSgE8r9qNs6fxiXEgF3XdRHoX41/yIysnbSz/UuVITfHhAJU1oU0gtHPxoxgZXUpT041osnSftEmumsa2KFM34cnmBgYHEn6IiZROrnEar72ipSZXrViQ11XeQAu0NrQspUP2BpKjWBe0jS+kPqTWkUqLsJM1zQhvlcsbGRFGzxomUk5tPfXp0oYfvvY1CpFNe9l3jgFzamREkhUkfiqN0ur+v+qPIzrGl9HdukFQvX7o03kTDk5Tbfst46dRJcGtKzcijBi0voucfnkSJ0cp69evennanSG3w/iE0ZsJNdMuQbvZVpybSaYnkQn9KL/GjQN8qmtY5T3FKQJGplhG8XTYJK6eCcn9qFWmmm9oWONxWuzYw076cQCqXWtJ7xpnEdmhfZFhYKLVv04IyMrOpS8e2NPuBuygivPoUlHVa3gEqlo7c/aW6X92smHrHu3YNhJ+fH4WHh1N+vuNrFniHLzo62roK6HdRAOfgXQC80LlIF4p2e9b6fg7e7YAuzEDPc/AuVLPWrN683XPdY2KkawRSU2tdPiNP4B1zPsjxokunLJw4B2+h0K2npi1Kt1liRhCAAAQgAAEIuFsAAd7dwigfAhCAAAQg4AEBBHgPoGOWEIAABCAAAXcLIMC7WxjlQwACEIAABDwggADvAXTMEgIQgAAEIOBuAQR4dwujfAhAAAIQgIAHBBDgPYCOWUIAAhCAAATcLYAA725hlA8BCEAAAhDwgAACvAfQMUsIQAACEICAuwUQ4N0tjPIhAAEIQAACHhBAgPcAOmYJAQhAAAIQcLcAAry7hVE+BCAAAQhAwAMCCPAeQMcsIQABCEAAAu4WQIB3tzDKhwAEIAABCHhAAAHeA+iYJQQgAAEIQMDdAgjw7hZG+RCAAAQgAAEPCPi7c56ffPIJpaWlWWbRsWNHGjFihGW4oKCAlixZYhkeNmwYdenSxTKMHghAAAIQgAAE1Am4NcDfdNNNVFlZKWq2ePFi4gBv3aWkpFDLli1p1KhRYrSfn5/1ZPRDAAIQgAAEIKBSwK1N9BywAwICaOvWrdSiRQtq1aqVTTU5wPv7+9OGDRsoIyODfH3dWh2beWMAAhCAAAQgUJ8F3HoEz3B8BM8B/OGHH1Y4ms1m8vHxocaNG9PSpUtp9OjR1LZtW0u6hQsX2jTxjxw5kgYPHmyZjh7XBcLCwlwvBCWoEkhKSlKVD5lcF4C964ZqS6jNXm7tVVsu8ikFfKqkTjlauzF79uwh/kycOPG8he7du1ek42Z9uSsqKqKKigp5kIqLi4l3CozSBQcHk8lkMkp1nKpHeHg4BQYGUnZ2tlP5jJLYm+1DQ0OJd6y41cobO2+2DwoKopiYGJsDB29aB1x//g1088+2W0i4NTcuLo7OnDnjsHw+2GvWrJnDaRipTsDtR/D79u2jbt26Oazd8uXLqVevXsR7dGfPnhVH8tYJ7Y8uy8rKDBVQeY/TW/c65R8Ib62/t9uzP+yt/9r16Wf3+mAv//3qo6bNXOTtXf62L5UDPDptBdx+0psDd6NGjWxq/cwzz1BhYSH16dOHVq5cSW+//TbxETwPo4MABCAAAQhAwHUBtzfR16WK3MzNzX4X6rKysohvrTNK581NlREREcTNfZmZmUbhdKoe3mzPLVP8SU9Pd2qZjZLYm+257txEn5qaahROp+rh7U30CQkJxBdXO+r4CL628/OO0mPchQXcfgR/4SpQnYJ7XcpBGghAAAIQgAAEqgUMEeCxMiAAAQhAAAIQ0FYAAV5bT5QGAQhAAAIQMIQAArwhVgMqAQEIQAACENBWAAFeW0+UBgEIQAACEDCEAAK8IVYDKgEBCEAAAhDQVgABXltPlAYBCEAAAhAwhAACvCFWAyoBAQhAAAIQ0FYAAV5bT5QGAQhAAAIQMIQAArwhVgMqAQEIQAACENBWAAFeW0+UBgEIQAACEDCEAAK8IVYDKgEBCEAAAhDQVgABXltPlAYBCEAAAhAwhAACvCFWAyoBAQhAAAIQ0FYAAV5bT5QGAQhAAAIQMIQAArwhVgMqAQEIQAACENBWwF/b4lCaNwhkZefSy29/TCUlpTT+2qHUpmVzt1f7y6NhtCMjmJqEldOUznkU6GDX8sSp0zTrmVeosKiYpt1xIw0ffLnDeu3KCKTd2SHUKMSXhjQtJj8HZTnM6GDk2/siaG92EEUHVtJjPbIpPFCZaOcpEz319ndUWpBDQ4aPoEevbqJMJI15d38E/ZkVRJHnyop0UNa2lDJ6/1AAVflUUHP/Kpp7uY/Dsh76/BClmiPIt7yYFl6XSE1jwxTp9pzKpYU/51CVXxB1iCymJ69ro0jDI+YvP0S/blhLUfFN6bXpQykxOkiR7lRaDk2b+x/Kz82lcWNH0YyJwxRpeMTEF9dQ6tF9FBAeQ8/dM5J6tW6gSLcjuZAW72tAvgHBFFGcTItGRynS8IjrF3xP4V2uJnP+WbqtdTYN699FkW7DgVz64EAE+QUGk0/hWXpvfKwiDY948tcAOlsuzaeqku7rlE6dE5Q/bau2H6O3NpyR6t6AzBnH6NsnRzgs644n3qLDJ9PIzz+Anr/verqkewdFuuOpOfTo0v3kExpLCZVp9Nq0KxVpeMQLX+ygNRu3UnB4FP1nxkhq10RZ/z1HztCDb6yi8pJ8io5LoK+fuclhWc+tTqdjPq2JKqpoWvt06tEyWpEuN7+YHvtsDxX5R1PniByaNeFSRRoesSU1iL5PDqMwaRu8q1MexYVUKtIVF5fQQ0+9RCelv8trrhpA0++cqEiDEcYW8KmSOmNXsaZ2WVlZVFBQUDPCw33BwcFkMpk8XAvnZ3/HfU/Qrj1/i4wx0ZH0/dLFFBKs/NF3vmTHOTacDqGlRyIsEztEl9LMbnmWYbln4LW3UV5+oTxIP3y+mBolxFmGuedYvj89vytG6qsOjCOTCmlUi2KbNHUd+O5EKK08yYGzuqzowAp6vm+WIvugqS9TzsHNYrxvUCi9tvgt6tcy1CbdquQQ+vp4uKWsKKmsFxyUdfeGhuTjU71Hwn96Y5ucoavbBtiU9drqE7QnoBf5+PqJ8QXHttNntyfZpOGBiR+lUURSVzG+osxEYxsepmu6J9ikW/Z7Bi2cNZ2oskKMb3rpWPruOeUP9RXjplN+Vpol7+svL6T+3W13GJ5cuptWvvucFEOry4rtchWtfW2qJY/cc+eaSBGQeZiXsWfAIZran9dZTTfj5W+ptMedkkW1fUnGSfpoXEhNgnN9t6/0pcCIhmKIy0oo2kfPjIi3Sff5nkpal5VgKYuD/NsDM23S8MD1r/5BDbtdLcZXVpRT5Z+f03szh9qke+d/a2jxOx9QVblZjA9v1oU2fTTPJg0PjPvvTmrQfbgYXyWV1Tn3J7p/bB+bdL/sPU0zZz5KleWlYnxsh8to7eIHbNLwwFX3v01Zf62pHi9tG/1H3UKvzxxtk27dviz6PKODZRm5zHcGK/+G7nhrBwV0vEbkrZIcrqxYRzcNrt5G5ALTin3pqe28Y1ZtHxlQQS/2U273t8+YS7v3HpCz0XxpOxo1dKBl2NmegIAASkhIoJSUFIdZeVtISlJu5w4TY2SdBFw49qlT+UhkQIG9Bw5bapWTm0+nU89aht3Rsy/bNoCdLlIeXfF8rYM7D2/ftZe/bLoT+VxW9Q8TTzguhm2S1HngL+lo27qs/DLHfw4FKfstZVaWFtOqP9Mtw3LP7kzbsgrMjsuynh//oG06o9yx+vOM2RLcuXy/UNvgKM8zMDpR7iU/6Wh5w8F8y7Dcs+mvE5bgzuNyTtUsi5yGvwtyMqwH6ectu22GeeDPQ8mW4M7DpswT/KXofANqlomX8U9hY5sspSzaEqx4SpDVslin9A+JtAxyWclFwZZhuWdHdphNWVVW24echr9D4qWj33Odr58/nS2o3lGRx/H36t/2WYI7D5vzbV14HHdBcS3EN//nI5W184TSfvXOE5bgzukKU2v+7nhY7kzZp+Ve0QJxNFUZuNeflJZKWn658/Fz0DwkTTSH1bQu8Y7kjhM1O8xy3r+lFivr7bCglu3+2EnbQPzr1j/kIvDtJQK1/Qp5SfVRTTUCA/r2smRLataY+OPOrl8jbuWoaSjqFFN9dGQ/z8aJNUdmflK7+4B+ve2TUKdYM/n71JTVraHjshQZHYwY2LhYGltTVqOQcgepiGI79LeMD4iMo1v61wRWecKVdmUl1FKWfCTN+fiIdFyb6qM7uRz+HtYhnCrMJTWjipQ7FDzRnHXSkqasKJduvLihZVjuGXd5O/INrDkybtK5nzzJ5juhaUurYR8af81lVsPVvdf07UB+QdziUd2FNekk99p8V5hqWtl4GYe3VC5jv8QysfxyxsLUg3KvzXdZQc2RJZd1SYKyrKubFdiU5SMduTrqCk7tsYwuNxVS1+Y1Ow/yhMljLiPrnYrgmEbyJJtvc2p1CxiPrJB2+kb1Uqa76coO5B9Ws3PWoHVPmzLkgbD4Gns+rXF5l5ogLacZf1GQzTJWmYvkSTbfUSUnLMMVZhON6GHbosMTe8abpF2gmu0+PkS5o8PpenfvzF+W7roRgy396PEOATTRu7CevLWJ3mwuo5/Wb6GSUjMNueISiolS/tC5wOIw6470INp4JpjaRpXR6JYcWJVdiXS649lFS4hbFWZO/Re1aZWkTCSNSSn0o315YZQYZCJXAjwXvjYlmNaeDqXm0rUBU7soj8I4TW5pBd3/znYqzM+lu6/rT8M61Zxu4Olyt14qa41UFl9ncG8tZRWYKmjW5nAqpwAa2iiLxnaybd2Qy3p3fTJtOF5G3NT/xi3t5NE234UlZTTzf8fJVOFH13UJpesvVgYZzrD8j0xatmobNW7SmF6+tYdNGfKAuaycHnr+Q0pNO0szbh9HV/RqL0+y+X5q2W76bccecU552WMjKDi4+jSCdaLS0kqavqqMKgPCqFfkWZp2GTcHKztups+I6kHmnBT65J5eFBpac+Qvpy4urqCpK4vJRwp8zQNzFM3zcrqP/6qkzVkNKJDM9O9+BRQepDx2KSw0000vryXfoHCKCzDRuzOHyNltvl/75Af6esOfFBzoT/978V4KDwu1mS4P3PHKWiqsDKLBbUNpyijHwfvHHSfpve+2SX9jEfTfaVdRaLDj9T1q9qdUaiqlrq0T6KV7R8izsPn+bncmrTzdkAIqpesyrgqg8BDHZc3/dCulFPjSiC6RdO1lHW3KkAeSC/ylU0ph0vZVSRPbFTi8JobTvrrkE9p74AjdOGY4Dbr8Ejm7qm800aticykTArwLfN4a4HmRIyIiKCgoiDIzlecqXSDRLas324eFSRc3SZ/0dMdH5rohqpyRN9tz3WNiYig1NVXl0ns2G//Nms1mm6N5z9ao7nNHgK+7lVYplbu5WpWMciAAAQhAAAIQ8JgAArzH6DFjCEAAAhCAgPsEEODdZ4uSIQABCEAAAh4TQID3GD1mDAEIQAACEHCfAAK8+2xRMgQgAAEIQMBjAgjwHqPHjCEAAQhAAALuE0CAd58tSoYABCAAAQh4TAAB3mP0mDEEIAABCEDAfQII8O6zRckQgAAEIAABjwkgwHuMHjOGAAQgAAEIuE8AAd59tigZAhCAAAQg4DEBBHiP0WPGEIAABCAAAfcJIMC7zxYlQwACEIAABDwmgADvMXrMGAIQgAAEIOA+AQR499miZAhAAAIQgIDHBBDgPUaPGUMAAhCAAATcJ4AA7z5blAwBCEAAAhDwmAACvMfoMWMIQAACEICA+wQQ4N1ni5IhAAEIQAACHhNAgPcYPWYMAQhAAAIQcJ8AArz7bFEyBCAAAQhAwGMCCPAeo8eMIQABCEAAAu4TQIB3ny1KhgAEIAABCHhMAAHeY/SYMQQgAAEIQMB9Agjw7rNFyRCAAAQgAAGPCSDAe4weM4YABCAAAQi4TwAB3n22KBkCEIAABCDgMQEEeI/RY8YQgAAEIAAB9wkgwLvPFiVDAAIQgAAEPCaAAO8xeswYAhCAAAQg4D4Bf/cV7Z6Sg4OD3VOwilL9/f3JSPVxZhECAgLIz8/Pa+sPe2fWtrZpvdk+MDCQfH19vXa7579ZHx8fbVeoTqXxdsN199bfTJ2YNJ2N1wV4k8mkKYArhfGGaqT6OLMsHOD5h85b6+/N9vwjzYEG9s5ssdqlrays9Fr7oKAgMpvNVFVVpR2ITiXxbw7Xu7bt3lt3XHTiUzUbNNGrYkMmCEAAAhCAgLEFEOCNvX5QOwhAAAIQgIAqAQR4VWzIBAEIQAACEDC2AAK8sdcPagcBCEAAAhBQJYAAr4oNmSAAAQhAAALGFkCAN/b6Qe0gAAEIQAACqgQQ4FWxIRMEIAABCEDA2AII8MZeP6gdBCAAAQhAQJWA1z3oRtVSSplKpYdDPLXwDdqyfTf17NqRnpk1gyLCw9QWV+/zmUrLaPikhyk3/TQFhkbQkleeoW5tmyqWe9eev+mZl96i/IIimnbHBBo7cogiTV1HPLp0L+XEX0Y+vv5kOvUHfTSpuSKryVxO1z3+PmX+vZVCE1rS0w/eQVd2VdbrsZeX0sZff6MKUyHFtehEP7zxkKKsiooKGjxmMuUVFIqH/ry84DEa0LeXIt2ipetpl19vCgiLJfP+lfTJI8MUaXjEjW8foLCkHlSac4ZGNUqjCQM7KtIdOHyMnn5xMWVk5dCk8aNp0oTRijQ8os/Vk6i8tJikR3/RrZNuogduG6tIN+axd+nkzrVUVVFOIfEtacuyFxRpeMRV199JWdm50jL60PzHptOIoVco0t34yKt08sQJKi/KpZCE1vTLB3MUaXhEjyvHWcb37d2V3nzxScuw3PPIoqX083fLpcEqCoxoQNtWvC1PsvkePmEqpaVniqebzX3wbofbzv2vr6Ki9jeQb0AQFSbvoc8mNbIpQx7oM2IylRfnSYM+1P2SAfTBwhnyJMv3k59up3U/fEsmaf3Edh5Iq1+cZJlm3dNryESqLDeLURddPIA+ev4+68mi/5utx+ilNz4gU/ZpatR1IH230HFZV157O+XmF4g8E68fQY9Mv11R1vc7kunzI1EUGJ1Apaf30CeT2yrS8IhhN8+k9DOnpD4fuuWWm+mhydcp0q3btI0emfcf4of5REdF0PpvPlCkqeuIsxlZNOfZ/9LhY8k0fPBl9Nh9kx0+RW/+Bz/T98uXUVBYFM19eBoN7d2qrrNAOjcL/GOO4L9csYZ+Wr+ZCgqLaOOWHfTx/75zM613F//Ii/8ngjsvhbm4gB6e94rDBeKdpuPJpykrJ5eeffkdSs/MdpiuLiNz4i8nX/9AKcBLjxJt3os+3nhUke3RdzfS2V2rpcBdQAUn/6Ln3l2hSMMjNv66jUqlH1/+0U/dv5Xe/WqdIt2jT/9HBHeewD+Is555WZGGR/zh00PamWhNAeExFNrnFvps1e+KdNOXbKGoDgPIPySCwhq3p2+OO955XLBoCR04fFwE3Jff+oiOnUxRlDV2yrzq4M5TpCd/ffTJF4o0PCJ513opuJdxIipJP0bXz3pfkW7BorfFvHhCZWUVzZd2xhx1yafOkCkzmcpL8qngxC4a+4DSYuj1d9lk3brjL5theeDn77+RequftGYuyKI75irnycvOwZ07frrZc6+8K/rt/ytoNZr8AkOkwOJLEUnd6JYXVtknoTHTnzsX3HlSFe3+fZMiDY/YuH4dFZ05QBXSMmbsWEG3LlJuE+Pvf8ES3DnPnt9/4S9F9+qHy6no9N+irJRtK+ipj7cq0vAyysGdJ3721feKNGL8fj8KbdRW2nYiKaxNf3pq2V5Funf+t/pccOdJVfTJp58q0vCIOf9+VWzL3J+bV0CPzV/Evaq619/9jHb+uV/aeS+kZd+sog2btyvK2XM8g77++G0yF+ZQwdkT9Ox/FivSYITnBP4xAT43L99G2X7YZiIGKDsnx0bBVFJkMywPWP+AcZDkHSi1nY/0CFe548dWnswslQct37lSS4F1V2ayHZanVZTajj9xujqYyNP5OyMr13qQzGXlNsPygH9IlNwrjmBSMgotw3JPYZntn5KvFJQcdfbbXd65ozvrtPkFtttqVWWF9WRLPx+5W3f5eXwEa9vJQVQeWy61WjjqKs0lNqPzi5WPhC4otjW1yWA9IG0H1l16Zpb1oOhPPZthM67CLo88kY/crTuTT6j1oOjPy7X1oirb+csZuDXHuiswKbevrCzbelmnt+4vK7EtKytPaZOckmqdpdZ+nwDbbSXHpKx/8pmztvlreVRtud02nGm3jdsWcv4h+201x+43lHNn5kmtTFZ1MRUpt8HzzwVT3Slg+6vkzjl5uOyxI6+iuIaxohbRkRE0YcxwD9fI2LOffe/N0pF0TcCddPMEhxW+e9I4y/ghV/Sl1i2aWYad7TEl7xZHc5yPj/zmXt9JUcRjN/Wn4AbV8/CVfhiHDFY2N3OmhJYdRfM294c16UhP3TOWe226eY9Osxm+7ppBNsPygM/hVVLMqA6MeUd+o/vGXyZPsnw/PLwVlUhHwNxVlJkowXTEMs26565/jROnA3jcpb27UbfO7a0ni/6X5trWq2VbpQMnDGtSM943MJTefeIORVlPPDTVpll16MB+ijQ8IjgmwbK+QxLa0DvzlE3Jrz9v22wfEmwbfOWCG7boIPdK68CX3p43pWb4XN/j990pThnIEwb0U54a4Wm5f0n25wJIhXTK4okxreQslu93/i01x1u9gCUsrrllmnVPo87S6R+/ADEqtFE7mj/xYuvJov/Dl2fZjAuOrP7NsBkpDfQdMEgqK1CMDk1sQ7MnXmqfhJ553LZpv2XzJoo0PKK17xmqLKve2SjJPEkPDW+hSDfnngmW9cMTW7WvWffWiUcOG2gZ5FfSPPnwVMuwsz233nit9GKY6nXcKqkpOdp2rujanJp36VtdtLQORo+70dnZIL0bBXykP57qtjQ3zkSrorOysqigoPp8lpoyS0pMdOR4MrWUNtbwsFA1Rdjk8eYXnkRERBC/uCIzU3lkKy9kRk4BfbNuJ13RqwO1a5Eoj1Z8n049S4VFJdS+TQvFNGdHvPfzYUrJMdOsaztIL2Sp2cGwLidDOmpY+ssRuqxjIvVsE289yab/w2830PGUTJp793UUEOD4cpPsnDxa+vUPdPmlPalrJ2WwlQv87pe/6OiZbJo+7nLy93dcr7NZBfTWT/vp8o7xNKhHSzmr4ju/sJhKpKPHuNgoS7C3T8RH3vNe/ZQG9+1JN4y43H6yZfiB/35LR0+l0TuzbqLEBpGW8dY9BYWF9MkXK6lP9y7Uu0cX60k2/bNf/oz+Pp5Gr826nZo2irGZJg+kpKXR9Mf+TZdf0pMemnabPFrxvfh/a2nH3iP0wsxbqEFMmGI6jygsKaFP/rdC2snpQH2lnZ3aupeWbqI9p4vomVsvpuZxjgNu6tk8unfBW9Szc2uaO7Vmp9O+zPfXHqKdRzJp9oSu1CQ63H6yGE7LKKDp89+gbl3a0xNTxjhMwyO37E+l7YfP0m2DO1BUuOO3XPKLVf7zxv/RRZ3b0eirr6y1rN8OptP2o9J1GQOSai+r1EyLP/uR2rZsSiMHOt4h4hns2nNAutZoF91y/UiKks7Du9LxUftpqfWgXesW0t9j9c6RfXl86mfjX6coISaUOiU1tJ9sGeaXzSQkJFBKivK0FCfiVrukpCRLevS4LvCPCvCuc9mWUN8DvO3SGmvIm+3DwsKIP+np6cZCrWNtvNme6x4TE0Opqal1XFpjJfP2t8khwOu7PfnqOzvMDQIQgAAEIAABPQQQ4PVQxjwgAAEIQAACOgsgwOsMjtlBAAIQgAAE9BBAgNdDGfOAAAQgAAEI6CyAAK8zOGYHAQhAAAIQ0EMAAV4PZcwDAhCAAAQgoLMAArzO4JgdBCAAAQhAQA8BBHg9lDEPCEAAAhCAgM4CCPA6g2N2EIAABCAAAT0EEOD1UMY8IAABCEAAAjoLIMDrDI7ZQQACEIAABPQQQIDXQxnzgAAEIAABCOgsgACvMzhmBwEIQAACENBDAAFeD2XMAwIQgAAEIKCzAAK8zuCYHQQgAAEIQEAPAQR4PZQxDwhAAAIQgIDOAgjwOoNjdhCAAAQgoL3AunXraMeOHdoX7MUlIsB78cpD1SEAAQhAoFpg8ODBlJycXCcOs9lsSWcymSz9co/1dHkcf5eWlloPGr4fAd7wqwgVhAAEIACB8wncc889YvL48ePpxx9/pMDAQNq6dasY17ZtW3rttdfo4MGDYvzMmTMpMTGRBg4cSGPHjqWIiAgaNmwYVVZWUnZ2Ng0aNIhCQ0Opf//+tGrVKlFG+/bt6fLLLxfjd+3adb6qGGoaAryhVgcqAwEIQAACzgq89NJLIsv7779P3bp1o7KyMqqqqhLjuL+iokIMc3/37t3pm2++oY0bN9I111xD3333Ha1evZpSUlLokUceofT0dDp+/LgI9FOnThWBv7y8nAICAoiDO+f3lg4B3lvWFOoJAQhAAAIOBcLCwsT48PBw8vf3F/1ygLdvVu/VqxclJCSINH369KGWLVuK/qKiIhHAhwwZQs2aNaObb76ZTp48Sfv37xfT+RRA165dycfHRwx7w38I8N6wllBHCEAAAhA4r4Cfnx8VFxdTcHCwCMKnT58m/mRmZtrk4x0AOUjzUbl1xwF87dq1lJaWRsuWLaMWLVpQ586dRRIu19s6BHhvW2OoLwQgAAEIKAR69+5N//rXv+j333+nCRMmiCPwcePGUVJSkiJtbSOef/55ioyMpCZNmtD3339Pb775pmVnoLY8Rh5f3ZZh5BqibhCAAAQgAIELCGzZsoUKCwtFgL7qqqtoyZIl4gI662xysz2Pq63/119/FS0BfKGd3B09elTu9apvHMF71epCZSEAAQhAwJGAr6+vCO7yNL46Xm1nHdzVlmGEfAjwRlgLqAMEIAABCEBAYwEEeI1BURwEIAABCNQvgdmzZ4tz8vZLxQ/EmTJlCg0YMIDmzZtnmfzKK6+I2+wmTpxIBQUFlvF69+AcvN7imB8EIAABCGgq8O7OMjqWXelSmVe09KdhbfxsyuBz+tOmTaOff/6Z+PY6+27RokXUsWNHev3112nSpEm0Zs0a4lv1Nm/eLO6tf+utt2jhwoX07LPP2mfVZdilAM83/+fm5lJ0dLTl3kNdao2ZQAACEIAABM4JHMqspD9TK1zyaBPLDdq2AZ6fbMdH4c2bN3dY9qZNm+idd94RD8G54YYbaP369RQTE0NjxowRMZHzjhw50mFePUaqCvAnTpygBQsW0IoVK2jGjBl09uxZatq0KT3++ON61BnzgAAEIAABCFgE2jXwofiw6uCcWVRFu1LrdjR/RUs/CjwX0xuEKh9gw4GdP3xlvaOOn34XGxsrJnFg53vu+YE5F110kRjHB79ZWVmOsuoyTlWAv+OOO4hvQ5g/fz5lZGTQo48+KvZS+Lm+7dq106XimAkEIAABCECABdQewW88Vm4BjA1x/pK0qKgocWsePwSHA3t8fDzx1fzctM8dP3gnLi7OMg+9e5xeIr53cN++feKZvfKTfXgPhx8swK/rQwcBCEAAAhDQU6CqspyqKsyufaQynO169OhB27ZtE9n45Tb8JDxH45wtV6v0Tgd4fsQfX0SwZ88eSx34LTwrV66kRo0aWcahBwIQgAAEIKCHQFVFOVVWlLn0qaqqW7M+P5+en5rHHb+chl9wwy+t4bfV8Xn40aNHi6N5/p4zZw49+eSTehA4nIeqJvoXXnhB3BbAD+nnZ/m+/fbb1KlTJ49eTOBw6TASAhCAAAT+AQLSm+POvT1O9cKeJz9fcyZ3/OjbHTt2iEG+9uyrr76ikpISCgkJEeP4mfj8iFvrcXJevb9VBXjeW9m5c6e4L5Bfv8fn3vmdu+ggAAEIQAACugtwcK7jEXitdTtPgK81z7kJcnC3TudonPV0PfpVBfgOHTqI+/0efPBBPeqIeUAAAhCAAARqFaiqlN73LjXTu9S5uoPg0szdk1lVgOdmCT4Hjyvm3bNSUCoEIAABCNRdoLKiQjr/7lqAr5KuJatvnaoAn5CQQPwaPr783/oWAH6qz7Bhw+qbEZYHAhCAAAQMLSAFZ5ePwKVm/nrWqQrwc+fOFfe+21vgiN5eBMMQgAAEIOB2AXGNnWsB2vr1sW6vr04z8JMekD/P2Xnx7XCNGzcmflRtYGCguPevWbNmlqsI5fL4Ifv8jF6+P5A/kZGR4kEA8nTOv2zZMlq7di3xIwEvdKEeX5XID/c3Sufv7y8MjFIfZ+qRnplDZzOyKSwkyJlsDtOeScug1LMZ1CA22uF0HllYWEyr1v1K4WGhFBkRXms6LudCZXFmrez5ItEDR45ToHQ3SHCwaxY50mObX3rjA6luftS8Se23jGbn5hObhQQHEt926qgrNZfThj+TKcDPl6LCaq/Xlt930eoNW6hLhzbEV+866kzS38xPa38lP18/io2JcpREjOP18+WKn6h3987C11HCXOkBHi+99oH4O2yZ1NRREjFu+co19P6nX9MlPbtJroEO05nNZfTT+s3ivdzn23aOHDtJv+38i5KaNq51GTOzCuieOS9SenYe9b6ovcP58cjPV26kV977gnp0bUNh0sNJHHWFRcX00LOLKTMnn7p1bO0oiRiXnZNHx5NPS48mjRIPN3GU0GQyiW0iv7CI2rZKcpREjCsw+1BqsT9FBFSSr+NNgkylZlr81a+Uk1tArZvF11pWTl4+HTt5imKiI6V17vhOaA5mh46eoIqKSvE3WVthuXkFdJTLioqo1b62vPbjefvkW6zz8/PtJ4lh/lvgJ7+p6VbtzaHUXFP1UTwfyav4dG0SQr2SwtTM3rB5fKQV7fRuz+HDh0UTPT/wJiwsTAQ5bp7nt+pYd3///TfxZ9SoUWI0r2B+yo/crV69Wtxmx2/i+eijj6hv377EF/DV1vEj/zz5Zh77evGDfvgP2Nu6j5atoJff+khUe/hVl9O/59yvehE+/fJ78QPGBQy9sh89/6TywstTp1Np7G0PSNtJ9bOi5z44ha4fNUQxz2Vf/0jPv/a++MEfPOASeunpRxRp5BFa2PMP+a3T59CxE6ekYBtMrz73uBTcusizcOqbfywn3PmwJc9ll/Sg1xbOsQzLPStWraf5Ly0WP6x9+3Sj16U01n8TnC6/qJRG3/0U5Z05Qr7+gfTIrDl046DOchGW77sfnEfbd+0Vw/y3tem7j6SdbNudgdz8Aho+fqoUHEpFuim3jqept423lCH3jJ/8IB0+liwP0vrvPqRo6cfYuktLz6ThE6ZaRnXv0p4+eE35Eo0RE6fRmdR0S7rVX7xFcQ0bWoa5p7i4hIaMu4uKS6r/fm678Vq6f8q/bNLwwH/f/pg+/PxbMT40JJjWfPkOhYZW344kJ045c5ZG3XyvPEiR0bG08esllmG5Z9Tk2ZRy7NC5QR/63wevUNsWTeTJ4js9N4+GjZlsGRcbn0hrl71uGZZ7Nv32Bz385ItklnYQO7VrTe+9Op+Cg2ztCwtL6PJRNcvEO0TLP3xFLsLyvT87kN7cF0VllT7ULLyMHumeQ0F2+2oc3G/75AxFtblYXExWsesTeu+hayxlyD3bpB2hB+YsFDsDvEPx4esLKPTc7VtyGv7Jv2/2c/SrtAy87c158G4aO+IqebLle8fufXTfrOeoRPqNa9WiGf3f68+ed2fAkrGWHr6lmk/v8uNdHXUc4PkWNDXdA0uP0a7kQjVZLXluvjSe7r4i0TJcH3pUNdHffffdImjzk+t4j+u3334TN/hffvnl4n54GYZXJB9pbdiwgTp37iyO+uVp/H306FHxIH/+ceKn/xw6dMgmwC9fvly8zEbOw08J4jf3GKXjeldIF3d4U8d/3Es++sJS5R9/3kSzZk6hVknNLOOc6Xnn45qyVq/fQo8/MIXatW5hU8Tc516zBHee8O4nX9HUOybapOGBJR9/KYI796/9ZRtlZOdTp/aOj6C0sP/5l+9FcOf58Y/Y0uWraPiQK3nQ6e7622fa5NksHVlbX58iT3z/s69FcOfhrdv/pOTT6dSnZ/Vzq+U07/6wRgR3Hq4sN9P/ff4NzZgwUJ5s+ZaDO4/g7fBtab0+O9e2Hm+897kluHM63iF74pGaYMjjuLMO7jw8//nF9PFbL3Cvpbv9vrmWfu7Zvfegw2W0Du6c7v45L9Ca5R9wr6V74dV3LcGdRy77ZhUtmKvcOeTxcsc7A8u+XU2P3nenPEp8D7/Fdgc1PzfbYb1Sjh22yldFM59+nbatfNdqHNGEafNshrPT0xyW9ckXK0Vw58T7Dx2VLA7RqKsH2eSdtcDW6/jJFIdlrdtPIrhz5lOFAXTUHEdXNLcpih599SspuI8TI338pJbDVkMdlvXZ8h9EcOeEh6WWj9937acbrr3aprA//tovgjuP5IeUvffJcppy2002aXjgc2mHm/8uuOOd4K079tDEcSPFsJr/OIDzDoWjvwsuT8WxpqUaVST9c/5Y1ZLf1fnbFGSgAVUBfv/+/fTTTz+J5nlelv79+9Ptt99O/GYdfuCN3HFzOq9Ubs5funSpeMKPdTM8v4kuNDRUJOdvfpavddeqVSubcREREeLhAdZpPNnPe6TcxOttXVRkBBVJR1Dc+UlNwAHSjgqf/lDTRUnrJC+/es+Z/3gDA/wVZcVERdoUzUdgjuYXLdWLmz254+0mKFBZllyQFvZhobZNtBERYQ7rJc/zfN+x0VF0jE5Zkvj6+Dosy/70BDdf21tEh9seoYbVst2zN/9Ay11iQgNFWfZN8nxawH5+cn7r70aJDRXp4hrEWCcR66guZSUmxCnKamh3OidIOvp1VJYYb6pufeCZcz77dA1iG1Da6dM2dbNPIyZKXtKl1pZ03LRun65RYhwd+tuSRPTYp+GRkdK2Yt1x64J9uiaNEqyTiH77NDwyxI9PYdT8FAdVlUpl1axXTtM4NoyOcM+5rrwkT0qjbM6OtGt1cVSvkCDbUyZRkeGKuvNswsNtl5H/XhzVX67Thb55p7y29Szn5SZ8VR0Hd1xkp6Cr2aoUk2of0aVLF9q9ezddfPHFlkS//PILPfDAA5Zh7hkxYoRlmPeu+Ok/1gGeHwRQKjUd8o817wzYr9zu3btb8nMPmuhtOFQPPP3YNFqwaIkI8tNuv1FqWgywvBzB2UKfevQeqcn5LSqQzjHec9sEcU5fftGCXNaMOyfSpt920slTZygiPJSenXOfw/k98fBUevqFNymvoJC4KTlSSmtfllymFk30l/a6iMZfO4x+kFoxuDlz6q031Do/eb61fS9+cS71v+ZflqO6R++b7LCsWfffSfOkZczIyqFbJ4ymxLhYRbpxA9rT6qvG0K5f11BUQnN6avpNijRcD3Z99Z1PpCMXomZNEunG64Yr0t00djitXL2B9h88Kp2GCKIFsx3bT5LqwqduuIuQftgfufd2RVkvPPUQ9fvtFstRHW87jtbP2JFX0fKVP4uyAqQdvkXzH1GkGzXsCvpq5Wr6U2oF4Kbt+Y9PV6ThAnj8o/P+I1ohukmnBDif/Tw/efVJ6jl4gvT7Xh28+/fvp0jDZY297lpavvxrqa+K/IPD6eNFjyvSvfLkvXTxlt+orLR6h3fwkKsUabis++66mc5KpyxOSNv0mGsGU9dObRXppk++UVzTkCedJuHupjHK9cPjr23uS5nFkZQmnYPvm2Ci1qGFUlk8paa7eVgv+uHFTymo8wgqK8yiPr57pDQNahKc67vn9gl06kwqHT1+ikYOvYL6dO+kqFfD2Ch6aNqt4hoJ3smZPfNuRRoujv8ekqXl45aAa6RTeX17d3WYTlGJWkbw7zwfpNmvPzk579Q3tDuVI0+70Deve9wmp1RSdQ7+yy+/pMmTJ9PAgQPFq/T4aJ6b6vkl97wS5Y6b2Hv16iXOq/CFdNxcf8UVV8iTicvhc+68w/Ddd99RkyZNqGfPnpbp9j0I8PYi6of5D433pvn1ht7YaRHgPbXcfN0Kf9LT0z1VBZfm6832XHd+rWdqaqpLBp7KzH+zfDDkanO0J+rPscFd5+BnfPQ3/XGiuvVP7bLd0r8J3TNY3alKtfN0dz6pzcr5ju+B54Ddp08f0UzP74Hn9+VaB3culafzS2j4WfV79+4Vwzz+mWeeEXtxgwcPFufvFy9eLN4pz+fh0UEAAhCAAAScEeAXxbj64Zad+tapaqLn2xz46JwDNZ9X4avn+SjcusmeofjWuXvvvVdcac57znL3xBNPyL105513ij1Svt0OHQQgAAEIQMB5AQ3Owbt8Dt/5Wrs7h6ojeL6KPi8vz3KBz6BBg8T74PledkeddXB3NB