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Contact_Closeness.html
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<!-- Automatically generated by RStudio [12861c30b10411e1afa60800200c9a66] -->
<h3>Contact_Closeness.R</h3>
<p>hbuhr — <em>Jun 11, 2013, 11:02 AM</em></p>
<pre><code class="r">
# Runs a logistic regression to estimate the fit of different features for contact closeness
# vote outcome model
# Updated: Helena Buhr (June 10, 2013)
#---------
# Set up
#---------
# Remove old data
delete_old <- function()
rm(list=ls(pos=.GlobalEnv), pos=.GlobalEnv)
delete_old()
ls()
</code></pre>
<pre><code>character(0)
</code></pre>
<pre><code class="r">
# Set working directory
#directory_path <- '/Users/shill/documents/R scripts/Mobile Viral Growth'
directory_path <- '/Users/hbuhr/Google Drive/Android_viral'
setwd(directory_path) # set working directory
getwd() # verify working directory
</code></pre>
<pre><code>[1] "/Users/hbuhr/Google Drive/Android_viral"
</code></pre>
<pre><code class="r">
# Open click datafiles and zip code data (sepate files for zip code data)
dat <- read.table("contact_data.csv", header = TRUE, sep = ",") # read data
summary(dat)
</code></pre>
<pre><code> areaCode vote lastCallDateTime callFreq
Min. : 11 Min. :0.000 0 :1030 Min. : 0.0
1st Qu.:408 1st Qu.:0.000 5/24/13 10:26: 3 1st Qu.: 0.0
Median :502 Median :1.000 6/3/13 8:16 : 3 Median : 0.0
Mean :565 Mean :0.638 5/2/13 10:01 : 2 Mean : 3.5
3rd Qu.:800 3rd Qu.:1.000 5/21/13 21:54: 2 3rd Qu.: 0.0
Max. :980 Max. :2.000 5/24/13 21:07: 2 Max. :730.0
(Other) : 236
textMsgs talkTime starred hasPhoto
Min. : 0.0 Min. : 0.0 Min. :0.0000 Min. :0.00
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.:0.0000 1st Qu.:0.00
Median : 0.0 Median : 0.0 Median :0.0000 Median :0.00
Mean : 7.6 Mean : 3.7 Mean :0.0563 Mean :0.28
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.:0.0000 3rd Qu.:1.00
Max. :1206.0 Max. :754.0 Max. :1.0000 Max. :1.00
isFirstName callRecency areaCodeDist gender originAreaCode
Min. :0.000 Min. :0.000 Min. :0.000 F:394 Min. :408
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.301 M:884 1st Qu.:408
Median :0.000 Median :0.000 Median :1.982 Median :408
Mean :0.187 Mean :0.477 Mean :1.479 Mean :523
3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:2.386 3rd Qu.:612
Max. :1.000 Max. :2.562 Max. :2.857 Max. :925
testSubject
Min. :1.00
1st Qu.:2.00
Median :5.00
Mean :4.71
3rd Qu.:7.00
Max. :9.00
</code></pre>
<pre><code class="r">
#areaCode
#vote Other (=0), Friend (=1), Family (=2)
#lastCallDateTime
#timeCalled
#textMsgs
#callFreq
#starred
#hasPhoto
#isFirstName
#callRecency
#areaCodeDist
#
#dat<-subset(dat, dat$gender=="F")
# Select which version to test
#dat$vote <- ifelse(dat$vote == "2" | dat$vote == "1", 1,0) #combine friend and family
#dat$vote <- ifelse(dat$vote == "2", 1,0) #just family
dat$friend <- ifelse(dat$vote == "1", 1,0) #just friend
dat$friend <- ordered(dat$friend,
levels = c(0,1),
labels = c("Not Friend", "Friend"))
dat$family <- ifelse(dat$vote == "2", 1,0)
dat$outcome <- ordered(dat$vote,
levels = c(0,1, 2),
labels = c("Other", "Friend", "Family"))
# Descriptives by user
descriptives <- table(dat$testSubject, dat$friend)
descriptives <- cbind(descriptives, descriptives[,2] / (descriptives[,2] + descriptives[,1]), (descriptives[,2] + descriptives[,1]))
par(mfrow=c(1,2))
plot( descriptives[,4], descriptives[,2] , xlab = "Total Contacts", ylab = "Friends")
abline(lm(descriptives[,2] ~ descriptives[,4]) , col = 'red', lty = 2)
plot(descriptives[,4] , descriptives[,3] , xlab = "Total Contacts", ylab = " % Friends in contact list") # share friends
abline(lm(descriptives[,3] ~ descriptives[,4]), col = 'red', lty = 2)
</code></pre>