3B3ZEKxkEAAhCAQF0EXD16F/kdHMHz6RA+gOVbuefNm6eoCl9cfumll1o+Tz31lEjDr43l2775wwfCnupUHcHzC+6PHTtmeUjHhAkTiK+sX7FiBd12222eWhbMFwIQgAAE/okC0kPTpFuaXFty6aE/9h0/34VvzeYHtk2aNEm8ZG3IkJpneCxZskQc6PI1EfyWVX5Ue3FxMSUnJxNfeM4dt3J7qlN1BM9Xu2/fvt2mzhs3bhRPqrMZiQEIQAACEICAmwUaRQfRRc0jxCepYUidz8d3bBJuyRcepAzEfOv3+PHjxfVlfFS+fv16myXh6874wsf3339fHMX369dPvIgtMTGR+NoyjoueDPCqjuAXLFhAV111lbjinc+z84NseC9n9OjRNguPAQhAAAIQgIC7BYpLyyi7sPp5CSXSY56lBx7UaZY50lMj/c49G7jMwUPL+GFtsbGxoiy++8LRXUf8kKlXXnmFuGWbu4yMDHGXDF+X9vHHH4vHtMtN9yKBjv+pCvDXXnutuKed92744TR8jqFFixY6VhuzggAEIAABCFQL5BaZ6VSG3cMD6oBzJqvm4Womc81DkOSsUVFR4o4vvo6MY118fLw8yfL9ww8/iHP0vAPA3ciRI8WH+/lOMj4P76kA73QTPZ9r4JfE8Jvj+JzERRddJF4UwwuDDgIQgAAEIKC3AD/kprK8zKWP/JFqNUUAACjpSURBVKAk67rzrdvykTm/MI0fl27fcYAfO3asZTS/QI3P2XN35MgRm6e7WhLp1ONUgP/iiy/EY2d37dolLizgJvlbb72VxowZQ3xlPToIQAACEICA7gKV0rPopcc2u/IRj4S0q/gjjzwizq/zBXQHDx4U71w5efIk9e7d25KSX6jGzfFyx0fwa9asEWn5KvvZs2fLk3T/rnMT/dmzZ8U96++99554Oh3fB//XX3/RgQMHxBPqeAH5wjtukkAHAQhAAAIQ0E+A74OXPi51yvxNmzalr776SjyDnx+tzh2/8Y4fuy53fA2adcdPqfz2229F077949et0+nRX+cj+J07dxI/G56fYscvufjxxx/FHgo/8pQXnJ9KZ73QelQe84AABCAAAQiIF83wg2pc+igDvCwrB3d5uC7fng7uXMc6H8HzW4SsK8yPquXbAOSO3wwXGWn71jB5Gr4hAAEIQAAC7hKoqCyXXplsdqn4yirlRXYuFWiAzHU+gueXxmzZskU8U56bLDig81E7d3xO4ueffxbvdDfAMqEKEIAABCDwTxJw6chdPvKv/QjeWynrfATPt8Hxo/r4sXzcffjhh+JFM59//jlNmzaN7rnnHo9eLeitKwD1hgAEIAABFwWk2OzyG/ZcPofv4jK4IXudAzzP+/777xfP5eVz8PLz4zt37iye1sO3y6GDAAQgAAEI6C7AR/DSVfQudf/0AM949i+OQWB3aZNCZghAAAIQcFGgSroPvqrCtWfR8wtn6ltX53Pw1gvO736/8sorxeNp27dvT/Jn1apV1snQDwEIQAACEHC/AB998xG8Kx8cwVevJ35j3OTJk2ngwIHiHnh57bVp00buxTcEIAABCEBAFwHpMTfinysz4xLqW+fUOXheeL6QIT8/nx5//HHL62LrGwqWBwIQgAAEvEiAj75dPQKvf/GdnG6i9/HxEe+85dfjmc2u3XfoRZsPqgoBCEAAAgYVqJLuYa+U7oV35cNl1LfO6QDPAAUFBeKxtQ0aNKAOHTpYPjgHX982DywPBCAAAeMLVFVIz6GXXtvqysflFgADMjndRM/L8PTTT9OsWbMoNTVVvFmO3wnPt87hHLwB1zCqBAEIQKDeC2jRRF//2uhVHcGHhoaKt8fxc+n5YrtBgwbRH3/8QfL7cOv9toQFhAAEIAABwwjwtWF8m5trH2WA59PQU6ZMEe975we92XcZGRnife/8znf+fP/99yLJK6+8IuLixIkTRYu3fT69hlUFeH417KhRo4jfMJednU2rV68WR/X79+/Xq96YDwQgAAEIQKBaQNweJ51Dr3Th4+AivUWLFonbwfndK/y6WH4NrHXHr07v168f8Rvl+DN8+HDi98Zv3rxZxEWetnDhQussuvarCvAcyJ988knic/B+fn7Uv39/4vfebtq0SdfKY2YQgAAEIACBKpLOv1dJD7tx4UNSGfYdx7Tx48dTQECAeHvq+vXrbZJwgA8KCqL//ve/dPjwYXGqmp8TM2bMGHELOR/B2+exKcDNA6oCPL/7fffu3TZV++WXX6hhw4Y24zAAAQhAAAIQcLdAl1YJdN2AzuJzaZfmdW6qH963gyVffEy4opopKSkUGxsrxvMp6MzMTJs0xcXF4nZxfqLrXXfdJR7bbp0nOjqasrKybPLoOaDqIjt+scyQIUPEg26aN29OP/30E/GCjB49Ws+6Y14QgAAEIAAB2nvkDG3bd9JpiR8277PkaRitDPBRUVFUWFgoHtFeVFRE8fHxlvTcwxecy12FdBX/Z599RgkJCSIPj+cdgLi4ODmJ7t+qjuD54jo+J9GnTx/x0hl+6A03S3AzBjoIQAACEICAngJV0rn36ufR8zPp1X3EY27tKt2jRw/atm2bGMvn1rt27WqT4sEHH6Tt27eLcXyOno/kL5THpgA3D6g6guc6cXP83Llzic/Hr1y5kg4dOkTcdI8OAhCAAAQgoKeACPDSg25c6Ry9bOaRRx4Rb1F94403iO8eW7BgAZ08eZKuv/562rFjB/3rX/8St4zzeXh+wuvy5cspMjJSXIzHLdppaWm0YsUKV6rlUl5VAf7hhx8WC8dN83yL3CWXXEIMwEfxfE88OghAAAIQgICuAg6ugndu/srb5Jo2bUpfffUVlZSUUEhIiCguKSlJxD8e4KN1fsAbP/wtIiLCMrs333zTJo9lgs49qproly1bRt99951opucLD7799lu6+eab6eeff9a5+pgdBCAAAQhAgIOzq5/aFeXgXlsK6+Aup7lQHjmdO7+dPoLnBwqUl5dTeHi4aHrgpgru+IEAjhbSnZVH2RCAAAQgAAH5UbUuSfC99PWsczrA88tmLr30UvGgG26S5xv633vvPfroo49ozpw59YwHiwMBCEAAAkYX4HPwldJV7K50VfUwwKtqov/000+Jr6TnJvnOnTtT48aN8ahaV7Ys5IUABCAAARcF3NdE72LFPJbd6SN4rilfTXjbbbdZKs2P5+POZDKJ+wUtE9ADAQhAAAIQcLNA9bPolRfJOTNb6Wn2ziT3irSqjuD5HvgBAwaIe/741riOHTuKm/vlB+17xZKjkhCAAAQgUE8EpODMV9G79KknFFaLoeoInp9kN2nSJHEPPAd4vuCOm+35+bvoIAABCEAAAnoKVEn3wFdVlrk2yyrXzuG7NnP35Hb6CJ6bQvLy8sQFdcOGDRNXz9933300ePBgcT+ge6qJUiEAAQhAAAK1CHDreqX0nysfLqOedU4HeL6KPiwsTAT5bt26iYfbsAk/kD85Obme8WBxIAABCEDA8ALSgad8Hl71dz08B6+qiX7q1KnimbzHjh2jEydO0IQJE2jdunX022+/GX47QAUhAAEIQKC+CfA97K7ex17/DuGdPoLnzeLRRx8Vz5/39/cXT7PjB+zzk+1at25d37YaLA8EIOAFAmfS0um9T5fTj2s3iSM5L6gyqqihAN8DX1le5tKH76Wvb52qI3hG4Lfq8BPtuLme3ybHwR4dBCAAAb0FcvLyaeKURykvv1DM+tDRk3T/3bfoXQ3Mz6MCOIJ3xK/qCJ6b5e+8807xgJvFixfTAw88QAsXLnRUPsZBAAIQcKvA7j0HLMGdZ7Rxc/XrO906UxRuLAGXbo+TmuZFfmMtkha1URXg77jjDmrVqhXNnz9f1IGb7PlF9/zKWHQQgAAE9BRo36YFBQTUtCB26dhWz9ljXgYQqI7vrl9oZ78o/I6VKVOmiOe+zJs3z34ypaenE982PnLkSJo9e7a4q4wT3XDDDdS3b1/xeeaZZxT59BrhdIDnKxT37dtH/J7c4OBgUc/mzZtbLrTTq+KYDwQgAAEWaJwYT2++MJeuHnQZ3TFxDD1232TA/NME+F3wFdJ98K58HNwHv2jRIvEgN36428GDB8V73q1pefoVV1whrknj8V988QUVFxeLO8o2bNhA/Jk1a5Z1Fl37a3Z76zhbvk2OH2yzZ88eS45K6SH9K1euFOfiLSPRAwEIQEAngd7duxB/0P0zBRIbRlOnts3FwhcUllDymfQ6QXRo3Yz8/KqPc8NDqg9YrTNu2rSJ3nnnHamFKEAcla9fv56GDBliSXL//fdTXFycGObXw54+fVrExsTEROLT1506dbJJb8moU4/TAZ7r9cILL4gmi5YtW4oFf/vtt8WCcDMFOghAAAIQgICeAnyQWVlRfZtcpbgavm63vFVIV9/7nKsot07bdykpKeIZLzw+JiaGMjMzbZI0atRIDPOR+pdffkm//PIL8U4BX3zOT3n9+OOPaevWrfTUU0/Z5NNrQFWA53fAc+X52fNlZWU0duxYatsW5730WmmYDwQgAAEI1AikZ+bQ34dP1IyoY9/hY6csKQf17Wbpl3uioqKosLBQnI4uKiqi+Ph4eZLle9WqVfTEE0/QDz/8QJyeD3Tlg90+ffqI8/CGD/Dbt2+nyZMdn9vi59Bz99JLL9HQoUMtC44eCEAAAhCAgLsFKqTz5xVV0nl4FzqpDUCRu0ePHrRt2zYaMWKEOBLnp7dad2vWrBF3kK1evVoc4fO0ZcuWUUZGBk2fPp2OHDkiWret8+jZX+cj+Hbt2olzCuerXIcOHc43GdMgAAEIQAAC2gtUScFZaqZ3qXPQRM8Xk/N59jfeeEO8Jn3BggV08uRJ4lbsHTt2iGmlpaU0aNAgMWt+quuMGTNo4sSJtHHjRnFh3ocffuhStVzJXOcAz00P/fv3d2VeyAsBCEAAAhDQXoBPnzsI0E7NSHkKnpo2bUpfffUVlZSUEF9Ex11SUpII7ty/f/9+/lJ03377rWja5wvSPdnVOcBbV7Jfv36UmppqPYp8fX0pOjqahg8fLu6P52F0EIAABCAAAfcLSPfAu/yyGAcR/lzF5eDuzHJ4OrhzXVVFYT6S5+fPv/rqq/T555/TrbfeKt1q4EfPPfcc7dq1i/79738744C0EIAABCAAAfUCfAV9ufQseVc+rjbxq6+923KqOoLnoL53715xxSDX7JJLLqGdO3eKJozXX39dBPy5c+e6pdK8I2GUjp8JYKT6OOPCLSzeXH9vrjvsndlStU1bX+wd3dKlrZT2pcmturX9ZvLftPpOtNGrz15Pc6oK8PwEO76hn8/Lc8cb2+HDhykwMFCcq5DHu8PMtY1A2xpxXYxUH2eWTq63/O1MXiOk9WZ79vPm+nt73WV/I2zHztZB/nuVv53N78n08nbjjrpzDHJ1p8fV/J60rW3eqgL8Y489RgMHDhRP6OGb//lWAX7oDTfb820Fc+bMqW1+Lo/nN9gZpeM36BmpPs648AMevLn+3lx38VAOqTnQW7cdb7bnuvMPubfa89Ev/+16YzDiwH4+e9cCP47gHf3+qwrw/Ca57t27E9/7x7cILF26lHr27En8IICffvqJWrRo4WheGAcBCEAAAhDQXIDf5V5V4eLBH99qV886VQGeDXr37i0+1h78eD7+oIMABCAAAQjoJVAltYhxkHelq0KAd4UPeSEAAQhAAALuEEATvSNV1UfwjgrDOAhAAAIQgIBHBFx+0E3t98F7ZHk0mCkCvAaIKAICEIAABDwoIDWvu97EjgDvwTWIWUMAAhCAAASUAtV3puAcvL0MjuDtRTAMAQhAAAJeJiAdfbvcRO9li1yH6qp6VG0dykUSCEAAAhCAgE4C8kV2rn7bVtdsNtOUKVNowIABNG/ePNuJ0lBt01955RXxhjl+q1xBQYEin14jEOD1ksZ8IAABCEDAPQJ89M63ubnycfCymkWLFlHHjh1p7dq14tWv/FA3687R9K1bt9LmzZvFc2L4xWwLFy60zqJrPwK8rtyYGQQgAAEIaC1QfR883wvvwsdBE/+mTZto/PjxFBAQQDfccAOtX7/epuqOpv/66680ZswY8aRQPoK3z2NTgJsHcA7ezcAoHgIQgAAE3CvQo2snatYkUczkTFo6bdz8e51mOHbkUAoKChRpGyXEKfKkpKRQbGysGM+PZc/MzLRJ42g6P9GVH9vOHb9CPSsryyaPngMI8HpqY14QgAAEIKC5wK6/9tOvv+1wutzlK1db8iTEN7T0yz384rTCwkLiF6xx4I6Pj5cniW9H0/mteZyHu+LiYoqLU+44iIk6/IcArwMyZgEBCEAAAu4T6H9JD4qJjnRpBh3btVbk55enbdu2jUaMGEF8br1bt242aRxN5+b8LVu20Lhx40Serl272uTRc8BHersPX3boFR03dXjyikR7JN6rM5lM9qO9YjgiIkJqmgpSNDl5ReWlSnqzvfzOhvT0dG/htqmnN9tz3bmpNTU11WaZvGWA/2b5ym0v+tm20HLgS0hIIG7WdtTx2+SSkpIcTfLYOK7r/fffL16DHhoaSl988QUlJyfT9ddfTzt27BDLYj+d78mfMWOGmJaWlkYrVqygxMTq0wd6LwgCvAvi3vxDhwDvwop3MSsCvIuALmRHgHcBz8Ws3hjg5UUuKSmhkJAQeVDx7Wi6o3GKjG4egavo3QyM4iEAAQhAwLsFzhfceckcTXc0Tm8FBHi9xTE/CEAAAhCAgA4CCPA6IGMWEIAABCAAAb0FEOD1Fsf8IAABCEAAAjoIIMDrgIxZQAACEIAABPQWQIDXWxzzgwAEIAABCOgggACvAzJmAQEIQAACENBbAAFeb3HMDwIQgAAEIKCDAAK8DsiYBQQgAAEIQEBvAQR4vcUxPwhAAAIQgIAOAgjwOiBjFhCAAAQgAAG9BRDg9RbH/CAAAQhAAAI6CCDA64CMWUAAAhCAAAT0FkCA11sc84MABCAAAQjoIIAArwMyZgEBCEAAAhDQWwABXm9xzA8CEIAABCCggwACvA7ImAUEIAABCEBAbwEEeL3FMT8IQAACEICADgII8DogYxYQgAAEIAABvQUQ4PUWx/wgAAEIQAACOgggwOuAjFlAAAIQgAAE9BZAgNdbHPODAAQgAAEI6CCAAK8DMmYBAQhAAAIQ0FsAAV5vccwPAhCAAAQgoIMAArwOyJgFBCAAAQhAQG8BBHi9xTE/CEAAAhCAgA4CCPA6IGMWEIAABCAAAb0FEOD1Fsf8IAABCEAAAjoIIMDrgIxZQAACEIAABPQWQIDXWxzzgwAEIAABCOgggACvAzJmAQEIQAACENBbAAFeb3HMDwIQgAAEIKCDAAK8DsiYBQQgAAEIQEBvAQR4vcUxPwhAAAIQgIAOAgjwOiBjFhCAAAQgAAG9BRDg9RbH/CAAAQhAAAI6CCDA64CMWUAAAhCAAAT0FvB35wwLCgro+++/p9zcXGrcuDFdc8015O9fM0uevmTJEksVhg0bRl26dLEMowcCEIAABCAAAXUCNdFWXf7z5lq3bh21bduWevXqRStWrKBdu3ZRnz59LHlSUlKoZcuWNGrUKDHOz8/PMg09EIAABCAAAQioF3BrgB84cCCFh4eL2gUEBIgjeeuqcoDnI/oNGzZQ586dxVG+9fS9e/dScXGxZVRMTAxFR0dbhj3dw8vkrTslQUFBwj4sLMzTjKrmD3tVbJpk8mZ7rruvry9583bPy1BVVaXJutSzEP6t9PHx8Vp7Pa20mpdbA3xUVJSo5+HDh2n37t30wAMP2NTbbDaLFc7N90uXLqXRo0eLI3450f79+yk7O1sepJ49e1KTJk0sw57u4R+KwMBAT1dD1fx5x8qbf+i82Z5/6PjjrUHGm+257t5uX1lZqepv3tOZOLifL8B7406Lp00vNH8fCdWtu4IcpFeuXEl33333eY+++Wh9z549dNNNN9Va56ysLOLz9kbpgoODyWQyGaU6TtUjIiKC+Cg+MzPTqXxGSezN9hzY+ZOenm4UTqfq4c32XHduCUxNTXVqmY2SmP9m+cDIzT/bbllcbnlISEggbrl11HHwT0pKcjQJ41QKuPUq+gMHDtCaNWto+vTpDoP78uXL6eTJk6LqZ8+eVTTRq1wmZIMABCAAAQj84wXc2kT/5ZdfUnl5Ob366qsCmpvYhw4dSs888wzNnDlTXHDHF99xczEfCd91113/+BUCAAhAAAIQgIAWAm5voq9LJTm4c9PZhTo00V9IqO7T0URfdyutU6KJXmvRupeHJvq6W2mdEk30WoteuDy3NtFfePbVKeoS3OtaFtJBAAIQgAAEIEBkiACPFQEBCEAAAhCAgLYCCPDaeqI0CEAAAhCAgCEEEOANsRpQCQhAAAIQgIC2Agjw2nqiNAhAAAIQgIAhBBDgDbEaUAkIQAACEICAtgII8Np6ojQIQAACEICAIQQQ4A2xGlAJCEAAAhCAgLYCCPDaeqI0CEAAAhCAgCEEEOANsRpQCQhAAAIQgIC2Agjw2nqiNAhAAAIQgIAhBBDgDbEaUAkIQAACEICAtgII8Np6ojQIQAACEICAIQQQ4A2xGlAJCEAAAhCAgLYCCPDaeqI0CEAAAhCAgCEEEOANsRpQCQhAAAIQgIC2Agjw2nqiNAhAAAIQgIAhBBDgDbEaUAkIQAACEICAtgII8Np6ojQIQAACEICAIQQQ4A2xGlAJCEAAAhCAgLYCCPDaeqI0CEAAAhCAgCEEEOANsRpQCQhAAAIQgIC2Agjw2nqiNAhAAAIQgIAhBBDgDbEaUAkIQAACEICAtgII8Np6ojQIQAACEICAIQQQ4A2xGlAJCEAAAhCAgLYCCPDaeqI0CEAAAhCAgCEEEOANsRpQCQhAAAIQgIC2Agjw2nqiNAhAAAIQgIAhBBDgDbEaUAkIQAACEICAtgII8Np6ojQIQAACEICAIQQQ4A2xGlAJCEAAAhCAgLYCCPDaeqI0CEAAAhCAgCEEEOANsRpQCQhAAAIQgIC2Agjw2nqiNAhAAAIQgIAhBBDgDbEaUAkIQAACEICAtgII8Np6ojQIQAACEICAIQQQ4A2xGlAJCEAAAhCAgLYCCPDaeqI0CEAAAhCAgCEEEOANsRpQCQhAAAIQgIC2Agjw2nqiNAhAAAIQgIAhBBDgDbEaUAkIQAACEICAtgII8Np6ojQIQAACEICAIQQQ4A2xGlAJCEAAAhCAgLYCCPDaeqI0CEAAAhCAgCEEEOANsRpQCQhAAAIQgIC2Agjw2nqiNAhAAAIQgIAhBBDgDbEaUAkIQAACEICAtgII8Np6ojQIQAACEICAIQQQ4A2xGlAJCEAAAhCAgLYCCPDaeqI0CEAAAhCAgCEEEOANsRpQCQhAAAIQgIC2Av7aFuf+0oKDg90/kzrOwd/fn4xUnzpWWyQLCAggPz8/r60/7J1Z29qm9Wb7wMBA8vX19drtnv9mfXx8tF2hOpXG2w3X3Vt/M3Vi0nQ2XhfgTSaTpgCuFMYbqpHq48yycIDnHzpvrb832/OPNAca2DuzxWqXtrKy0mvtg4KCyGw2U1VVlXYgOpXEvzlc79q2e2/dcdGJT9Vs0ESvig2ZIAABCEAAAsYWQIA39vpB7SAAAQhAAAKqBBDgVbEhEwQgAAEIQMDYAgjwxl4/qB0EIAABCEBAlQACvCo2ZIIABCAAAQgYWwAB3tjrB7WDAAQgAAEIqBJAgFfFhkwQgAAEIAABYwsgwBt7/aB2EIAABCAAAVUCCPCq2JAJAhCAAAQgYGwBBHhjrx/UDgIQgAAEIKBKAAFeFRsyQQACEIAABIwtgABv7PWD2kEAAhCAAARUCSDAq2JDJghAAAIQgICxBRDgjb1+UDsIQAACEICAKgEEeFVsyAQBCEAAAhAwtgACvLHXD2oHAQhAAAIQUCWAAK+KDZkgAAEIQAACxhZAgDf2+kHtIAABCEAAAqoEEOBVsSETBCAAAQhAwNgCCPDGXj+oHQQgAAEIQECVAAK8KjZkggAEIAABCBhbAAHe2OsHtYMABCAAAQioEkCAV8WGTBCAAAQgAAFjCyDAG3v9oHYQgAAEIAABVQII8KrYkAkCEIAABCBgbAEEeGOvH9QOAhCAAAQgoEoAAV4VGzJBAAIQgAAEjC2AAG/s9YPaQQACEIAABFQJIMCrYkMmCEAAAhCAgLEFEOCNvX5QOwhAAAIQgIAqAQR4VWzIBAEIQAACEDC2AAK8sdcPagcBCEAAAhBQJYAAr4oNmSAAAQhAAALGFkCAN/b6Qe0gAAEIQAACqgQQ4FWxIRMEIAABCEDA2AII8MZeP6gdBCAAAQhAQJUAArwqNmSCAAQgAAEIGFsAAd7Y6we1gwAEIAABCKgSQIBXxYZMEIAABCAAAWMLIMAbe/2gdhCAAAQgAAFVAgjwqtiQCQIQgAAEIGBsAQR4Y68f1A4CEIAABCCgSgABXhUbMkEAAhCAAASMLYAAb+z1g9pBAAIQgAAEVAkgwKtiQyYIQAACEICAsQUQ4I29flA7CEAAAhCAgCoBBHhVbMgEAQhAAAIQMLYAAryx1w9qBwEIQAACEFAlgACvig2ZIAABCEAAAsYWQIA39vpB7SAAAQhAAAKqBBDgVbEhEwQgAAEIQMDYAgjwxl4/qB0EIAABCEBAlYC/qlx1zFReXk5ffPEFnT17ltq1a0fXXHONTc4LTbdJjAEIQAACEIAABOos4NYj+HXr1lFiYiLNmDFDBPkDBw7YVOxC020SYwACEFAlsHvvAXp20RL69MuVVCbtdLvS/bX/ED39wpv08f++o7KyMleKQl4IQMDNAm49gj969ChNnDiR/Pz8qEePHnTo0CHq0KGDZZEuNL20tJQqKyst6SsqKsjHx8cy7OkerouR6uOMh1xv+duZvEZI68327KdX/U+mnKG7Zs4jbi3jLiMrh2ZOnST6nf3vdOpZqaynyGyuDuzpmVn08L23O1uMR9PL27v87dHKqJi5XtuNiqpdMItsLn/bZ6htvH06DNddwK0BPjc3l0JDQ0Vt+LuoqMimZhea/tJLL1FaWpolz6hRo2jo0KGWYfS4LhAeHu56IShBlUBSUpKqfM5k2rpzryW4c749fx8htfPd8dcBS3Dnsv7ar74szu/JTq2BJ+tcX+Zdmz0fwKHTVsCtAT4kJIT4KDwgIED6YTCTfTC50PTZs2dTVVWVZYlzcnLoxIkTlmFP9wQHB5PJZPJ0NVTNPyIigoKCgigzM1NVfk9n8mb7sLAw4k96errbGZs1akhBgYFUKv39cdfjovaq/4Yax8dScFAgmUqry+rZtYPqsty+4LXMgLebmJgYSk1NrSWFsUfz3yz/llr/Lhq7xjW14ziQkJBAKSkpNSOt+vgIvrbgb5UMvU4IuDXAN23aVPwAdOnShY4fP05NmjSxqdqFpvMKR7ONDRkGIOCUQJNGCf/f3n2GSJH0cRz/rznnuOpjYDGg4BsThjeHa3hc8VQEQRTFCL4wgC+FAw99oy8UQVEE0QVR0RciPKiPGDFgWgwo5hx3Maxr1rv9FczeMOfu3M5Wz/T0fQt0eqd7qqs/Ncy/q7q7yrau/93+9/+T1vU/ufbr2F+q9fn4jTu2b1ue10o7ePSU5ZYv//rf1POKz5dlBBAIRiCn/Ezwryay532oxb1nzx53M47O3mbPnu0C9ooVK2zJkiXu/Z+tr6wYJSUlVlpaWtnqtL+fza1IWvBp/7pU7DCdLfiKnXpcyObvPS14j1+EamZFC76aYB42DzTAx8qnLqV65d2ElaVk62OfI8DHJGr+SoCvuWGqORDgU5Wr+ecI8DU3TDUHAnyqcql/LtDH5GLFqiq4a5tk62P58IoAAggggAAC/0wgLQH+nxWFrRBAAAEEEEDAlwAB3pck+SCAAAIIIBAiAQJ8iCqDoiCAAAIIIOBLgADvS5J8EEAAAQQQCJEAAT5ElUFREEAAAQQQ8CVAgPclST4IIIAAAgiESIAAH6LKoCgIIIAAAgj4EiDA+5IkHwQQQAABBEIkQIAPUWVQFAQQQAABBHwJEOB9SZIPAggggAACIRIgwIeoMigKAggggAACvgQI8L4kyQcBBBBAAIEQCRDgQ1QZFAUBBBBAAAFfAgR4X5LkgwACCCCAQIgECPAhqgyKggACCCCAgC+BnD/Kk6/MyCd7BE6ePGmPHz+2qVOnZk+hI1LSoqIiO3/+vM2ZMyciR5Q9h3H79m3bv3+/LV68OHsKHZGSPn/+3DZv3mzLly