<p><img 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Cxsvim6q5i13HoRC6dH1DpwqvXJbzUNNxM3WTKzDitMgTcriQeBVzfvwy/zUmBxojxm1qNc4K3UBlV5bV3VT3bqfBHxK2AucKeknSS9EzgVOLpTGczMoCCT7MzaYbaUbUN25FDeCrljIuIDkn4F7AEsBPaLiEc6mcHMzAXeSmtrKvsDf8mi9tdOnzsirgWu7fR5zcyW8SV6K6X50lpC+9aoX5Y6i5lZCh7BWyk9BYNPUX/rphGe3GZmPckF3krJhd3Mep0v0ZuZmZWQC7yVSl3V7RoauPwaaULqLGZmKbnAW2lIktDRYulXZ0QsTZ3HzCwlF3grjUH6dwcG+6Nxc+osZmapucBbKcyWsgn0vSeIi1NnMTMrAs+it1LIQA9TP9iz583Mci7wVgod2inOzKxr+BK9mZlZCbnAW1dbLG3aUPXqR6RJqbOYmRWJC7x1tQlkRwV8e4OIRamzmJkViQu8da2Gsu0FW3yX+nWps5iZFY0LvHUlSYK+o8TQpW5qY2b2TJ5Fb13pD9D/OLVjNop4OnUWM7MicoG3rvTiiAbQSJ3DzKyofInezMyshFzgravMk6Y1VP2vRdIGqbOYmRWZC7x1lSlU3w1x06SIR1JnsdUjaWN5O1+ztnOBt64xKG0O8ZonaXw1dRYblx8CN0vaKnUQszJzgbeu0Ud25BBctV7EwtRZbNz+Atwi6YOSpqUOY1ZGLvDWFa6RJjxB/eNZ1L6VOou1xOXAq4DXAw9IulTSqyRNTZzLrDRc4K0rzIhY6jXv5RIR90TEbsD/AxYCXwXmSbo8bTKzcvA6eDNLKiLuAI4Hjle+adA6iSOZlYJH8FZos6WsoepXaxp4Tuos1jKfAuaM9ERELIqIv3c4j1kpeQRvhTadbD/B3dUYvDN1FmuNiPh66gxmvcAF3grrSWnNyWQzatQP9y9qe0maAtQioj7s2ACwUUT8bwdzPAeYGBG3juG1hwIHjPL0s4H7WpnNrNv4fdMKaxLVQ4Kh6ydFPJw6S1lJyshv1Z0BXC/pxmFP7w0cBOzZwUgzgC2AQ1f2woi4nHw2/jNIOh9wt0PraS7wVkiS9CB83jPn2+5dwCXNr49e7rmngP9s58kl9QNTImIeQESc0c7zmfUST7KzQpG0vqRPAVdtDJdIWi91pjKLiEuBCnAcsGPz6wrQHxFTI+LiVp9TUibpLElzgDowV9IiSbMkHdLq85n1Khd4KwxJE4GHgduBDwBXAd+RtFnSYCUXEUsi4jzyme0C1gDeJ2mfNp3yIuD55Jf+p5K/D21Mfln+CElHtum8Zj3FBd6K5L3AR+pUX9qg/zkRcQNwYfO4tZGkVwB3kd+3Phd4B/BxSe9uw+l2Bw6PiD9HxMLIzY+ImcAx5Pf+zWycXOCtSCZfSF8A0R+NXzeP3Uk+yrP2OhB4D/AIsB9wcPPYvm041yxgl1Ge2wt4rA3nNOs5nmRnhTEN/voCJnwqiH8fdvhTgPvPt9808sL6auDRiJgl6WXAgjac62TgaknHAvc2zzENeB75e9Ib2nBOs57jAm+F8ShZ5UqGHn8tjS8hXQi8AmhExGWps/WAHwIXABOAL0vaFrgSOLPVJ4qIWyXtQD6pb0tgQ/IPF5cAN0dEtPqcZr3IBd4K41Hq1x4acdVh0u7ApsBPge8njtUTIuJqSY8Da5JfMdkKODIiblzxd672+QaBtvxsM8u5wFthbBqxGCAifpo6S6+Q9Fvgw+RXS5YtUTtj2PM/johjUmQzs/FxgTfrbYcBfyO/Fz7SB6t5HU1jZi3jAm/J1VU9Hbg1i9p1qbP0moi4rfnlfOCBlFnMrLVc4C2phrLthaZ/h/ppM1KH6UGSbia/7z6an0bE8Z3KY2at4wJvifUdJZZ+fkbE0tRJetTx5K1pR/N4p4KYWWu5wFsyNVV272NCrT8aN6XO0qsi4nepM5hZe7jAWzJzWHLT1iy5wYuezcxazwXekpkeUXNxNzNrD/eiNzMkTZGULXdsQNKzUmUys/FxgbeOa2jg6Iayw1LnsH/uzT5A3tzmdc2iPtA8tjfw2bQJzWx1Fe4SvaR+YEpEuMFGCQ1Km/eT7TKPxkHrpQ5jAO8i7wEPcPRyzz0F/Gdn45hZqxRiBN8cRZwlaQ5QB+ZKWiRplqRDVvb91j36yI4cgq+tF7EwdRaDiLiUfJncceSbv1Saj/6ImBoRF6fMZ2arrxAFHrgIeD6wJ/ne333AxsChwBGSjkyYzVokb2rDFt+l7o51BRIRS4DzgWcBr2j++3BJh0iakDadma2uohT43YHDI+LPEbEwcvMjYiZwDPm9QOtyd9OYfRb1A93UppD+AzgWeLj575uBtwHvSJbIzMalKAV+FrDLKM/tRb5XtHW5bSPqp0QMpc5hI3o9cFJE3AUQEbPIC/6+SVOZ2WoryiS7k4GrJR1LvqvVAmAa8DzyjG9ImM2sF9wP7AHcMOzYTuR/i2bWhQpR4CPiVkk7kE/y2RLYkHzUfglwc0SMqR+KpJcDO4zy9AuBxeNPa6uqruoBfbBVf9TOWPmrLZErgJ9J2hP4DfnfywbkI3sz60KFKPAAETEI3Ljs35L6gIljLe5Nixh9c4ynATdO67AnpTUnk+1fo354YX7Z7Bki4h/ND8i7AlsDXwBmAtWkwcxstRXiPVfSVOAw8kuC5wFTgC8CVUn/BXwwxrCsKiL+AvxllHO8gnxEYh00ieohwdD1kyIeXvmrLRVJ6wKXkhf3CcDBwAD5aP6AhNHMbDUVZZLdfwIvBX4EfAY4G3gT+eX6DPBW4V1oUNpcxGuepPHV1FlspY4FJgKXAw+Qz4tZAJyVMpR1D0kTJH1I0o8l/VzSTZLWSJ2rlxWlwL8ZOCQiLgG+DfwiImZGxFzgE3gmb1f6DTx4JvW3uKlNV3g2cC7wZWCTiPgWcAh5AxyzsTgXWAfYKyJeB3wVuKB5u9USKMr/8X8FdpM0DXgN8OJhz70Q+J8kqWxcdo5Y4mVxXeMfwObNW2GZpHWAucDmaWNZF3kZ+VLLpQAR8QVgiPw93BIoxD148k9+VwBbARcCUyT9FbgNeBWwc7poZj3hi8BMSfcA3wN+QH577JqkqaybzItnNrGqk/8eWQKFKPARMVPStsDaEfGEpCr5mtw1yS/de3lbF6mpslsfE95YicHlNy+xgoqI2ZKeAywlnz1/BPAk8M2kwayb3CPpjIj4KICkvYH34w2LkilEgQdoLod7ovl1jXwUYS0iaTfg3eQNhO4Cjmv2HG+p2VK2DdmhQ8Sprf7Z1j6SpgBzI6LePPTZ5paxmwD/my6ZdZGPAH+UtCn5e/mzgM08QEunMAXe2kfSHuRbgX4AeBR4D/B5SYeNcEltXKaTzQBuz6I2u5U/19pDUkY+F+cM4HpJNw57em/gIPJNoMxWKCIWNq/EvpC8f8KfI+LpxLF6WlEm2Vl7nUy+mc/dzU18ziVv/PO6Vp5knjStD/arU7+slT/X2upd5B0ejyZfprp42OMy4Ifpolm3iYilEXFrRPzGxT09F/jeUAceWu7Y34HJrTzJz2DhmdT3mRix/LmsoLwfvFl5+RJ9b/gTcBT5CgUkrUd+SXbbVp7E28B2p4hYIukK4GJgO5qzniUB/DgiPpAwnpmtJhf43nA6+QzXLckn2L0VeGtE3JsylBXKh8knYB4NDG9MNDdNHDMbLxf4HhAR8yRtRN4xcCJwRETc0aqf31C2PehD36F+sEfxXWsT4OKIuHGlrzSzruAC3yOay5/a1LSk7yix9DIX9672beAgSb+PiEdThzGz8fMkOxuXmiq7A7X+aNyUOouNy8bAG4CHJN0t6Y7m44LUwcxs9XgEb6ut2dTmPW5qUwo/AP4wwnHfgzfrUh7B22p7PjTOpP5WN7XpfhExJyJ+D9xHvqzy9oj4fTdPxDyf/uf9UaqkzmGWygoLvKRNJFUkTZF0jKR9OhXMii8iwrvFlUNzL+8vAY8DvwCeknRtc1+IrrQUYjuy00/zdqXWo0b9xZf0CvIlVRuQ7/b2DuDjkt7doWxm1jmHA9OBbSNiHWBrQMAJSVONw4dZcgegk8iOT53FLIUVfbI9kLxn+SPAfsDBzWP7diCXFdigtPkSVa97TGppJzxL6uXApyPirwARcR95/4SdkqYahyHgu9RPGoL7l6iyY+o8Zp22ogI/DXgMeDXwaETMIt9AYEEngllx9ZEdMQRfWy9i4cpfbV3iFvK/9eFeTf4e0LVmRCzNovaN/mjMTJ3FrNNWNIv+h8AFwATgy81dgq4EzuxEMCumhrLthbb4b+on75w6jLXSNcBfJO0E/Bp4MbA9XTyCN+t1oxb4iLha0uPAmsC3gK2AI93pqtf1vVcsvWznNuwlb+lExBOStgPeTv63/kPgwIiYkzZZ6yyWNuunekoQF2RRm5U6j1m7rXB2aUT8NCK+GRFDEXGPi7udRe0IN7UpH+U7y+wO/Km5ucxCYFdJE9Ima501IuYE8Zk+OKMmTU+dx6zdnjGCl/QL8lH7aG6IiK6dWWvj42VxpfUfwLHkE2kBbgbOI59Jf0WqUK2WRe0vS1Q5ZwLZ2Yuk902KeDh1JrN2GWkEfxJwDPBZ8kJ/NfA+8q0kAW7tTDQz66DXAydFxF0AzUm1x1LCVTP90fj1UnRShexV93TxOn+zlXlGgY+ImRHxK+AlwCkRcU5E3BIRXyAv9Ad1OqSlNU+atkTV7z6d70hn5XQ/sMdyx3aipKtmqjF4Zxa1b02PqKXOYtYuK5pF/xSw5XLHXkDe6cp6yGSqhwRDP5kY8VDqLNY2VwA/k7Qn8BvgheRNrl6fNJWZrbYVFfgrgOsl/TvwO/JlM1sBe3UimBXDoLR5P9lO82gctF7qMNY2EfEPSS8HdiXvYvcFYGZ0aM6FpAFgaUQ0OnG+4ZaoejJocX8MfrrT5zZrp1Fn0UfEncDLgC8DDeAq4KUR8afORLMicFOb3hER8yPi2og4OyL+u13FXdLmkr4i6SWS1pP0ReBh4ElJV0jK2nHe0dxF/eyAzRrKjuzkec3abYXbxUbEo8DlHcpiBZRF7SOpM1jpnA78HbgdOJH8fegF5J0yzwY+1nx0xLYR9cekj6xJdlFd1blZ1L7RqXObtdOoBV7SmuQz57cDhn+i/nFznayZ2ep4DfDciKg3d6jcOyIeAJD0MeDSTgdaL2LhA9J716P62kFp84GIv3c6g1mrrWgE/2HyfvRHkze9WGZuWxNZYZwm9Xndu7XBXeSbV30B+CXwBuCi5nN7AXenCLVpxGLyDn5mpbCiAr8JcLG71/We2VK2NdlXTyQ7DZidOo+1X4ev2B0F/KC59fQ9wDmS3kW+AdxU8hF+UtdIE2ZELE2dw2w8VlTgvw0cJOn3zXvx1iOmk80QzK5EzcW9d3Tsil1E3NvcvGo34Dnk9+PnkY/cfxgF2OdgbyovWaLqCQ3qR6/RvH1g1m1WVOA3Jr90NkPSfcCyT7PX+x58ec2Tpk0h269O/YgVzsC0sunoFbuICOCnzcc/SXqOpIkRsdKOmZI2I889kg351ysRq6QS9d/WVLmsn+zTC6Sjpkb41qR1nRW9h/8A+EPz63WAJ8mLvH/RS8xNbXpWUa7YzQC2AA4dw2u3ZfTtbLcif89abdVoXF9Xde01qH7qIenojSKeHs/PM+u0FRX4fwDvAPYj33DieOBIxvaHZ12qEoMXpM5gSRTiil1EnLEKr/0J8JORnpN0PnknvnHJonZ1XdU/rQGZpMXNKw9mXWFFBf4w4LXku0xdB/wCeFPz+Jntj2ZmHTT8it1wbb1iJ6kfmBIR89p5nvHIojY7A1zZrdusaD/4VwPnAA8CNFtIXkBe9K2ErinR3t+2aiJiTkT8foTHva0+l6RM0lmS5gB1YK6kRZJmSTqk1edrlUekSQ9Ia6TOYTZWKyrwc8iL/HBvBnxvtoQaGrhsH/pflTqHdZak30raSdKJku4a4fGZNpz2IuD5wJ7ky+L6yG8RHAocIamQLWMbMLQB1QsbyryjpnWFFV2iPx/4PflSlo0kzSTfXW7XDuSyDqqpslsfExr90bgpdRbruMOAvwH3styM9qZ2XDrfHdgxIh4edmw+MFPSMcBpwCVtOO+4bBqxeL704Ylkn6tp4IlqDP4odSazFRm1wEfEI821qvsDmwM3ATeFmz+Uymwp24bs0CHi1NRZrPMi4rbml/OBTq33ngXsAnx9hOf2Ah7rUI5VNi1i3tPS8RnZRUtUebI/GrekzmQ2mpVtNrMQ+GKHslgC08n2BWZlbmpjnXMycLWkY8mvHCwgb7LzPPL3pDckzLZSEyMefEx6x1r0bzdXmrp2xILUmcxG8owCL+m35F2tXgGMNOHlxxFxTLuDWWdkUbs6dQbrLRFxq6QdgB3Jb/ttSD5qvwS4uRuWojW3T565duogZisw0gh+2T25f9C5e3JmlpikTYBHgQHgXcDfI+K6dpwrIgaBrt/n4gFpjbVg2o/gMfeut6J5RoFfdk9O0qnAIxFxdqdDWWf8UurfuQB9vy09Sa8AbiDvDX8y8BLy5i5rR4Rv041i04jFdVX33Bve+Jh0VHNkb1YIK1omdz+wnbw2upTqqp75CrJ9UuewwjgQeA/wCHn3yoObx/ZNGaobZFH7FsQta1L9xGxptfvfm7Xaigr8YvIZrQsk3SHp9ubjvA5lszZpKNtesOUt1Nty+dW60jTy++CvBh6NiFlAlXwCnK1EJeqfF/HgdLJTTpNW9L5q1jErmkV/PXDbCMefaFMW65i+94qll/nyvA3zQ/JOlROALzeXyF6J21KPWX/UPlGTpr87/2C0OHUes5Fm0V8PHBAR90t6FFg3IuZ0Ppq1Q02VXfuYUHNTGxsuIq6W9DiwJvAt8t3YjuzU9rFlUY24Z9PUIcyaRrqU9BKg0vz6pYCXUZVINRo/q8Tg+1PnsOKJiJ9GxDcjYigi7nFxXz2nSX2D0rOelNZMmUPS+pK2kFRZ+autjFbY6MbMyk3SL8hH7aOLrX4yAAAgAElEQVS5ISJO6FSeMjglYmiR9PRkssuXqPLZTl8tkyTgOPKNwRYCL5f08oh4sJM5LD0X+B4yW8q2jainzmGFchL5+8DWwEeBi4FbgG2B9wK3povWvSZFPFKTTphAdm5D2fxK1P/UwdN/BFgb2CsihiQdCFwl6U3hZXw9ZbQCv6mkAfIOU1VJWwx7blFEPN7+aNZKDQ18YGsqdfI3cDMAImImgKS3AadExFebT90iaTb5B4D/SpWvm1Uj7msoO1Xo1Jp0fLUNW++OYmfgLRExBBARV0l6FvBK4CcdymAFMFqB/8Ny//7bsK+vIV8na11iUNq8n2zneTQOXC91GCuqp8jbxg73AsAf5sehEvXbHpMOmgrrd7CxVA1YvqtesOJl0VZCIxX4DVbyPYXvE23/qo/s8CH4mrts2QpcAVwv6d+B3wEvJp9Jv1fSVCXQ/LtbuHPnTvkL4Bs0/9tJeh3wcWCtzkWwIhipVa37KZdIQ9mLhDb/DvWTZ4zwvPKZvi8EFkfE7zsczwoiIu6U9DLgzeT3468CfuCJWa3ztLRxP9nUKvU727yhzmeAf5P0a2A2sA7w7Ih4so3ntALyJLuSq0T9NuCgUYr7c4FPk78JTJe0EfDa5kYg1kOaH/QuALYDlrVbPU7SjyPiA+mSlcfEiAeXaOCAGtm7rpFObNfmNM1B2oGStgImAvdGhBvv9CAX+B4laV3gr8DLI+K3zWOfI59U9bGU2SyJD5O3qz2afGnVMnPTxCmnM6mddyLZ6fuQnQCc1c5zRcR97fz5VnyedFFis6WsuSZ2JC8BTl1W3JuOIZ9pa71nE+DiiLgxIn4/7NGpmd894ZSIoXuonx5oo4ayI1LnsXIrXIGX1C/Jk0HGqa7qAdPJPrKCe32D5Pt+DzdA3kfbes+3gYMkrZ86SNltG1HPqB1do3HtY9Lk1HmsvApR4JWPNM+SNAeoA3MlLZI0S9IhqfN1m3nStD7Yfwn1y1bwsluArSTtC/l/A+Ac4NpOZLTC2Rh4A/CQpLubO0jeIemC1MHKKCJicsRjXtli7VSUe/AXkTfV2RO4D1gETCXvpnWBpIGIuCRhvq4ymeohwdBPJq5gBnRE1CUdCvxU0uGAgG9FxKUdC2pF8gOe2f8CfA++rR6Q1tiQ/u0XseSOaRHzUuexcilKgd8d2DEiHh52bD4wU9IxwGmAC/wYLJY2q5C9ch6NQ1bW1CYiFgAv70QuK7bmjpFzACRtCDwe3k647TaNWFxXdf4ksi/VNHBCNQbvTJ3JyqMQl+iBWcAuozy3F/BYB7N0tTUi5vRHbYYv/dmqkNQn6aOS/gzcALxO0nckuflhm2VRmw1Dn5xAnLVY2iR1HiuPohT4k4GTJf1O0tclfV7Sf0m6DdiHfOmWmbXPYeS7j/1H89+/AP7RPG5t1h+NmUvRF/rJPr1AWjt1HiuHQlyij4hbJe0A7EjeD3tD8lH7JcDNY+36JOktwOtHefplwCPjT1tcs6XsGlhySnOTCbNV8GrySZYPAkREoznB7lLgzJTBekU1Bn88W/r5ev/XaMhsXApR4AGa3dNuXPbv5m52S1expeONjL695UnAlNVPWGw1VXbbmurep8TgUamzWFeaQ17kfzns2JuBh5Kk6VHN7Zy9pbO1RCEKvKTNgTOAC4H7gbOBtwAVSd8Ajogx7GMeEXMZZdavpAXAGi0LXSCzpWwbskOHiFNTZ7GudT7we2A3YCNJM8mvpu2aMlSvaij7N4BK1P8ndRbrXkW5B3868HfgduD95B88XkC+CcoU3Dp1haaT7TsEs/PJOmarLiIeIV+WegmwrGXxphFxe9JgPeq/afwZOKihgaNTZ7HuVYgRPPAa4LnNtdn7AHtHxAMAkj5Gfh/QRjBPmjaFbJ8a9ff7xp2NR+QrL76YOofBzhFLHpJOWpfqhXVV355F7WupM1n3KcoI/i7g4ObXvyTvqLXMXsDdnQ7ULdaKmN8ftRmT/rWHgNmYSPqtpJ0knSjprhEen0mdsVdtFPH0YmofFuxZ08Bok4fNRlWUEfxRwA8kvRu4BzhH0ruAIfKOdq9JGc6sxA4D/ka+JO6nIzzv7moJTc3nFR1wmtR3Suow1nUKUeAj4l5J25JP8HkO+f34eeQj9x+6o9bIZkvZozC0s///sdUUEbcBSDoVeCQizk6byEbipa+2OgpR4CHffIF8BDHSKMKW01C2/dZkH36U+sErf7XZSt0P/D9JEyJiaeow9kyLpA0qZC9fQP0X60Y8lTqPFV9R7sHbKus7Ugxd5tG7tchi8vkuC5q7yN3efJyXOpjlJkU8MsTQwmlkX1joFsI2BoUZwdvY1VTZrY8JS/uj8cvUWaw0rgduG+H4E50OYqMbiMbP66quXSX71GPS+73nhK2IR/Bd5o9SpY++g4O4KHUW636Srpe0dkTcT17g/xERvxn28AqWgsmidg3Er6eRnfJHqZI6jxWXR/Bd5sURDeCg1DmsNF4CLCsSLyXvKPnqdHFsLCpR/yLAi1MHsULzCN7MzKyEXOC7yGwpmy25YZ2ZAXCNNKGmgTct611vNpwLfJdYLG22Ddk314dq6ixWOptK2oJ8m+aqpC2GPdZNHc5GNyNi6dPUbgJ9sKaBN6XOY8Xie/BdYgLZEUPwNa9/tTb4w3L//tuwr68B9utcFFtVa0XMXyQdXyW7aIkqT/ZH4+bUmawYPILvAg1l2wuedQv161JnsdLZgPyD/miPt6aLZmM1KeLhJdSPCyYcXFd129R5rBg8gu8Kfe8WSy9xUxtrNXetK4+BiL8D70mdw4rDBb4LVGLw/akzmJlZd/ElejOzkqmrul1D2cF/kwZSZ7F0XOBXkaR+Sa+WtKuktdt5rtlS5j9QM1tVWdT+AlQ2oXqe30N6lwv8KpA0EbgC2AfYCXiiuc1ty82Tpm1Dds360NYPEWbDSapKOlfSbEnflvSy1Jls9VSi/kXB/25Mdto10oTUeazzXOBXzfeBn0XEByPiY8DLgHPaMZKfTPWdQfx0YsSDrf7ZZgCSNMLh/YBHyH+3Pwp8rKOhrKUq1M4RevDNZF4N0YNc4FdNf0R8Zdk/IuJ3wK3A9q08yWJpMxG7zKfxlZW/2my1TZZ0k6Q9hh17AtiG/Hf6hUAjSTJriYiISgx+Jova11Jnsc5zgV81SyQt30luS/K9tFumQnbIEFxZhKY2kjaV9BlJ10n6lqSNU2ey1oj89+utwBsl/UzSThHxI+B3wPuB5wDHdiKLpPUkeVWPWQu5wK+abwKfkTSg3CeB7YDftPIk/VE7PYta8qY2kqYAc4BfAe8APgt8U9LmSYNZy0TEQxHxPuDdwMGSfgT8KSL2i4jTIuJvrT6npK9Iem7z6+dI+iH579nDkj4rb4HaFo9LUxrK3u9GOL3DBX4VRMTngXuBm4CfAQJeHhGRNFj7HAUcHRHfiogFEfFL4DPAYWljWStJ2gaYDhwBfAA4tnnF5kVtOuULgEnNr08E7gA2Bl5BfkXsxDadt6etG/HUUhrf64MzGsq802wPcIFfRRHx6Yh4WUS8LiI+HBFPt+pn/1LqfyifqV8Uk4FZyx37X2BqgizWBpIuAc4BDiLvSf94RLwNOAU4VdLVbY6wB3BqRMyNiLvIJ/bt3OZz9qyBiPuH4GNCH6tpYJvUeay9XOALYraUvZLsa+uQPSt1lmFuBY5c7tglPHNzEutCkvqA6RHxpoh4J/nVmTcARMSfI2If4Lw2nf4VkjYiv721zrDj25H/3hVec0nhS5qPrtnGOYva7UPwUREHPyWts/LvsG7lSS0FMZ1s34A7sqjdnjrLMhFxbbOhz++AS4EdgTuHrySw7hURQ5Juk/TfwGPkl8d3Xu417fgw9zXgjeRL8KYBg8DbJJ1KflvodW04Z0s1l8ZeTD53YBKwv6StI2Ju2mRjk0VtFvDRrvlUYqvFBb4A5knTppDtX6d+ZNH+4CLiSEk7AZsA10TET1NnstaJiOMlbUY+ir6tE/NJIuJc4FwASZvwf7d8rgfOiYiF7c4wHs3Z/ncAp0XE55rHZgPnSnqPN/CxonCBL4DJVA4I4gdFbWoTETelzmDtExFzyEeiKc79D+Afza9/05xVPzEiVnqZXtJrySfmjeSlwILWJf0XzwJuWFbcASLis5KuAjYnn6fSNWoaeFMfQ+t9h8aXZ/jDSam4wBdAJeqXpM5gVhAzgC2AQ8fw2vtX8NyLaN/7W32Un70h+e2GrjJA7fs1slP3ITsJOD11HmsdF3gzK4yIOGMVXnsv+bLVZ5D0RmCDVuVa7rz3S7pf0geB88mXy14LLIiIh9pxznaKiPijdMYLqXy6oex9lah/NnUmaw3Pok/oGmnC43kzGbOe1Nydca3UOVbDScDLgZ8DPwD+BOyfNNE4vDiicTeND0Nf3xJVdkqdx1rDI/iE9qZ6sVj6deCXqbOYdUpzSdmp5GvvN8kP6Wnye9fnRsSXEsYbk4hokG/MUxrbRtSBC1PnsNZxgU+kpspufUxY2h+NX6bOYtZhF5Hfr94TuA9YRD6TflvgAkkDEeF5KWbj5Ev0CcyWsj763hP83yxcsx6yO3B4s5nOwsjNj4iZwDHA3onz9bxBafO6qp8YlLZMncVWnwt8AtPJ3iS4tUhNbcw6aBawyyjP7UXedMcSGoj4u4hv95OdN+jNpbqWL9EnkEXtW6kzmCV0MnC1pGPJZ8EvIO9o9zzy96Q3JMxmTZWo/76myiUTyD71lPS+KRGPp85kq8YF3sw6KiJulbQDeevjLcnvxz9Gvs/BzSXenbHrVKNxQ0PZggEq+9wjfWV6RC11Jhs7F/gOOk3qOxqmrBUxP3UWs5QiYhC4MXUOW7lK1H8L/HZ66iC2ynwPvoNOJDt9EtkeqXOYmVn5ucB3SEPZiwTPvoX6t1NnMTNbVddIE5Zo4LiGsnekzmJj4wLfMX3vFUOf3zliSeokZr1I0hqSdmpugTx15d9hw82IWPowtYuh7xUNZe9MncdWzgW+A5aoslMQ893UxiwNSesAXwV2BV4NzJf0rLSpus+mEYsXUvswaLeaBt6YOo+tmCfZdUB/NG4CvOWqWQKS+oBfAcdFxI+bx24GzpF0UEQ8nTRgl1krYv5c6cgpVGbUpK2qEfelzmQj8wjezMpuA+DuZcUdICJ+DjwEPDdZqi62dsSCStS/6OJebC7wbfaktOZp+QjCzNJYDAzomX+H05vPmZWSC08bNTRwzCQqB54SMZQ6i1mviogngZ+QX5JfQ1JF0lXAUET8NXG8rrdElR0bGrj0KWnd1FnsX7nAt8liaTMRu8yncWXqLGbG+cB84HrgR8AdeFObluiPxswgfjJAdt5cr04oFE+ya5MJZIcPwdXrRjyVOotZr2u2vz2t+bAWy6J2XUPZOlOonn2PdKxb2haDR/BtUFf1+ULrf5f6tamzmJl1QiXqXxBLr94UNpCk1HnMI/i2aG4De9iM1EHMzDqoPxq/7ge8W1AxeARvZmZWQi7wLfaktOY10oTUOczMUnlMmtzQwOfrqr45dZZe5gLfQnVVD5hIduyMiKWps5iZpbJexMIatY/2wdsHVXlt6jy9ygW+ReZJ0/pg/yXUP586i5lZapMjHltC/cP99L2/oezfUufpRS7wLTKZ6juDuGFixIOps5iZFcFAxN8WU39PoM2elNZMnafXeBZ9CzwtbVQhe/GTNN67XuowZmYFMiXiCeC7WeogPcgj+BaYGPFQJWoHrxexMHUWM7Oi8gTkznKBNzOzjtibytsbGvj8A9IaqbP0gsIVeEn9ktZKnWOs5knTZku++mRmthKVqH8FuGMDKh//peRbxG1WiAIvKZN0lqQ5QB2YK2mRpFmSDkmdbzQ1VXadTPUT20bUU2cxM+sGGbULAj39KrITU2cpu0IUeOAi4PnAnsBU8lwbA4cCR0g6MmG2Ec2Wsj763hPExamzmJl1i4iIKvVTgrhhoeR5yW1UlAK/O3B4RPw5IhZGbn5EzASOoYDbOk4n2zfgzixqs1JnMTPrJhERlaj/ZnLEY6mzlFlRCvwsYJdRntsLKNQvwePSFMEeDeoevZuZrSZJelya8kepkjpLGRVlksPJwNWSjgXuBRYA04DnkWd8Q8Jsz9Dc4/0d/o00M1t9ERGLpWkvIruyoeyTlaj/NnWmMilEgY+IWyXtAOwIbAlsSD5qvwS4OSLGtPugpEOBA0Z5+tnAfeNPa2ZmrbJGxAN1VT/Sh86uq3piFrW/ps5UFoUo8AARMQjcuOzfkgaAbKzFvfkzLgcuH+k5SecDG4w351xp6tPQ2DRi8Xh/lpmZQRa1OxrKzupDZy6WjlkjYk7qTGVQlHvwI3kLcF7qEMM1lL1oCtnn74FG6ixmZmVSifrvBqkf0U9lI/cWaY1CjOAl3Q2su9zhDOiX9BbgOxFRgPXwfe9dytLLdo5YkjqJmVnZTI54FHh029RBSqIQBR44BLgCuAq4snlsb/J78icAixLl+qeaKrv2MWHpQDRuXPmrzcxsdc2TpvVDZUrE46mzdLNCXKKPiF8DLwGmk1+WXwQ8DiyMiPsj8X/kP0qVPibsOwSfSZnDzGx1SZom6UJJP5d0q6RjUmcazVoR89cgO7Ku6umSlDpPtypEgQeIiAURcTDwDeBmYNfEkf7pxRGNSgweUY3BO1NnMTNbVcr7vs8CHoqI15FfHX2RpHcmDbYC11E/S8SkOtUPpM7SrQpT4JeJiG+Sd7ZbF3g4cRwzszJ4DfCjiPgE/HPV0jHA/klTrcCMiKWP0Pgo8NyGsnekztONinIP/l9ExAPAG1PnAHhMmgzgvd7NrItVgeWXni0GJibIMmabRiy+Rnrvm8me+5S0ru/Jr5rCjeCLZLG06VpkXxP4HpCZdbM/ADtL2n7YscOAJxLlAf65Pfg7JH1E0pGSJiz/mhkRS7Oo3e7ivupc4FdgAtkRQ3B1szWtmVlXinxTl+OBWyV9XNLngB2At6bK1Jw8dyPwQmAm+Y6id0sa8arCPVK1Jk1/QFqjgzG7mgv8KOqqvlDw7L9Q//ZYv0fSAZK+LOkqSaO1zDUz67iI+BP53KYbyZckHx4R9YSRDgYejYjjIuLGiHgf8AXgiJFePD2i1kdlyoZkVw9Kz+po0i7lAj+6t4uhi14cMaaudZJOBY4DTgE+Buwi6ag25jMzWyUR8URE/CIiZkbEUOI4mwCfXe7YD4HNRvuGStRvXcLQhf1kn1word/WdCXgAj+KLGon9EfjlrG8VtLGwH7AS5vr9v8XOBLYS9KG7cxpZtal5gKvXO7Yns3joxqIxo1D8PUq2TlzpaltS1cChZxF34WmAr+OiKXLDkTEEkmPAJPTxTIzK6wrgdubg6DLgZcDHwXWXtk3ZlG77inpVxNhTUlPrcqmZL3EI/jlPCRNnCdNW8Vv+zuwfnPLWwAk7Qj8O/CPVuYzMyuDyHfk3IZ8G+93AOsDmzbX6K/UlIjHByL+7uI+Oo/gh5knTVuP7CsN6u8F5o/1+yLiaUmnAH+StGyCyFHAbuFtZc3MRhT5xl3j2jW0Jk0PaAxE3N+iWKXhEfwwk6m+M4gb1ohY5VF3RNwGbAQsbD72iIi/tDqjmZn9nzkwZwLVExrK3pM6S9F4BN+0WNq0QvbaJ2kcuPy+tWMVEQ8DX2tlLjMzG930iNpc6T+nkH22ruoTWdSuS52pKDyCb6pQfdtS9AU3tTHrPEkD8oxoW01rRyyoUT++D942qMouqfMUhQt8U38Mfroag99PncOsR72Fcd6Ltd42OeLRX1M/YClL/jpbylLnKQJfojezjpJ0N3lHteEyoF/SW4DvRMQhnU9m3W7nfNLew9umDlIQPT+Cf0Ba4ylpndQ5zHrIIcBjwPnA9s3HR4Drml9/KF00K4OnpHWWqLLj36SB1FlS6ukCP1vKNiD7UpVso9RZzHpFRPwaeAkwnfyy/CLgcWBhsxOkdw2zcZkS8cRSJqyzCdkXn5TWTJ0nlZ4u8NPJ3hJwZxa1WamzmPWSiFgQEQcD3wBuBnZNHMlKphqDP4D46SSqn+zVHeh6tsDPk6b1wVuXUr8sdRazXhUR3wR2J78n/3DiOFYylahfKbhzfbLTrxlhr/my69lJdpOp/EcQ161OUxsza52IeAB4Y+ocVk79MXjeA9IaM4btFdIrerbAV6L+pdQZzOxfSXoOMDEibh3Daw8FDhjl6WeT9zg3Y9MebRneswXezAppBrAFcOjKXhgRl5PvQvYMkvYH1mptNOtm10gT9qH/lUtZ8kA1oic+/PXcPfjZUrZQWj91DjMDSf2S/lmII+KMiFhpcTdbVTMili5mye19ZGfVVNktdZ5O6LkCvzXVC/vpf0HqHGa9SlIm6SxJc4A6MFfSIkmzJLnBjbXNlIgnllD/0AT6jmwoe2nqPO3WUwV+BlofGBqIxi9SZzHrYRcBzwf2BKaSvw9tTH5Z/ghJRybMZiW3RsScIThJ6CN1VZ+bOk879VSBfyf9WwVxceocZj1ud+DwiPhzRCyM3PyImAkcA+ydOJ+VXBa1v/ZHbe9PUL8rdZZ26qkCfxtDc93Uxiy5WcBoO37tRd7G1qztTokYSp2hnXpqFv1HWHrXialDmNnJwNWSjgXuBRYA04Dnkb8nvSFhNusxdVWfGwxtMZMlP29uVlMaPVXgzSy9iLhV0g7AjsCWwIbko/ZLgJsjIhLGsx6TRe2OhrJdX0nl9X+UPvTiiEbqTK3iAm9mHRcRg8CNqXOYAVSi/tklqp68HdlHJZ1alg+ZPXUP3szMbCTXUT8TqDTITk6dpVU8gjczs57X7FX/kdQ5WskjeDMzsxJygTczMxvmcWlKXdUDBqXNU2cZDxd4MzOzYdaNeCrQ7/vJLqyr2rWtzV3gzczMllONwbuDOL0PPj4obZk6z+pwgTczMxtBJer/A0OfnkD1hAXS2qnzrCrPojczMxtFfzRuAW6ppA6yGjyCNzMzKyEXeDMzszGoqbJ7TQNvSp1jrFzgzczMxuB7LPm5iFcu0cCHUmcZCxd4MzOzMZgRsfTv1E8OeFZD2btT51kZF3gzM7Mxmh5Rm0vtQ4GePajKa1PnWRHPojczM1sFG0Qsogv61nsEb2ZmVkIu8GZm9v/bu/doueryjOPfJzkkAXJBAiQBE8OtLKhWqOACRRZZq2BBbIVakAoFRFuLtAhrWXRVsJQWbUGLRaE3WrlIQaBULQVpLReBFJEViBgjRG4BgomBhFwJSZ7+sXdgGHLOnExmzp6zz/NZa69kfnv2ft8zv3nPO3vPPjPRprXSjFc17uxV0pSqc2mWBh8REdGmcfYzYsOPxjLma6ukqVXn0ygNPiIiYiv0+dUfbEBXj2HMl1+SJlWdzyZp8BEREVtprNd+dyO6dgJjPzFPGlN1PpCr6CMiIjpirNfeBty2X9WJlHIEHxERUUNp8BERER10gTTqFW3z/lc15l1V5pEGHxER0UFfsDe+zPr7QGeewqjpVeWRBh8REdEmSeMknSvpZknXS9oPYGd75YusO3M7NLqq3NLgIyIi2iBJwH3AZOBM4GLgckmHQPGRtlew4amq8kuDj4iIaM8xwM9s/6ntRbYfAj4N/EnFeQE92OAl9Ul6S9V5REREtDARuKdpbAHQEz2sJxq8pDGSLpK0EFgHvChplaRHJZ1WdX4RERGbMQ84RtL2DWO/DfyionzeoFc+6OYyYCrwAeAJYBXFK6P9gEsljbN9RYX5RUREvIHtOZJuBZ6X9ClgD+Ag4CPVZlboiSN44EjgD23Ptb3SheW2ZwNnAR+qOL+IiIg3KQ8+jwQmAE8CJ7j4vvjK9coR/KPALODfNrPuGGDJ0KYTERExOLYfAB6oOo9mvdLgzweuk3Q28HPgZWASsC9FjkcPZiflnyz0d1ZCHcgzIiJiWOiJBl++j3EAcAgwk+L9+CXAFcA9tj3IXX2M/t/72B2Y3zggabsy5jbA/9letuXZR0TUh6R3AtOBxbZ/WHU+0b6eaPAAttcCdzaPSxotqc/2K4PYx5XAlZtbJ+kEGv50QdJOwD8BjwAbgNsk7W17QZs/QkTEsCbpXODXKU43XyDpXttnVZxWtKknLrKTNF3S1ZJWSvpvSXs1rP5d4JoOxxsN3AtcYfvPbV9IcQ3A3zT9uUNExIgg6Sjgc8BHbX8FOBDYXdLx1WYW7eqJBg+cDSyieELNBu6R9CtdjLcLxacP3bFpwPZdwHMU7/tHRIw07wGOs70eoHxr9C+B91aaVbStV07RHw0cYHsNcL6kecD3JB3apXirgbHlqf/1DeP7ACu7FDMiopetBqY0je1K8bkkMQz1yhH8PIqjdwBsX0/x4Te3UXyIf0fZXg7cDlwiaUL5bUDXA2tsz2+xeUREHV0LfFLSuwEkHQTcAny90qyibb3S4P8euLG8wAOA8j2gm4G/7VLMrwIvAN8G/oPiYrsPdylWRERPs70QOBX4K0l3ULx1eoDt5ypNLNrWE6fobd8haU+Kj/lrHL9A0t3Anl2IaeBL5RIRMeLZfhI4ouo8ojN6osEDlB/t9+PNjN8F3DXU+URERAxnvXKKPiIiIjooDT4iIqKG0uAjIiJqKA0+IiKihtLgIyIiaigNPiIioobS4CMiImooDT4iIqKGVHygW/1J2h+4FZgzyE3GUXy70uquJTWw7anuSx6qjL0d1T3m4yg+bGlpl+PsARyRjwDtntT7sImdeu+iEdPgt5SknYDLbVfyXciS7rQ9awTG/jZwku0VFcS+FPgX23OHOnZUK/Weeq+jnKKPiIiooTT4iIiIGkqDj4iIqKE0+IiIiBpKg4+IiKihNPiIiIgayp/J9UPSKGAn24srij/N9qIRGHsKsNgVPDElTQZW2F431LGjWqn31HsdpcFHRETUUE7RR0RE1FAafERERA2lwUdERNRQGnxEREQNpcFHRETUUBp8REREDaXBR0RE1FAafMUkbTMSYyfT2iMAAAibSURBVEeMRKn3GEpp8ICkAyU907TsVq47XNJ9khZJuknS+A7GPRaY3TTWbzxJn5X0U0kLJZ3ThdhfaHoMvtPJ2JL6JP21pLnlcuGmXzqS3iLpW5KelfRjSe9q2G6r56BF7ErmP6qRen9tLPVe93q3PeIX4JPAdcDbGpbRwGTgeeAQYFvgCuAfOhBvB+CLwCLgoYbxfuMBvwP8ENgFmAn8BPiNTsUu130fOKnhMZja4dinA7cC48vlduD0ct31wEVAH3AM8BwwrlNz0CL2kM5/lmqX1Ptr61LvNa/3HMEX3gncAxjYaPtp2xuAA4H5tmfbXgNcAhzfgXhHUDyxT28aHyjeUcC/2l5s+yngmjZz6S82FI/D7cBE4EXbL3Q49lzgLNsrba8E5gCHluuOBr5ie73t/wReAN5H5+ZgoNhDPf9RrdR7IfVe83pPgy/sD3wauBt4RtJF5fgMild0mywCdpA0ZmuC2b7R9meA1U2rBoq3uXW7dCq2pLdSFPrdwP8ACyUdPkBe7cR+0PaCMt52wInAf0naARhr+5cNd3+hjNGROegvdrl6SOc/Kpd6T72PiHpPgy/8BPiU7d2Bg4GzVXzT0E7Aqob7rSn/7db7MgPF27lp3eoO5zEeuAE4DNgNuAz4bLmuo7El9QHXAnNt38ibf27K2+M3s26r5mAzsaF35j+GRq/Md+r9dan3LuirOoFeYPvjDf9/QNJs4FhgKfCOhruOB14BXupSKgPF+yUwoWHdRIpXmB1hez5w8qbbki6jeFU/uZOxy4K7nuK9waPL4aVN+2+MYTo0B/3E7qX5jyHQQ/Oden9zjNR7B434I3hJ4ySd03QaZjSwnOLCjxkN4zOAZ2136zt2B4r3HDC9Yd10YGGnAkt6u6QPNwyNAl6lePXekdgqvnP7GxRHCB+0vRbA9kvAOknTGu4+o4zRkTnoL3aPzX90WY/Nd+r9dan3buj0VXvDcQHuBU4p//+rFE/yiRRXdS6mOI3TR3FV5Zc7GPdw3nhVbb/xKK40fZDiSs9pwDzgkA7GngEsoygIAZ8HvtPJ2MAZFBe/TKIoqtHAqHLdVRRX+44CjgQeB8Z2ag5axK5k/rNUs6TeU+8jpd4rT6AXFuA95aTPpTgdc2rDuhOBl4EngP8FJnQw7huKbqB4ZSFcDSyheKV7QRdin1MW8+PAo8C+nYwNPElxCq5xubVct0cZ+0lgATCrk3PQInYl85+lmiX1/tpY6r3m9a7yhwpA0o7AMtsbm8b7KCZ6SN6LGSiepInAK7Zf6VJsAZP9xitchyR2GWNn20s2M971OeiV+Y+h0SvznXpPvXdLGnxEREQNjfiL7CIiIuooDT4iIqKG0uAjIiJqKA0+IiKihtLgIyIiaigNPiIioobS4CMiImooDT4iIqKG0uAjIiJqKA0+IiKihtLgIyIiaigNPiIioobS4CMiImooDT4iIqKG0uAjIiJqKA0+IiKihtLgIyIiaigNPvqlwsSq84iI7ku9108afA+SNEGS+1nubrHtVElnDCLGpZLO62fdLpJuAhYBP5U0W9LJ7f00W5ZXt7aP6FWp985vH4U0+B5kewXQVy6HAksabs9qsfks4Kh2Y0uaAtwLzAX2tr0b8MfAVyUd1u5+tzavDmwf0ZNS713ZPkiD71m2N9jeAGxovG17I4Ck90m6WdKLkm4pX4XvClwCHC7pmvJ+vy9pnqSVkh6SdGCL0EcDC23/RfmLB9s/Ao6h+MWDpF0lXSxpkaRHJM0qx98h6aryaGGppDnl2BblJelcST+X9LCk05q3lzRa0pWSlkl6StJnOvSwR1Qi9Z567wrbWXp4AQ4GFjeNvQ1YDny8/P+3gNsoXrD9AfA9YBKwJ7ASOAjYCbgSuL3cx6XAeZuJ93Xg4hY53QlcDcwAzgBWAFPLOBuAC4G3Av8IfHcL8/oI8BhwCPB2YDEws2n74ymOOnYu77MC2KvqucqSZWuX1HvqvZNLjuCHp2OBebb/2fbTwOeB3wQmUzz519leDiwFDrX9ILAKeAiY1mLfU4Fl/a0sX10fDvyZ7WdsXw48ARxX3mUFcL7tZ4HrgN1dHIUMNq/jgG/anm37UeBDwLqm7V3muT8wH5hqe0GrBy1imEq9p97bkgY/PM0E7t90w/ZjFEW0W+OdbC8D3i/pYeBZ4DRgdIt9zwP2ah6UtKOk8WXshbYXNqy+vyH2L1y+NKd41d7XvK8Wee0DPNBw3/ttP9+0i3+nOFK4CXgeOE/Sm+JE1MRMUu+p9zakwQ9PSykKAyiuggV2BJ5uvJOk3wM+AfwRMAW4AFCLfT8CvFfS2KbxvwO+CbwITJM0oWHdvg2xTQst8lpOcUpv030PlrRP0y5McRQzheKCoJOAU1rFjRimUu+p97akwQ9P3wcOkzSzvH0SsMD2S8BqYPtyfFdgbnn6az1wIrBNi33fArxAcRXtBEl9ko4FTqB4v24BxavwjwJI2ht4N/Bgi/0ONq87gQ9KGle+Sv8GsG3T9h8DbrC91vYNwByg+RdURF2k3lPvbUmDH4Zs3w98CXhU0uMUp7x+q1z9CPBrku4HrgWmS3pA0kMUF7NMk7TtAPveQHHhyzSK4l4EnA2cavuOskBPBj4n6THgB8BZtue0SHuweV0CrAeepPjTnZtsP9y0/VXARknzJf2M4pfFdYN8+CKGldR76r1dev3tkxhuJG0DTLS9tGl8FDDW9pry9mTgpfLily3Z/6hy/5u9CKc8VbjEg3wSbUlekiYBa2yvG2D7ccAY2y9vyc8VMRyl3lPvWyoNPiIiooZyij4iIqKG0uAjIiJqKA0+IiKihtLgIyIiaigNPiIioobS4CMiImooDT4iIqKG0uAjIiJqKA0+IiKihtLgIyIiaigNPiIioobS4CMiImooDT4iIqKG0uAjIiJqKA0+IiKihtLgIyIiauj/Ad1W1M/ZPJ/NAAAAAElFTkSuQmCC" alt="plot of chunk unnamed-chunk-1"/> </p>
<pre><code class="r">
# Descriptives for friends / non friends
# Text vs calls : Exclude zero values
callFreqfriends <- table(dat$callFreq, dat$friend)
par(mfrow=c(1,2))
datSub <- subset(dat, dat$callFreq > 0 ) # Exclude zero values
boxplot(log(datSub$callFreq + 1) ~ datSub$outcome, ylab="Call Frequncy", main = "Calls (zero values excl.)")
datSub <- subset(dat, dat$textMsgs > 0 ) # Exclude zero values
boxplot(log(datSub$textMsgs + 1) ~ datSub$outcome, ylab="Text Frequncy", main = "Text (zero values excl.)")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-1"/> </p>
<pre><code class="r">
# Text vs calls
par(mfrow=c(1,2))
plot(dat$callFreq , dat$textMsgs , xlab = "Text messages", ylab = " Calls") # share friends
abline(lm(dat$textMsgs ~ dat$callFreq), col = 'red', lty = 2)
plot(log(dat$callFreq +1) , log(dat$textMsgs + 1) , xlab = "Text messages (log)", ylab = " Calls (log)") # share friends
abline(lm(log(dat$textMsgs + 1) ~ log(dat$callFreq + 1) ), col = 'red', lty = 2)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-1"/> </p>
<pre><code class="r">
# Text to call ratio
par(mfrow=c(1,1))
dat$text2cal <- dat$textMsgs / (dat$callFreq + 1)
boxplot( log(dat$text2cal + 1) ~dat$outcome, ylab="Text / call ratio (logged)", main = "Text / call ratio (logged)")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-1"/> </p>
<pre><code class="r"> par(mfrow=c(1,3))
hist(log(subset(dat, dat$vote == 0)$text2cal + 1), main = "Other", xlab = "Text to call ratio (logged)")
hist(log(subset(dat, dat$vote == 1)$text2cal + 1), main = "Friends", xlab = "Text to call ratio (logged)")
hist(log(subset(dat, dat$vote == 2)$text2cal + 1), main = "Family", xlab = "Text to call ratio (logged)")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-1"/> </p>
<pre><code class="r">
# People without any contacts
dat$textMsgsBinary <- ifelse(dat$textMsgs > 0, 1, 0)
textBinary <- table(dat$outcome, dat$textMsgsBinary)
textBinary <- cbind(textBinary, (textBinary[,2] / (textBinary[,1] + textBinary[,2] ) ))
barplot(textBinary[,3], main = "Text", ylab = "Fraction with at least one text msg")
dat$callFreqBinary <- ifelse(dat$callFreq > 0, 1, 0)
callBinary <- table(dat$outcome, dat$callFreqBinary)
callBinary <- cbind(callBinary, (callBinary[,2] / (callBinary[,1] + callBinary[,2] ) ))
barplot(callBinary[,3], main = "Calls", ylab = "Fraction with at least one call")
# What does it mean if you text and not call and vice versa
par(mfrow=c(2,2))
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-1"/> </p>
<pre><code class="r"> # no text and no call
dat$noTextnoCall <- ifelse(dat$textMsgsBinary == 0 & dat$callFreqBinary == 0, 1, 0)
temp <- table(dat$outcome, dat$noTextnoCall)
temp <- cbind(temp, (temp[,2] / (temp[,1] + temp[,2] ) ))
barplot(temp[,3], main = "No Text & No Call", ylab = "Fraction of users")
rm(temp)
# Call and no text
dat$noTextCall <- ifelse(dat$textMsgsBinary == 0 & dat$callFreqBinary == 1, 1, 0)
temp <- table(dat$outcome, dat$noTextCall)
temp <- cbind(temp, (temp[,2] / (temp[,1] + temp[,2] ) ))
barplot(temp[,3], main = "No Text, but Call", ylab = "Fraction of users")
rm(temp)
# Text and no call
dat$TextNoCall <- ifelse(dat$textMsgsBinary == 1 & dat$callFreqBinary == 0, 1, 0)
temp <- table(dat$outcome, dat$TextNoCall)
temp <- cbind(temp, (temp[,2] / (temp[,1] + temp[,2] ) ))
barplot(temp[,3], main = "Text, but No Call", ylab = "Fraction of users")
rm(temp)
# Text and call
dat$TextCall <- ifelse(dat$textMsgsBinary == 1 & dat$callFreqBinary == 1, 1, 0)
temp <- table(dat$outcome, dat$TextCall)
temp <- cbind(temp, (temp[,2] / (temp[,1] + temp[,2] ) ))
barplot(temp[,3], main = "Text & Call", ylab = "Fraction of users")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-1"/> </p>
<pre><code class="r"> rm(temp)
# Correlations between text and calls
par(mfrow=c(1,2))
plot(dat$callFreq, dat$textMsgs, xlab = "Calls", ylab = "Text msgs")
abline(lm(dat$textMsgs ~ dat$callFreq), col = 'red', lty = 2)
plot(log(dat$callFreq + 1), log(dat$textMsgs + 1), xlab = "Calls (logged)", ylab = "Text msgs (logged)")
abline(lm(log(dat$textMsgs + 1) ~ log(dat$callFreq + 1)), col = 'red', lty = 2)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-1"/> </p>
<pre><code class="r">
dat$starred <- ifelse(dat$starred == 1, 1,0) #fill in missing false values
dat$hasPhoto <- ifelse(dat$hasPhoto == 1, 1,0) #fill in missing false values
# Descriptive statistics
summary(dat)
</code></pre>
<pre><code> areaCode vote lastCallDateTime callFreq
Min. : 11 Min. :0.000 0 :1030 Min. : 0.0
1st Qu.:408 1st Qu.:0.000 5/24/13 10:26: 3 1st Qu.: 0.0
Median :502 Median :1.000 6/3/13 8:16 : 3 Median : 0.0
Mean :565 Mean :0.638 5/2/13 10:01 : 2 Mean : 3.5
3rd Qu.:800 3rd Qu.:1.000 5/21/13 21:54: 2 3rd Qu.: 0.0
Max. :980 Max. :2.000 5/24/13 21:07: 2 Max. :730.0
(Other) : 236
textMsgs talkTime starred hasPhoto
Min. : 0.0 Min. : 0.0 Min. :0.0000 Min. :0.00
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.:0.0000 1st Qu.:0.00
Median : 0.0 Median : 0.0 Median :0.0000 Median :0.00
Mean : 7.6 Mean : 3.7 Mean :0.0563 Mean :0.28
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.:0.0000 3rd Qu.:1.00
Max. :1206.0 Max. :754.0 Max. :1.0000 Max. :1.00
isFirstName callRecency areaCodeDist gender originAreaCode
Min. :0.000 Min. :0.000 Min. :0.000 F:394 Min. :408
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.301 M:884 1st Qu.:408
Median :0.000 Median :0.000 Median :1.982 Median :408
Mean :0.187 Mean :0.477 Mean :1.479 Mean :523
3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:2.386 3rd Qu.:612
Max. :1.000 Max. :2.562 Max. :2.857 Max. :925
testSubject friend family outcome
Min. :1.00 Not Friend:677 Min. :0.0000 Other :570
1st Qu.:2.00 Friend :601 1st Qu.:0.0000 Friend:601
Median :5.00 Median :0.0000 Family:107
Mean :4.71 Mean :0.0837
3rd Qu.:7.00 3rd Qu.:0.0000
Max. :9.00 Max. :1.0000
text2cal textMsgsBinary callFreqBinary noTextnoCall
Min. : 0.0 Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.: 0.0 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
Median : 0.0 Median :0.000 Median :0.000 Median :1.000
Mean : 2.3 Mean :0.143 Mean :0.195 Mean :0.747
3rd Qu.: 0.0 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:1.000
Max. :326.0 Max. :1.000 Max. :1.000 Max. :1.000
noTextCall TextNoCall TextCall
Min. :0.00 Min. :0.0000 Min. :0.0000
1st Qu.:0.00 1st Qu.:0.0000 1st Qu.:0.0000
Median :0.00 Median :0.0000 Median :0.0000
Mean :0.11 Mean :0.0587 Mean :0.0845
3rd Qu.:0.00 3rd Qu.:0.0000 3rd Qu.:0.0000
Max. :1.00 Max. :1.0000 Max. :1.0000
</code></pre>
<pre><code class="r">
#-------------------
# Subset the data set
# Exclude family since they will be excluded with other rules
#--------------
dat <- subset(dat, dat$family == 0)
#--------------
# Baseline model
#---------
M0 <- with(dat, glm(vote ~ 1
, family=binomial("logit")))
summary(M0)
</code></pre>
<pre><code>
Call:
glm(formula = vote ~ 1, family = binomial("logit"))
Deviance Residuals:
Min 1Q Median 3Q Max
-1.20 -1.20 1.16 1.16 1.16
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.0530 0.0585 0.91 0.37
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1622.5 on 1170 degrees of freedom
Residual deviance: 1622.5 on 1170 degrees of freedom
AIC: 1625
Number of Fisher Scoring iterations: 3
</code></pre>
<pre><code class="r">M0_likelihood <- with(M0,
y * log(fitted.values) + (1 - y) * log(1 - fitted.values) )
mean(M0_likelihood)
</code></pre>
<pre><code>[1] -0.6928
</code></pre>
<pre><code class="r">
#--------------
# Add features
#----------------
# callFreq + callFreqBinary
# log(textMsgs + 1) + textMsgsBinary
M1 <- with(dat, glm(vote ~ textMsgs + textMsgsBinary +hasPhoto + starred
, family=binomial("logit")))
summary(M1)
</code></pre>
<pre><code>
Call:
glm(formula = vote ~ textMsgs + textMsgsBinary + hasPhoto + starred,
family = binomial("logit"))
Deviance Residuals:
Min 1Q Median 3Q Max
-2.32 -1.02 0.26 1.34 1.34
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.37667 0.07481 -5.04 4.8e-07 ***
textMsgs 0.01124 0.00723 1.55 0.12
textMsgsBinary 1.23445 0.26968 4.58 4.7e-06 ***
hasPhoto 0.70342 0.13962 5.04 4.7e-07 ***
starred 2.28631 0.53238 4.29 1.8e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1622.5 on 1170 degrees of freedom
Residual deviance: 1490.2 on 1166 degrees of freedom
AIC: 1500
Number of Fisher Scoring iterations: 6
</code></pre>
<pre><code class="r">M1_likelihood <- with(M1,
y * log(fitted.values) + (1 - y) * log(1 - fitted.values) )
mean(M1_likelihood)
</code></pre>
<pre><code>[1] -0.6363
</code></pre>
<pre><code class="r">
dat$fitted <- ifelse(M1$fitted < 0.5, 0, 1)
table(dat$fitted, dat$friend)
</code></pre>
<pre><code>
Not Friend Friend
0 428 294
1 142 307
</code></pre>
<pre><code class="r">
#--------------
# No text data
#----------------
# callFreq + callFreqBinary
# log(textMsgs + 1) + textMsgsBinary
M1 <- with(dat, glm(vote ~ hasPhoto + starred
, family=binomial("logit")))
summary(M1)
</code></pre>
<pre><code>
Call:
glm(formula = vote ~ hasPhoto + starred, family = binomial("logit"))
Deviance Residuals:
Min 1Q Median 3Q Max
-2.428 -1.089 0.328 1.269 1.269
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.2129 0.0704 -3.03 0.0025 **
hasPhoto 0.7137 0.1363 5.24 1.6e-07 ***
starred 2.3923 0.5269 4.54 5.6e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1622.5 on 1170 degrees of freedom
Residual deviance: 1553.7 on 1168 degrees of freedom
AIC: 1560
Number of Fisher Scoring iterations: 5
</code></pre>
<pre><code class="r">M1_likelihood <- with(M1,
y * log(fitted.values) + (1 - y) * log(1 - fitted.values) )
mean(M1_likelihood)
</code></pre>
<pre><code>[1] -0.6634
</code></pre>
<pre><code class="r">
dat$fitted <- ifelse(M1$fitted < 0.5, 0, 1)
table(dat$fitted, dat$friend)
</code></pre>
<pre><code>
Not Friend Friend
0 450 364
1 120 237
</code></pre>
<pre><code class="r">
</code></pre>
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