+PyBGF/zBowYe/jgIp4bdv3+zr16+B5E2mVQt8//7dvnz5UvVGrA1EQPafP38OJG8yrVpAbUnsqzbyvZYA71uU/BBAAAEEEAiBAAE+BJVAERBAAAEEEPAtwDV436JZkl9xcbF9+vTJOnfunCUljk4x3759a69fv7Zu3bpF56Cy5EjKysrs2bNnlpeXlyUljk4x1T1/79496927d3QOKuRHQoAPeQVRPAQQQAABBFIRoIs+FTU+gwACCCCAQMgFCPAhryCKhwACCCCAQCoCdVL5EJ/JPoHS0lLbtGlTRcFHjx5t/fr1syNHjtiVK1esWbNm7pn4Bg0aVGzDgh8BPZJ45swZu3HjhjVv3twmTZpktWvXxt4Pb5W5FBYWmp6/jqU+ffrYuHHj7Pr163b06FFT3UyZMsU6dOgQ24RXjwKHDx+2W7duWatWraygoMAaNWqEvUffZFnV/q08JduI9dkvcPfuXfdjNmPGDBs8eLC1b9/e7t+/bxcvXrR58+aZTgD0o9erV6/sP9iQHcGxY8fcc+8KJE+fPnXjD7x//x77NNRT37593fd9yJAhdunSJRs2bJg1bNjQtm/f7r73utFx165dpvUkvwK6mfH06dO2YMECd1OpfoM6deqEvV/mKnOji75Knuis1Kh1derUca2WV69eWa1atezOnTvWv39/15ocMGCA3bx5MzoHHKIjuXz5sjtxUit+0KBBplYk9umpIPWU1K1b1wUaBfMePXrYixcvrEuXLq41qadIPn786E5+01Oif89e9HujE1l568RWvYPYp7f+CfDp9c7Y3jRyWk5OjuXm5tqOHTtct9mbN2/cj5wKpVaNHiEi+Rd49+6dHTx40DSK2saNG92PHfb+nSvL8cePH+7EduTIkW6TeHu9wXe/Mrmavd+kSRPTv507d7rGQ8+ePQ37mplW99Ncg6+uWJZur+uOsaQhIzUWuq67x4aO1LC1TZs2jW3Cq0cBtSB1z0Os1Xju3DkXVLD3iFxFVteuXXPPvev6r5JakjF7/a3vfuPGjbVI8ihw6tQp12OSn5/vxh5Qw2Ls2LHYezROlhUt+GRCEVm/d+9ee/DggTsadZOpJa/uSV2HV9IAFHqP5F9AgV2teCUNcqMAg71/58pyVIDXpahY6tixoz158sR0oqvueb2qO5nkV6B+/fruRFa56iS3Xr16hr1f42S5MdBNMqGIrH/06JHt27fP/ZBpBLu5c+e6QLN7927XbaYANH/+fNeqj8ghh+YwNGrdgQMH7MOHD+5mRuzTWzVr16616dOnuzu5Y3s+ceKEXb161dXHhAkTuLk0BuPxVb0karXrfh+dSI0aNcq6d+9u2HtETpIVAT4JUNRWK7gnPgqn6/M6uyYFK/Az55+9F2wpyD0moEfkFHz0jxScwM++49gH5x2fMwE+XoNlBBBAAAEEIiLAqWtEKpLDQAABBBBAIF6AAB+vwTICCCCAAAIRESDAR6QiOQwEEEAAAQTiBQjw8RosI5AmAd1RryE8NXSnxunWMLYaLljp4cOHbrz01q1b26xZs2zy5Mnujm+t07C3euSrRYsWbkz74uJivU1CAAEE/iZAgP8bCW8gELyAHt3ScLUaH13jdWs4W434pTRt2jQ3MEtsEiCNYaCnHzTE8Pjx423ZsmVu3gBNXLNq1argC8seEEAgKwW4iz4rq41CZ7uABh2KDfyh5YULF7oJT/SMvAYcUsu8ZcuWboyCNm3auNnoLly4YFu3bnXP1Ov4X7586QK+JgkiIYAAAokCDN+UKMLfCKRBQJOgLFq0yHW5t23b1g2XOnDgQNOMW5oURcFdSV3xeXl5blkTBqlVnzjjn0ZlU1c/CQEEEIgXoIs+XoNlBNIkoOvvuvaugK0R1TTLnIZM1fV1TbOpSTmUNBuXuvKVtM3QoUPdem2jf2rVM8Sw4+E/BBBIECDAJ4DwJwLpECgpKbHhw4dbu3btTC3zQ4cOuVa8JkTR+6tXr3bj169Zs6ZiKlPNhnb27Fl33V5lLCwstDFjxphmSyMhgAACiQJ00SeK8DcCaRDQjXJLly619evXu5b7xIkT3RS+2vW6dets5syZtmHDBisoKDB14et6vaY1XblypY0YMcJ1yetvTT+r7n4SAgggkCjATXaJIvyNQJoE1CWvlrxuooslvbdt2zZ3J71mOCsrK3OtfM32p9a+kuaVVxe+HqMjIYAAApUJEOArk+F9BDIkoJnPNBmHWup6RK5r1662ZcuWDJWG3SKAQLYKEOCzteYod2QF9Mz78ePHraioyN10l5+fz4xnka1tDgyB4AQI8MHZkjMCCCCAAAIZE+Au+ozRs2MEEEAAAQSCEyDAB2dLzggggAACCGRMgACfMXp2jAACCCCAQHACBPjgbMkZAQQQQACBjAkQ4DNGz44RQAABBBAIToAAH5wtOSOAAAIIIJAxgT8B/xhnEn8P8oQAAAAASUVORK5CYII=" alt="plot of chunk unnamed-chunk-2"/> </p>
<pre><code class="r">ggplot(data=prostate_cancer,aes(x=age,y=antigenValue,col=tumor))+geom_point()
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-2"/> </p>
<h3>Using the Validation Set Method to Calculate MSE</h3>
<pre><code class="r">train=sample(380,200)
glm.fit=glm(tumor~age+rectalExamResult+capsularInvolvement+antigenValue+gleasonScore,data=prostate_cancer,family = binomial,subset=train)
mean((tumor-predict(glm.fit,prostate_cancer, type="response"))[-train]^2)
</code></pre>
<pre><code>## [1] 0.175
</code></pre>
<h3>Using the Leave One Out Cross-Validation Set</h3>
<pre><code class="r">library(boot)
glm.fit=glm(tumor~age+rectalExamResult+capsularInvolvement+antigenValue+gleasonScore,data=prostate_cancer,family = binomial)
coef(glm.fit)
</code></pre>
<pre><code>## (Intercept) age rectalExamResult2
## -7.86572 -0.01080 0.73326
## rectalExamResult3 rectalExamResult4 capsularInvolvement
## 1.49780 1.36615 0.53790
## antigenValue gleasonScore
## 0.02668 0.97896
</code></pre>
<pre><code class="r">kfCV <- cv.glm(data=prostate_cancer, glmfit=glm.fit)
kfCV$delta
</code></pre>
<pre><code>## [1] 0.1741 0.1740
</code></pre>
<h3>Polynomial Logistic Regression using Leave One Out Cross-Validation</h3>
<pre><code class="r">cv.error=rep(0,5)
for (i in 1:5){
glm.fit=glm(tumor~age+rectalExamResult+capsularInvolvement+poly(antigenValue,i)+poly(gleasonScore,i),data=prostate_cancer,family = binomial)
cv.error[i]=cv.glm(data=prostate_cancer, glmfit=glm.fit)$delta[1]
}
</code></pre>
<pre><code>## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred