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@psinger
Created December 10, 2015 10:17
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading required package: Matrix\n",
"Loading required package: grid\n"
]
}
],
"source": [
"library(lme4)\n",
"options(rgl.useNULL=TRUE)\n",
"library(LMERConvenienceFunctions)\n",
"\n",
"library(ggplot2)\n",
"options(jupyter.plot_mimetypes = 'image/png')\n",
"library(repr)\n",
"options(repr.plot.width=6, repr.plot.height=6)\n",
"\n",
"library(car)\n",
"library(MASS)\n",
"\n",
"library(vcd)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Body length\n",
"\n",
"In this notebook, we document our various steps taken to study the effect of the course of a session on the body length (number of characters). Note that we only do this on a sample of 1 mio. data points here."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#no zeros in the outcome variable\n",
"data = read.csv(\"/home/psinger/Reddit-depletion/data/sample.csv\", header=TRUE)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"1000000"
],
"text/latex": [
"1000000"
],
"text/markdown": [
"1000000"
],
"text/plain": [
"[1] 1000000"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nrow(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data inspection\n",
"\n",
"We start by getting a better feeling of our data. Note that body_length represents count data."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"qqp(data$body_length, \"norm\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As can be seen, the data cannot be well explained by a normal distribution."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"qqp(data$body_length, \"lnorm\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Even though not perfect, the log-normal distribution captures the data quite reasonable if we take the large-scale of the data into account.\n",
"\n",
"When working with count data, one should also look at the Poisson and negative binomial distributions though."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"poisson <- fitdistr(data$body_length, \"Poisson\")\n",
"qqp(data$body_length, \"pois\", poisson$estimate)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nbinom <- fitdistr(data$body_length, \"Negative Binomial\")\n",
"qqp(data$body_length, \"nbinom\", size = nbinom$estimate[[1]], mu = nbinom$estimate[[2]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Both the Poisson and negative binomial distributions do not really fit the data well. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To confirm this, we take a look at the so-called <a href=\"http://www.jstor.org/stable/2343403\">Ord-plot</a> that allows a quick graphical test regarding the model choices for discrete data."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Ord_plot(data$body_length)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given these results, we can check the <a href=\"http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/ordplot.htm\">interpretation table</a> and based on that the intercept is negative and the slope is positive, we might assume that the data can best be modeled by a log-normal distribution confirming what we found above."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Regression model\n",
"\n",
"We use mixed-effects models where in the most basic form (that we analyze in this notebook), we are interested in studying body_length ~ 1 + session_index + session_comments + (1|author)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Linear mixed-effects regression\n",
"\n",
"Based on our data analyses above we cannot assume that a simple linear regression reasonably models our data. Nonetheless, we present the procedure next and check the assumptions."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"m_lmer = lmer(body_length~1+session_index+session_comments+(1|author), data=data, REML=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Linear mixed model fit by maximum likelihood ['lmerMod']\n",
"Formula: body_length ~ 1 + session_index + session_comments + (1 | author)\n",
" Data: data\n",
"\n",
" AIC BIC logLik deviance df.resid \n",
"14190724 14190783 -7095357 14190714 999995 \n",
"\n",
"Scaled residuals: \n",
" Min 1Q Median 3Q Max \n",
"-7.629 -0.416 -0.252 0.083 32.629 \n",
"\n",
"Random effects:\n",
" Groups Name Variance Std.Dev.\n",
" author (Intercept) 12440 111.5 \n",
" Residual 74413 272.8 \n",
"Number of obs: 1000000, groups: author, 527435\n",
"\n",
"Fixed effects:\n",
" Estimate Std. Error t value\n",
"(Intercept) 181.42278 0.37822 479.7\n",
"session_index -1.18393 0.12509 -9.5\n",
"session_comments 2.28614 0.08257 27.7\n",
"\n",
"Correlation of Fixed Effects:\n",
" (Intr) sssn_n\n",
"session_ndx -0.167 \n",
"sssn_cmmnts -0.172 -0.755"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(m_lmer)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mcp.fnc(m_lmer)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What we can see in above plot is, that the residuals do not appear to be normally distributed and we can see clear heteroskedasticity. Thus, as expected, a linear model does not provide a good fit here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Linear mixed-effects regression (log-transform)\n",
"\n",
"However, as our data looks log-normal, let us take the log of the response variable and repeat a linear regression."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"m_lmer_log = lmer(log(body_length)~1+session_index+session_comments+(1|author), data=data, REML=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Linear mixed model fit by maximum likelihood ['lmerMod']\n",
"Formula: log(body_length) ~ 1 + session_index + session_comments + (1 | \n",
" author)\n",
" Data: data\n",
"\n",
" AIC BIC logLik deviance df.resid \n",
" 2948155 2948214 -1474073 2948145 999995 \n",
"\n",
"Scaled residuals: \n",
" Min 1Q Median 3Q Max \n",
"-4.0579 -0.6176 -0.0097 0.6076 5.0837 \n",
"\n",
"Random effects:\n",
" Groups Name Variance Std.Dev.\n",
" author (Intercept) 0.2539 0.5039 \n",
" Residual 0.9091 0.9535 \n",
"Number of obs: 1000000, groups: author, 527435\n",
"\n",
"Fixed effects:\n",
" Estimate Std. Error t value\n",
"(Intercept) 4.5884874 0.0014146 3244\n",
"session_index -0.0039075 0.0004410 -9\n",
"session_comments 0.0119275 0.0002972 40\n",
"\n",
"Correlation of Fixed Effects:\n",
" (Intr) sssn_n\n",
"session_ndx -0.158 \n",
"sssn_cmmnts -0.176 -0.739"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(m_lmer_log)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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2ucw90HsAfwL+TlxSv5MYi1whlq5chlja+xSihvDJacJUC6xKMSZBvUyclE2ks8uJSwNZP/86nrgC18izHYpFUmtl7wEeiKRyB2CLSqy+rMTKnEB/kRj3syczD9N4mEh0TgEOJxZbMYEur9UoTl3vK+lLWKQiWA04lai20Z/hVCGSlE4GsDNwUnTePA9sbW3n4ihzAr0M8EsGH+N8CbBX+8NRm4whSgkekjqQ3LXAJ4mrGtMTxyIB/Iq4nPtpYmy0pHLL5gCmwAnAjsD/YW3nwilzAn0rsB3wF2JcaiOjiYPPsmPltRxRhaMIQzggSobNS8R1Z+JYJIja5HvhZEGpG2SrADfH5MB1iNrONySOSQ2UOYH+HnAefbPM7wBeyO8bTyQ42xEfLlumCFAtsRLRq/ZM6kByjxCX0FbDBFrFcDfwRuogJI1MBbL9gF9EScqbgM9b27m4ypxAXwRsAhwEfJeZJ89MA84Hvgxc0dnQ1EKrUrwrCDcQC1SckDoQiRjKdiAxXO3VxLFIGrpsQaIqwmYwBfiCtZ2Lr8wJNERifAWxDvxCwIL59ieJWtBTEsWl1lkRuD11EHVuBjZOHYSUm5foQHgAuIC4QlI/VvLgTgclqSnZ5sAF8b59HNi5AvckjklNKHsCXTWFWDTlvtSBqOVWIYrGF8kNxJWNMcDUxLFI++RfXwTe388+KRLoMcCmwLJEHffzmfn9shKwFnBsZ0OT0hsL2c+A/eKK+e+AH1T6n9OlkroS+DzwjtSBFJSF+tvjHURP2qqpA6mzMBHXcqkD0bCUplB/ic0L/IsZF7a6HXh33X4H0f7KArbPKppsSciuj8VRHs/iRFOhNO3zqCb3e4MYZ/cEsXDJHsSqbFI7rUj0WP0ndSB1HiOGCa2ZOhBpEB8D/pngeb8LrEBM5H5n/vVdwOl0z5VPaTiyXYD74eU1I59apQIXpw5KQ9dsQ7Yp0Rv4cWIlnD8CRwPnEmXk/oazwNV6KwL3Usxj60YigXaih1KbABxGXKmp7xRZghg+0WlbAYcSCTP51yeJOSufIzpkRmph+ua9DKSCC8moAOaE7DfA7vAW1nbuWe8kxt1dSlwee4lIqjej/6Vku5mXCNvjGIq7guT/ETWhVT6luUTYpD8Rpa5+Q8wDuZVIqG8ilvCuHzbRCa8Svc71fkXENCH/eSRDOG5jxiEiA92OGeZzSC2xKmR3x5CN+zJYI3U8BVaa9rnZIRy1KsCixNn//PnPo4iC3xcC1xE9h9JIrUh8SBbR9cQEx3GpA1HP2xw4nJincgCRoB5ITCi8F9g6QUwPAB9qsP1bRI3b4xne50+tlejrXR7olhHVDaSOGwXZ1yC7BlgmJsSvVokrmOoRsxCN9G+AR4kG6Rng98Slulnz/VYF/k0U9u8l9kC33iiiuspWqQPpx/zE332t1IFoyErTw9Gkt4gVVwHmI6pxVJPTz5BmFc+9ic+Jc4HPAnPV3Lc5MbfhXCKRdhKhutKCkF0Uvc6vZDF3TIPrtvaZl4lG7iHgCGBD+h8//UWKs2pcp9hAt97SxDG3aOpABnAPcbyrXLqtgX6EGG9c9RCwdv79XqRZXGUUMdb5MeJ9vGzd/VsAD9M3xKKdbJ/VcR+C7KlInq/JYPHU8ZRIt7XPfB9YneYmYswNLNDecArHBrr1dqD4J2J/IJaRV7l0WwN9OPA6MWwD4AzgVOKK4KXAHYniqpqPuIpZbwywPrBnm5/f9lkdMw6yIyCbGsnzpMyqM0NVmva52T/seUQj3KinYF5gSfouE76Ma7dr5FYi/Qf/YK4hxnRKKX2DmLy9ev7z14m5KNsz4/COVJ7vZ/tU4Kr8JpXeUpCdBKwZJX/3qMAlqWNS+zSbQF9DlEOa3OC+NYGzgdlbFJMEfePpi+wqYBFgMeKyuZTC68B+NT/fAyxEzPR/ACfQSW23G2S/BuaOsr6frMDTqWNSew2UQO+d36rOJGZP16oA7wHub3Fc0krEZegiuwd4gah2YAKtInmdWEFWUhtlMOcJ8MqOwLiogmNt5x4xUAL9JHBL/v3awF3AKw32uxHHgaq1FiRq16aoHjAU04GrgYn4HlA6zXxYu5CI1GKrQXYv0YMyDtaoFP8zSy00UAJ9Xn6DWJL1IGJcj9RuqxCrD5ahHOI1xAqdUiqfrPu5QpyEbkPUKf9yxyOSulgGo74D074BjIUTgC9UGncwqos1OwZ6/bZGIc1oLeLqx9TUgTThauC7wJxE3Wqp047rZ/uhxJWRDxLVOCSNUAYLXQKP7w+MhR0q8NfUMSmNgRLoC4gk5mBmrDHan4NbEpEU1QSKPoGwqnrJbjXgipSBSHUyIrk+HPhm2lCk8tsCsqeBOYB5YYlK48IK6hEDJdCb19y/TROPZQKtVlmTqGVbBlOAO4l5AibQKppFiIockoYpg3FHwptfAEbDd94J36+U4wqp2migBLp20kn9KlJFMQbYlIjvv8D5zHxQr0QMCTi2s6FpmBYlPvBLUUg9dz0u6a10Dupn+4LEMtqXdzAWqatksPSNcO92wGjYpAL/SB2TimEoK+TMBbyDSFRHERNX3kcM9bi49aENal6i3uI6NdvuIJaIfbRm22bATzGBLot1iNJw96QOZAhuJBavkFLo7+rfNOKqyOc6GIvUNXaH7BXgKWANeGel+KvjqoDWJVaTOjH/+QvE+Lqn86/bJojpSGLFw22JxH5b4DliBa7aE4ODaH9NRpeKbZ0jiCsJZbIacYy9M3UgakpplopVS9g+a0gymPdUyN6I5bj3zywD2UmlaZ9HNbnfT4AHgR/lP38G+AOwAHAy/V9CbKetiMmNpxNnhacDWxOrb9njUl4TiZOgMrmdKLu3ZupA1LPmJq7erJt/X2uWzocjlVMG60yGF1YGZoHVK3CEC6OokWYT6JWBo4nJUvMT44r/TBxUFxBDOTptAeDeum1XA78Fvg1M6HhEGqm5iWOrbJPx3iYq1phAq9PeTwwhegn4F9EGvgTcQFSzmYeoaGOtcmkAGYyaBNlU+Nc5wHth9kp5qkEpgWYT6DeJgvwQ1Tneoq+XcC7SnJ09AHyowfZvEfEeT/P/PxXDesSxdU3qQIbhOqISh9QpnyeS5ncRtch3IComfYNInK8DriXGQqeYpyKVQgbv+gdM2x8YA9vvD5UKvJ46LnWHs4HbgI8SvdDVFQqXJMaqXJ0gpr2JxP1cYqb5XDX3bU5U4ziXSKQdA10O36e8FQN2J+YEqPhKM8ZuAMsRw4ZOZ8a2r2o24Eqi7duqg3EVke2z+pXBh5+G7KoY77xY6njUFe3zDJYFHiYa4xeJSVMA9wOvEaXkOm0UMdb5sTyu+lJ7W9AXswl0OVwNTEodxDAtTRxnS6YORIPqhgb6j8BDzDzeuWp9IsF+mlhquJfZPmsmGYw7IoZsZJMieR5KVTK1Tze0zzMZCyxPXBqs+ijFOGObj8YTZcYQHyR7tvn5baBHbjZi6E2Kk7FWqBCVanZMHYgG1Q0N9C3ALwe4f0VgH2JOyIMdiai4bJ81g7y2c/ZYJM4bp45HMyhN+zyUMcJvA48TifT8+e1q4NX8+5SeJ5KvelOBq4jlbFVsaxPHYxnHP0P0PrugijplaWaeRF3rduB3+dcFOxKRVAIZfOYVuPcJ4F1R2/nS1DGpnJq9ZLEacCoDX54uap3EJYil6+9IHYgGtB7RqzYldSAjcB3wwdRBqCc8QHPDhRbP95V6Wgbz/hVeeJOoNPALGGV5Oo1Eswn0r4ghEp8GnmxfOG1xDJHUDCfBP5QoBTWYCv2PRVRz1iMqCpTZtcBXiYo1byWORd3tFmJy4DeIeSiNjCXmgtzeqaCkIspg3Yfg6uWAWWCFI+DOI1IHpdJrNoFeGdgLOKWNsbTLGQy/9/nhJvfbhCgVpeGpEPVs/5Q6kBG6jjjRXIWSjOFSaR0G7ESMcf4sMWGw1mjgh8S8lb07G5pUDFkMC/y/qfDts4H9oraz5enUUTcREwbVmJNURmY54lLaoqkDaYF7gH1TB6EBlWaSyiC+Qrxv7gcOIE7kJxIJ9fX5fYcmi644bJ97UF7bOXsesm0dqlEmpWmfm+2B/iVwIHAJMWmwSCrEhJpFiNUJRwNPEL3H9+EbpwzWJoYGNdvjX2TXEcsp/yp1IOp6PyVWSvsNcHjdfU8SC6uc1umgpNQy2PIZOG8WYDwsfnqUfJRaqtkEel4iMX2AWLr7KWZOTA9uYVzN2pUYc7pSP/ffSlzGPLVjEWk41qIkZ5xN+Bdp3gvqTf8gauAvTAzXmJUYsvYg0fMq9YwMZjkS3phKnFV+G8ZWohqX1HLNJtD75F9fJMaqNtLppGEPYszs34GfECskPkf0SM8HLANsT4zbHgWc3OH41Lw1gHNSB9Ei/yJqoy9EXAmR2i0DHs1vReMVQnVEBu/9N9yzLTAGNp4E/5yUOCapqG4gEujBqmscTlxWbyfH2A3fOGIC1BapA2mR0cBLwMdTB6J+lWaMXcntSlwFzPq53UIMM2k32+cul8H+r0F2TiyMknpdCo1MadrnoSykAlGNYx/gm8A7iBrLqeo/L0NcvhysB+MSZl7mW8WxAlG54sbUgbTINOLNv27qQKSE9gBOBB4DdgNWJSYJL5Z/vxMx+fGU/HtpyDIY/1fIXoNfHAB8JGo7P5s6LvWGZodwjCVW89ulZtvpxAzv8cDWRK9bJ90KbAf8hVglsZHRxNLKt3QqKA3Z6sBk4JnEcbTSNURFBKlXfRE4HtiTmTs5Hiba5FOIK4RfYnhD7LYixn0PpkJc6VIXyWC9h+CqZYDZYYWj4c6jUwelntJsAn0IMZ54HyJhfTrf/n/A+cC3iSodnfQ94Dyi5/IUYuLMC/l944nSaNsRveZbdjg2NW8Nuqf3ueoaYnLrLDReYl7qdssQ1ZuauUK41zCfY3eaW40RYJ5hPocKJouOsW9Z21ll8RDwg5qf36BvWMQ3SVciZgNi8tlUZh5fNxU4O9+n3RxjN3w3EKupdZPxxFCOdVIHooZKM8ZuiIo0xO5Kom0eO8A+o4le6svbHIvtc5fIYOFLIXsOso87AbVblaZ9brYHegIxZKKRe0k3aP+K/DYnUfVgwXz7k8RM7ymJ4lJzxgErAt9KHUiLvQDcRSxPfk3iWNT9ijjEziuEaqkMtnoWzh0LzAeLndEd6waoB1zHjMss1/ZAH070IvYyeziGZ3WiF+EdqQNpg98QV0BUPKXp4WjSd4C3iBUI56avfV4JeIRY9jsFrxBqxDKY7SjI3oZsUlTZaLbjT+XUbe0z2xON3p+ADYnG+mPEpfe3gU8ki6wYbKCH57PEBMJutCsxGzzVJXT1r9sa6KIOsauak6gFPTG/LZ1v6xTb55LKYIU7IHskEucNU8ejjui29hmIZOdZZuxFeAn4MiYJNtDDcyzw19RBtMnixHukmSoB6qxua6CnMGM95doEenvg1Y5HVCy2zyVUre18SiTP86aORx1TmvZ5KHWgjyJWk1oN2IZYfnlh4Oc4mF/DsybdO/xnMnH5fGLiONT97qT/ccTrEuPxpVLIYPzpeW3nfYAdoVKJVZClQhnqWKK3gduBm9sQi3rLnETvbCnONIfpcmB94HepA1FX+xlwav79H4mOkeWI1TD3BfZOFJc0JBms/zBcuRQwOyx/vCd/Krm1gTOIVaPezm/3AacRPYjyEuFwfICYSNTJsZCd9hnSjz/VzEpziXAIHGLXP9vngstg9KR8ouARMWRj1tQxKZnStM+DDeE4DLiW6EW7gRiu8fP8+w2I/+TP2hmgutZ6xNWMbi41eAV9yxdL7eQQO5VSBgtfBlO/CIyBj+8fQzbeSB2XNBI7Ew3vz4DZG9w/O32N8w4N7u8l9nAM3fnAr1MH0WYVoh75HqkD0QxK08OhlrB9LqgMtn4Gsiui13mR1PGoEErTPg80BvpTwFXEksTTG9z/GvEffX++76kN9pEaGUX0QP9psB1LLiN6oT9ArLgmtcqkNu8vtU0Gs58Ar04jelG+DWMqsZav2HwAACAASURBVHqrVBoDJdCrEYukNEqeq6YDFwH7tTIodb2ViAUf2r2EbxFcBeyfOgh1nT2HuP+kNsQgDVkGK94Ft60PjIZ1JsG1kxLHJA3HQAn0eODRJh7jYWC+1oSjHrEhcDfwVOI4OuFy4EhiLLRLz6pVFk8dgDRU+0P2OjH5ZUeYt9L5JeallhlsEmEzk0+coKKh2oje6H2G+Kx4Gtg4dSDqaqOIyYM/BE4AfgJsx9Bq/UttkcF8Z0D2Q2A2+MROMVHQ5FmlNljjOpropR7oNrqdAarrjCYquPRKAp0Bl2ECrfaZl5h0cyZR83l54BPEKp834CpuSiiDiQ/Dc0sCs8P7Ks4HUZcYLIE+lr7az/3djm1ngOo6KwHzEEllr7gE+CDW41V7HE4s3b01sAAxf2XB/OdliWpJUkdVaztPhSvOAlaC2Srwn9RxSa0y0BjoH3QsCvWSjYhG9MnUgXTQhcDRwIrAbYljUffZFDgUOLdmW5b//GNiQR+pYzJ49+XwyL7AGPjY/nCWM6nVbQZKoA/pWBTqJb00/rnqYeAO4MOYQKv1KvQ/QfVBHGanDtoRspeIg3ICLFqBR1LHJLWDE0zUSaOBifTW8I2qC4gEWmq184HdmblDZBSwE3HsSW2VwezHQ3YCMA8cvEHUdjZ5VtcaqAdaarVViPrPlyWOI4VzgQOIko/PJ45F3eVYovLGDcQErUeBdwK7EsOGPgNsVfc753UyQHW3DFa6C25dDxgL76/AdaljktrNBFqdtBFwF1HWrddcDbxI9EKfmDgWdZfaZGWVBvef1GCbE1rVEvtD9gYxNm0nazurh5hAq5M+SFSk6EXTgL8TlRFMoNVKC6UOQL0ng/nOguc2BWaFT+wMx++cOiipg0yg1SmzEPWff5s6kIT+BhwFjAPeShyLukcvVbRRAWwA2SPEcphzwHKVWFlW6illTqCvGuL+67clCjUrHx7Xk+Ofqy4AZgM+AFycOBZ1j9npW0Clv4obe3cuHHWrDMb+GN76MjAGjlwEvlaBN1LHJaVQ5gT698DBwHuJs98H0oajQWwGXAu8nDqQhF4kTvy2wgRarfN7otrGvcAziWNRl8pgiWvhv3sDY2CbCpydOiYppTIn0H8kLok/RHyA/CxtOBrEpsBZqYMogPOALwCuK6BW2RL4LvDt1IHUGUO875cF/kuU25tat89KwFq4om2h7ZTXdn4ZmAALVuCp1DFJqZW9DvRTwPWpg9CgJhDVAf6ROpACOAd4D7BC6kDUNV4A/pU6iDrzAlcQnRyHESfPNwPvrttvM+CYzoamZmUwx/GQHQ/MAwdsBqNMnqVQ9gQa4EC8lFR0GwKvEnVqe939RCm/j6YORF3jFOBTFGvFwe8SJ4nbETWptwPeBZxOua989owMVv4PTFkXGAtrV+CISiwRL4nuaMhuTB2ABrUxMXnw7cRxFMVZwDbAD1IHoq7wLWIOyG1EvfFGFV727WhEMc7/UCJhJv/6JNEr/Tnglx2OR03Kokb4fm/AL24BdoZ5Kr09d0VqqBsSaBXfRsDRqYMokNOBrxNVoCYnjUTd4BfAwsT44tUSx1K1ADGpsdbVRBnLbxOLuzzX6aA0sAwmnA3PbgJ8HjgeKrukDkoqqF5IoJcA5gDuGMbvLgMs0sR+FVzZqz8LAcsBl6cOpED+TfQY7gD8JHEsKr9tid7eb1CcS+wPAB8CTqvb/i3gY8SS4x/pdFDqXwYfeBQuW4yo7Xy8tZ2lAfVCAn0MsQLecBLcU4CVm9x3wWE8fi/YiJjkdFvqQArmr8D2mEBr5F4B/klxkmeAI4m2dwGi8sxJRJwvAHsRFTnOzn8erm8BKzaxXwWYawTP09UyGHskvDUdOAPYD2atwJup45KKrhsmEQ7mDOCIYf7uKvT1Lg90y4AnRhxpd6qOf56WOI6iOQVYnajIIY3EccAeFKs9/wMxCmA14HfEEJOqC4ne55WB3UfwHFOJBHywG3iFsKEM3nM9vLUDMAo23x8qJs9Sc3qhB/o3qQPocZsAP00dRAHdCtxDDOM4NHEsKrcpwERiaNC/aDyJ8ICORgTTifHOvwXmI6rw1Po7cfL4fmCpYT7Hj5rc79M4CW4mGez8Mpz0ArCWtZ2lIeuGBLoCLE2MVV6AKOX0BPAwcB/FuqzZa94LLIar7vXnVGBHTKA1Mp8ihkeMATZIHEsjz/ezfSqxMudVHYyl52Uwxwkw5W3g/4BfRG1nPyelHrMr0ZOX9XO7hejha7fpwHc68Dxlsy+xApkaW544Tt+bOpAe8xVcgCmV0cAFwJIdfE7b51wGq/wHsvshy2DN1PFIDZSmfS7SmLmh2gM4EXgM2A1YFViU6PFcFdiJWLTilPx7dd4WxEpkauxOYnLlrqkDUdf6GDHBsCgqwOY4qa+jMqhksP8bcPNNwJJR29mFraQedQPwJwafHHI4cF2bY7GHY2ZzAq8TSbT6dxDRS+8kp84pTQ9HkyYQbeFtRLnO2turwO3pQpvJGOKqyyodfM6ebp8zmJDB2a9AtodDNVR8pWmfy9wDvQzwDwZvEC4Blm1/OKqzJbHy4KWpAym4k4jx+x9IHYhK6zCip/kqYBai4s1FRDUFT2J7WAabPwXP3gxbr0EsjJI6JqlblDmBvhXYDhg7wD6jiUlat3QkItXaHjgXSyIN5nGiIsGnUgei0tqcuNL2eaLaxrPAgUSFi3uBrdOFNpNpRMWQ+1MH0s0yGJvBEdPgguOAVWHWe0yeJeU2I8o13UqswLU10TBPzL//GjHM4y1g0zbH0tOXCBsYT/R8bZU6kJLYmni93pE6kB5RmkuETXqL6CiAKBn3In2dI58BbkoRVIH0VPucwZLXQfYEZJs6ZEPlU5r2ucw90BcRNYYfAr5LrGp1RX47G/gB0bu3CZZR67RdiQUMLkwdSEmcRxyrn04diErpKWLiNETJuJfoq7AwFYew9YwMdnkZ7n8eWBAWuNheZ0mDmJOoBV3tgV4639YpPdXDMYhRwN3ESY2adzBRu3x06kB6QGl6OJp0OHEF48D85zOIGuOrEnMQ7kgUV1F0ffucwZwZHP8mZPtHiToTZ5VVt7XPGkTXN9BD8FHgDWCh1IGUzDuIJKhI41W7Vbc10LMBRwJ/zn9ehhjGkRFzELZJFFdRdHX7nMGqd0N2H2RrOGRD5Vea9rnMQzhUTAcR9bmfSB1IyTxDVOT4YupAVDqvA/vRV0/8HuIEdgNgCeCsRHGpjTIYlcHX3oR/3wAsBXPfaM+z1DEm0GqlDYF1gJ8kjqOsfgl8EHhf6kBUahOAtYhKF48njkVtkMGCwAVT4NDPALtDpRLLuUvqEBNotdL3gJOJ0lkauluIRX++kDoQlcLKwGnAt2u2fRiYDFxGJM+H4bj6rvIhyJ6CJ66FTVfC2s6Syq2rx9g1aVOinNZSqQMpuW2BKVjSrp1KM8ZuACsSQzcmA3vl22YlakD/G/gI8ENiTOz2CeIrkq5onzMYdwRkU2OS4KRs4DUQpLLqhvZZQ9AVDfQIjAZuBn6bOpAuMJq49P6j1IF0sW5ooE8mqt3MVbNtayJhXr9m21nEiq29rPTt81KQXQ/Z45E8b5I6HqmNStM+O4RDrbA70fNc6g+pgphGJM+fJxbFkBpZG/g9M4573YhIqq+u2XYF8N4OxqUW2xWym4A14e8LwQIVuCR1TJJMoDVycxGL1vwYeDJxLN3iT8RreXDqQFRYCwGP1G37ALF4UW0ps1dwOFApzQnZ8ZD9AZg7lmjfsgJPp45LUjCB1kh9lVjt7OepA+kiU4FJwL7AwmlDUUE9wYzHxruBVYB/1u23GPBYp4JSS2SrQXYjsDvcNw7WrMARFWs8S4ViAq2RWAj4EnAIMaFJrXMycBfw09SBqJCuBT4LjM9//jxx4nV5zT6zALsBN3U2NA3XKMgmATfB28vACcDqFbgxcViS1Daln6QyTL8jJg96ItYe6xJjoj+QOpAuU5pJKgNYHniNuKR/JdEGnZjfNxrYBbgm3756igALpAztc7YQZBfHJMEXMiunqHd1Q/usIShDA91qSxNl67ZKHUiXOxp4AJg9dSBdpFsa6JWBvxA9zL8D5sm3z0pc7n8U2CJNaIVS9PY52yIS56cy+FcGi6cOSEqoW9pnNanoDXQ7nEzM8Fd7zQX8l0iQ1Brd3kCPIsZHu8BGKGr7nI2D7IhInqfmtZ3HpA5KSqw07bNvVg3HBsB2wHqpA+kBrwCfJGr5/g04J204KoHpOHGw6LKlgPtifPPCwO4V63VLpeLYVQ3V3MCx+e26xLH0isuJMoHHEhM3pTKbHTgOWDZxHClkQLYbcF+cHD8FrGzyLJWPCbSGYhxwEtHD9bXEsfSaScRQjpOJSWJSWY0DPgEsmDqQDsqAbM745oQTYv7It4CPVOCZtKFJGg6HcKhZY4nkbXWiOsRLacPpOW8DOxETxr4HfCNtONKAHh3gvmrHzV+BN/Pv393ecJLKgEoG6xAdEO8H1q1YXlAqtW5IoCtERYhFgAWI3rkngIeB+7D4fCu8F/gz8SG3MfBg2nB61mRgT+BM4CpiTLRURCcBBwFT8u+n1tw3C7A3cQx381jtLP9nNHEF6ZvAH4CDKjMuwS5JHbcrcCv55bEGt1uAHToQR1FnebfCskRv81nAuxLHonAo8AKwUupASqo0s7xLbgPipO86YJma7fMS7fOGHYojRfucEdU1Fsrg4ry283YdjkEqo9K0z2UeA70HsXDAY8RqW6sCixJL165KXO6+Hzgl/15DN4ZYDety4GPA42nDUe4bwAXAJcCmiWOR+nMFcZJ3F7Hg0r6U+zOnGdXOGzLYkujgmR1YpQKnpQxMkqpuAP7E4LVOD6f91SK6tQf6J8BzxNAYFcsY4OfE2Ogf0h3DsTqlND0cXeRjxGS5S4AV6c4e6Gqv87gMjrC2szQspWmfy/zGXgb4JYOPcb4E2Kv94XSV0cAvgM8AHyVKLalYpgJfJo7v44H1gR2J8f9S0ZxJLC1+DHBtix7zr8BqTexXASa06DnrZXU/vBf4F1FucrMKXNqm55WUWJkT6FuJMWV/IXrhGhlNJBW3dCqoLjAG+CPwYWAT4Mq04WgQfycqo5xGzOrfBbgsZUBSP54EtiaG3C1PTPQeiT8BFzex369ofY9WbeJcyTfsQbwHLwe2sDydpKLajKileSsxJnRrYGJ+25qoU3xDvk+7x4l2wxCO9YFTgQeAZ2muZ0fFMQvwa6Jn+mja1+PWDUpziVAt8QqwVQsfr77Xed4MTs3gjQz2z1xCXRoJ2+cO2YBY2ngqM1fgmAqcne/TbmVMoJcAPgjMCRxA9OKfBuyHq92V2UbAHUT5sN/giVAjNtDpjCYmwC7ZwedsZQJdnzyvm8GDGdyewQoteg6pl9k+d9icRC3oag/00vm2TilbAn0AM550TCEuP6o7jCaGclxBHJvXAJ8kyofJBjqlMUSbs0oHn7PVPdBkMCqfIPh2PmFwtlY+vtTDbJ97TNET6HHAeGKCyx+I1b92BuYgxs++I11oarNlgSOIYTlvAbcDvycmiPZqj5kNdDplTKCz2q8ZvCuDf2TwfAbbjjg6SbVK0z6XeRJhs5YgEsU7UgfSJmOJ3vbZiXGw8wBzAQsTS8ZuQlQsGZ3vfxdxmf9f+c8uJ9vd7gb2JxqltYnlhFcHvg4sTiQX/wUeIZLsx4mqK9XvXyGuUEwhShq+0dHopXQyYjxzdUxzJa/t/EfgXmDVCjyUKjhJafVCAn0MMdZ3OBM7jqWJnpKxY8dWxo8fv+Dpp58+vtH9Rx111ITzzz//+CzLhnSZL8uyOennb5Rl2dxZls1BJM0z3T1q1KhHR40adf9ss8126oQJE26YMGHC0+PHj3/1wAMPfDLfp2Gs6mp35LdjAA477LAFb7/99tVef/31xd5+++13TZ8+ffy0adPWy7JswvTp0+fLsmw+YNaBHrBSqbxMXIF5u1KpvFp334tt+n8MaNSoUU+ddNJJu8wxxxwNS1zuuOOOsz399NOVqVOnNrpb7TWNGGZ3f+pA6lST5VqVmjtnIerifx74AfD9yozLk0vqMb2QQJ/B8HufLwXuG2ynt99+e+Wnn376+okTJ77Qzy6v53GM7uf+Rvp7LIjewLfzfarfv070Dr5M9BRmAC+88AKPP963gOCZZ545hBDU5V4A/jPIPnMQVzgm5F/nrrt/VvrGf87DwCvNVa+WtNtLH/rQh54f4P7XGbx+vNojA65KHUQD/Xaw5LWd/0IsKLVZBf7Zsagkqcu1fJKKpLYpzRg7tcSw2+e8LN1rGZyTwfwtjkvSzErTPndDD3SFqLqxCNFDMJpYje1hovfYniZJUtOyGOJ2NLGg1AHAMRU/SyTVKHsCvSvwVWClfu6/FfghsUCIJEkDymA94M/EcLi1KnBn4pAkFdBA4xWLbg/gROAxYmnYVYFFgcXy73ciJqqckn8vSVJD1drOwGXEIlxrmzxL6k+Ze6C/CBwP7MnMl9YeBm4hkufDgS8BJ7c5nlVobYmvefLHfKWFj9ku81MzcbHAZiNOGl8dbMcCmJ8oJVd0cxH1pd9MHcggqpPEbgKWShmIkhiwfd4KJjwDB88LS50CP9g9JjquN8znqgAbAi8N8/dTGEe0j2WKeU6iEkqZSmtOAJ6n+J+VtWYDru7g89k+d8DLNLd63pa0v1GYzMxLiXvz5q24t7+jXjGZAY6FrSB7BrIrIVs0/XHpzZs32+e2uxI4hyiN1Z/RRC/15R2JqLW+BPw7dRBNqK4stn7qQJrwa+KqRNEtS7ymC6YOpAkXAd9PHUQTPkKcdEv/k8E8GeydwR5Z9MK2wsR46CGVLU3t6/QtrlUW5wI/TR3EEL0AbJM6iCFYgTiWrUDTQJmHcHwPOA+4kUiK7qCvdvJ4YDlgO2BlohdakqT/qcTVyWNTxyGpfMqcQF9ELFN9EPBdZj7bnwacD3wZuKKzoUmSJKlblTmBhkiMryAmEyxE3yXvJ4la0FMSxSVJkqQuVfYEumoKsWjKoMtuS5IkSSNR5jrQkiRJUseZQEuSJElDYAItSZIkDYEJtCRJkjQE3TKJsBs9SSxJXnTTiZW+nkscRzMep/hLTkPUpn2Uciw5/ihR8abongEeTB2EesJzxLE2PXUgQ/AE8EjqIIaoLG1PrcnA06mDGIIXiePitdSBSJIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSeqgHwOPpg6igVHAXsCtwKvAA8ARwLwpg6qzGXAF8DJwHbBb2nAaKsPr2EhRj8uq5YFTgSeAJ4HfA/MnjUi9pujvEShP+1OGtrxWWV7XgZTh+JX6tQEwnWIexPsAGfAHYHvgEKJxuxoYnTCuqg2At4BzgU8AJxDx7pIyqAaK/jo2UuTjEmAx4HngSuBTwHeAF4BLUwalnlL090hVGdqfsrTltcrwug6kLMev1NDcwIPEm66IB/GjRINWa1ui0fhQ58OZydnE2f/Y/OcK8Pd8WyVVUA0U/XWsV/TjEuBnwJ3ArDXbdgNuBN6dJCL1kjK8R6rK0P6UpS2vVYbXtT9lOn6lhv5AXKr6HcU7iOcjGoLP1m1/R779Sx2PaEZzAlOBb9Rt342I770dj6ixor+OjRT5uITo3XmWvtduDHE5VeqUor9HqsrQ/pSlLa9Vhtd1IGU5fpPww6T4tgF2AvYgGo+ieRVYDjipbvsG+df/djacmSxEJFJ31m2/I/+6SGfD6VfRX8d6RT8uAd4JTCB6Ty7Iv74AnJhvl9qpDO+RqjK0P2Vpy2uV4XXtT5mOX2kmCwDPAPvmP/+KcpwFrgc8B9xO9PqlNJE4059Yt30Rij92rkivY62yHJerEX/j14E/Eh8I+xFjoq/FDgS1T1neIwMpWvtT5ra8VtFe10a64fhVD6gQb6LqbXTN9nOBS+j7oE95EPcXZ63xwC+BacANFKNHoL9Gd9F8++4dj2hwRXwdq4p2XA5kY+JvfErd9ur4w007HpG6SVna7lplbcehnG15raK+rvWKfPxKM1ifePNXbxfk23cnLv+sSJS7mRc4Bng8/372gsRZtRnwGDHm9Iv0TfJIbWki3q3rtq+Sb9+k4xENrKivY1XRjsuBrETjD9a58+0HdTwidZOytN21ytqOQ/na8lpFfl3rFfn4lWYwJ9EAVG9L5tsPZcaGrv52XEHihJhJPBX4G8UbWzonccb/tbrtO1K8iSdFfh2rinZcDmR+Iqa967ZPoPHEHmkoytJ21yprOw7lastrFf11rVfk41dqynuADetuZxLjkjYElk0S1cxGAw8B51HcMaXnANczY3ynUazSR2V4HaE8x2XV1UTN59q/c7Uu66pJIlK3K9t7BMrT/pShLa9Vlte1VhmP3ySKOoBdMTu3fobudsCbwGUdj6Z/6xBj0C4HDmxw/0XAbR2NaGY/I8ZzHQecTFzq25aYYZylC2sGZXgdoTzHZdWhwFlE/dhTiBnxXwGOB25OGJe6V9neI1Ce9qcMbXmtsryutcp4/EqDKuJA/j0Z+HJP/eXzVDYjVqN7mejB2DltODPZk3K8jo0U8bis9RHgKuJv/x9iJTA7D9RJRX+P7El52p+it+W19qQ8r+tAin78SpIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZJ6UyV1AJLUhHHAuxpsnwY80uFYJElSnUuADNhkgPsv6FAsnwSu6ue+zYArgJeB64DdumSf4TqL+LvV3l4FbgJ2b+HzDGbZ/LnfP8zfzxj8+Kp/jq2Avev2uRs4dJgxjMRw/8aD/d7GzPz3zYAn6/ZbHjgVeCK/7/fA/EPcZyzwFeAOYApwPfCZJv8f0lCdTONju/Z2d75vqvf1UDVqk5pp24ZqMjCpyX0nAn8l3vdvAY8DfyHa05Saea0m0/z/Ux0yJnUABXY0sCKRhKXwTuBrwLMN7tsAOA+4EPgikeyfAEwHTirxPiP1DPDV/PsKMAH4OHB8vu2EFj1PO/0DuGWIv7MVsApwbOvDGZLh/o2b+b0lgTeBnep+982a7xcDrgTuBA4BFgX2A5YgEvBm9/kNsAdwGPBvYL182xzA4YO8BtJQ/ZYZk6V9gcWBg2q2vdTJgFqgUZs0nLatVSYB3wb+C5wG3EckzlsBNwNfJt7jKRTttZKG7RLizfUm8PN+7m9nD/TqwGXAG8RZaKMe6LOBW4meMohk8e/5tkqJ9xmJs4D7G2wfBzwMXNSC52jGSHugh/McvwOurdsnRU/VcP/GzfzeoUTSO5Cf5fvMWrNtN+BG4N1N7rMU8dp+qe6x/4/orR43SAzSSJ1G9Dg2UpYe6EZtUjtMZvCe2erVqxOIk+Ba8wCnEyfra7c4tmY181pNxh5olUA1Qf4WMb5yzX7ub5eliZ6Hg4D/MHMCPScwFfhG3fbdiEbivSXdZ6T6S6AhzuRPrPl5NqJ38WHiROlx4Dhgvpp97gYOAD5BJFdTgKuB1Ro8zgPElYIzgA3pS26vAi6v2X880VA/WLNtFPA8fb0P9R+QzTxH7aXe99c9zueA24krKfXxt9Jw/8bN/t6pRKINja+cjSZeny/V7DNqGPt8PH/eFeq2r55vn9jP/0NqlWYS6MHe19sB1xDt1oNE8jW25v5RxJWXW/J9bgMOJt4jtc+1b/5cj+aP2czjD9Ym1cZwUM3/4zZgl5r7m2mnJzNwYlkB7iE+G2btZ5+5iGEdN9Rse5YZrwAA7Jn/f4YS32CfI82+VpOZ+f852N94GeBM4srsy8QQuXWQ2qiaII8j3ti3MWOvUyfHQJ/HzAn00sQb7aN121fJt3+wpPuM1FlEI7NgflsIeB/wXeAVZkx8jgReB35MDAn4Xr7PcTX73E289lcA2wA7E5PVapP084C3iYZuNyLJe4G+hvAQomGdLd9/K/oaykXzbSvnP29b87y1Dedgz7EQ8GfiMuTiwCw1j/MIkcB/lPhgegy4d6ZXrjWG+zdu9vduInppbiFOQp4AjiJOSiBehwz4FPH+fI247H0iMZSn2X3Wz/f5SF08O+bbd+zn/yG1ymAJ9GDv68/Q1+O6I/Ajog2p7UT4Ub7PkcAOwE+JDqPf1j3XRcSVl6OJuQPNPP5AbVJt23Y4MRb5u0QyeEL+uFvk9zfTTk9m4AR64fwxDxhgH4Dv5PvNlf/cTALdis+RZl+rycz4/xzsbzAWeIjIYfYD9iGGoz1HdFpIbVGbIK9NfFgf0s/97dYogZ5I456wRfLtu5R0n5FqNImwevsZM/Za/pkY81brN8TJUtXdxAdH7SW/vfPHmxdYN/9+n5r7K8QZfzW5XYMZk8BDifG3b9I3sXE/ogd2nprnrTaczTwH9D+E4wlg9ppt+zDjh0QrDfdv3MzvVYAXiYT3a8QH7CFEr9VVRE/Wavn+rwN/JD6s9iN6968dwj6zEj1W9xFjsxcAtiaSlCzfX2qnwRLogd7XsxE9jsfX/V51n5WJY/pNIvGr9XWiLVq85rleAN5Rs08zjw+DDytbjEj4ahPbCtELfEr+czPt9GQGTqA3Z+CiAFXb5/tVe2ibSaBb8TkCzQ3Bm0zf/7OZv8Hy+ffb1Ny/PJFgvwe1hJMIB3YdcAQxnOM0+mZCD+YeooFo5FfM/MZshepY0dFdts9QPElULqkaA6xKXJpcnL5GcteafcYTieqmzDghDeJEqXYSabXiwxhgLeLk6ria+zP6EjOIM/5ngY2ISSET88fMgA8QvQcfIC7pNZok1MxzDORCIumsj3+g17vVx+5w/8a1vzeK+HC4I79BjJF+lHgtPkj0ngGcw4zHwGPEe7fZfS4G1J+xngAAIABJREFUPkZ8OFWH37wEfJP4/z8/xP+H1GoDva+XIirK/LHud04iepfXIYaDjWPGHunqPj8kTjQn59vOJZK1qmWbePxbm/g/rEW0o7WTi6u9z9Urvs220wOpDtsY7Hfeyr8OZT5OKz5HhqOZv8HJRLv1A+Jk60Ji7kcrK1/1PBPowX2L+EA9luiRasaHmXEsUq0XRhhP9c03vm57ddzVEyXdpxVeZearA+cRY8QOI4YF3EyMqz2U+KAYTwwPeI2Zk7ynBniuBYnk+I267Y/WfD+daLg2IhryNYie07HEpbwKcUw1mqza7HMMZKD4+zPcY3e4f+Nmfm8a8YFQ75z868r0TRI9r26fi4e4z8XAXcTch4WJXqK7iRMxiHGOUkoDva+rQ8MuJNqfehPoa0/q35PVnxcZ4LmaefxmLEq8r5+p215bdarZdnog1RPu5Yirf/15X93+jdQn1634HBmOZv4GLxIdAt8jEu1xxInNUfmt0e9piOon0GhmU4DPEqWsPtfk7zxAfOg2uo30zfQEcfAvV7d9mfzrwyXdp53+nX9dhLj8dT3ReO9MJGobEz3E9bIG26oeIXoB6iemLFT38wVEb8tGxPvtOqJncynisuL8RG/qSJ6jPwPF35/hHrvD/Rs383uLEOPH61+H6ofAy/QltrPU7VM9GXilyX3GEZM0FyJOVO4gLmuvkz9fs1ehpHYZ6H1dPcZ3JDoMam/LEWOZH8v3WbDud6s/154kTqvbp5nHb8aTRKJZf+K8ANHDOpR2eiAPEu3Wp5g5sV2RmPMwG3FF6iaiLelP7evVqs+R4Wj2b3AT0SEyH5FM/5sYYlJ79U0jYALdnAuJS7qHEr1Sg7mHOMtvdPvZCGOZApxPTDqr/fttT4y9uq+k+7TTuvnXe4hqCvMQlyqvIRrAsTR/daHqOuL/UtsYVYhxcrUuIq70HEzMwn4tf963iasbjzPjmLnhPEd1eysM99gd7t+4md+bg7iUXL8gTnXi4XVEz9W/6BszXfs4EB92zewzlZioeVTN/bMSH8B/wx5oFdvdxIngcsx48juO+Pyak3hfvcWMQxDIf55OX4fDcB+/aqA26UYaT8o9jhgT3Kp2ejoxb2EtYqJd7dW19Yg5TRcS44I/W3PfNCKZr6ow40TnVsVX+/jNauZv8FFiouKSxJXZS4k27Bn6JoNqhBzC0bwDifFZyxKzWwfSziEcEInMJURjczLRk7ktMRM4K/E+IzU3MyaXo4mz8n2IEmj3Eg3PW8SwiRPzffYkxkjPRgzXObOJ57oxf8wj89+9DdiSvkv9VU8TPQEb0JeAvkYkaxOBP9D//7/Z55hGfABsSkzCebGJ+PszkmO3mb/x54lScZ+mr5zfYL93NzFG+QjiBPYu4u/6JWJoVXXM5aHEZNKziYlIyxErCh5PDN1pdp/DiQ/GHxOlJPfI96udzCkV0StEVYsfEZfyryGGGnyOWERkMvGeOpyYkDsbcVK5JvEZdzRxFWokjw+Dt0n3EG3fL4ie3VuJXtIPERU5/ktr2mmI1QfXIOZvbE5c8XuI6IF+mWiH/8OMFZZuJN7v9xJXovZkxitXrYxvqO13s3/jhYmVFv+U/87mxITQThVBUA8aqMpGtZRVyiocVZsRY7peJpKxnbtkn+HqrwrHg8T459pZ0B8jJlS8RvS2fIFYROMO4J/5Po0WLKiWoasu+zwrfTWanyMazHcz80Iq38+3bd1gW21t1UbP28xzbJDH/hrRM9Jf/Nsw4+zvdhjsb/wLGtdZHuz35iAS2oeIKhq3Eh/49ZdlP0K8Z14mPhQPYeaOgsH2GU3UpX6ImDR4HjGsQ+qEoS6kUv++rtA3JOFV4jg+/P/bu/eoWaryzuPf9xxuXjAEiIiKN0wEBRwElCghZCYIBNQ4gqxZoiISRaMoCuNlwjLomsSoQaNODOIoakTxBiiMCGqMIrdAREUuKnIRuSgX8YAIHnjnj929Tr377Kre1V3d1VX9/azVq7urqruf6nOUX+/z1N6s/N/9KsIMGN8bHHMZIVDH80CnFm3Jef+c/0/agLA64BWD97mUlbP15Pz/9LXkLzCyO+EH+g8J/x9yHSH8Pocw3dtlrAvJjyH8y9gaQoj+O9YtyFKnvpz/juR8V/F55vwZ7E0I179m3f+vHoQkSZLUgE1ZNxe/JEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSemep7QJ64ofAk9suQlK2LwAHtl2EJKmbNmi7gJ54DHAscEHbhUga6SBg57aLkCR1lwG6OZcCX2u7CEkj7YwBWpI0gVVtFyBJkiR1iQFakiRJqsEALUmSJNVggJYkSZJqMEAvhuW2C5AkSeoLA/RicL5vSZKkhhigJUmSpBoM0JIkSVINBmhJkiSpBgO0JEmSVIMBWpIkSarBAC1JkiTVYICeHediliRJ6gED9Ow4F7MkSVIPGKAlSZKkGgzQUjCqxcYWHEmSBBigtZhSYXhUi02830AtSdKCMkBrEU3aj748eA9DtCRJC8gALQV1wrAXhEqStMAM0FIwTig2SEuStIAM0FK1ZfJGp23nkCRpQRigtahyA+8SeSPN9kRLkrQgDNBaVOPOqrFc8XgpsV2SJPXMBm0XIM2JeAQ5d9QZVobn3NdKkqSOcgRai6psLuiylo2ykefh6yRJ0oLoa4B+MHASsF3LdWh+jQq9ZSG57HXLhfv4tbZ0SJLUI30N0BsBLwUe0XYh6qViiF5ObHNEWpKkHutyD/QNFfuGPww+B9w7ePzo6ZajDot7mGF0SC47rthLvcT6FxfG2yRJUsd0OUCfDBwN3DV4vLawb2PgcOBc4OezL00dURZ042OG+8v6plPHx/uXS/ZLkiTN1J7AtcCFwJMK2zcjBJW9Jnz/C1gXnEbd3j3hZ2m2UmE4vlCw6ph4/3J0P2pbWQ2avmOAi9ouQpLUXV0egQb4FrAT8E/Ad4H/Cfxzg+9/GPDIjOPOAX7b4OeqnnFGdavaMUbN7xyPSlfNKV01P7TtHJIkdVDXAzTAr4GXAV8CPgz8JXBUQ+99+eA2yjLwQEOfqfpyA2hVm8bwfeJjckJyqpayEB1vM0RLktQxfZqF41RgR+AeQuuFFKsKqamluMuCbXGu6OJ9VZ901dzRhmdJkjqkDyPQRTcDzwUOAZ4CXN9uOZpzVaPAqXYNSLdzFLenZuEoPp50tFySJLWsbwEaQvD4ZNtFaO7VacvIufAvDtrxZ9SZhcOLCyVJmmN9auGQUspm0ki1VRSNM/pb1qaxPGJb6nWOPkuSNKcM0FpkqdaMobL5nHPfd/geqb7qsraQ+DhJkjSHDNDqk1EXAVb1MafCbKqXuUrZdHUpo0bBHYGWJGlOGaDVNaPaLlLtEmX7U8eMM/KbE5zLlgq3H1qSpI4xQKtrRgXOnP2pNooqo46p8x5xf3Nx9Dt3CjxJktQiA7T6JGcWjaFZLGBSNQIet41U1WI7hyRJc8QArb4o63eOj4kfT3N0t6wnO24zqZqRYxZ1SpKkGvo4D7QWU1mPcazqYsFZGDXzRlldjkJLkjQnqgL07jXfy+WzNQ9SIbRspcE2+otTS35XXYRocJYkac5UBejza76X/6HXPChbjru4bZ7+rpaF53gVw3mqWZKkhVYVoLcvPH4EcCrwTeBjwA3A1sChwM7AXlOpTqqvGDbLFitps6+4rMcZVo6Uz+IiR0mSNEVfBE4h3Wf6aUK4XmQPAMe1XUTPjBtu42nh4sfzeqs6HzXrGOCitouQJHVX7iwcewJfIj16duZgv9SkcUde43mWoRshNDUfdM7MIpIkacZyA/QaYIeSfTsBtzdTjlTbqFCZCtTzaJzVDCVJUgtyA/TpwNHAEcDGg22bAK8GjgJOa740Kcuo6eqKqw7OszqLwEiSpA54EKFVYxm4H7hlcL9M6H/euPylC8Ee6PZVLUDSpVvqfAzRzbIHWpI0kdyFVO4B9geeCTydMCvHz4ALgYunU5pUS2qFv6r986o4C0fxeWo2EUmS1IK6KxFeAfwo2rbl4P7WyctRT82qhaJqWewuhGdYPzgvJbbHx0qSpBnK/Q/w04DPAts28F69sy88sB+ceSR8ue1aFtUr4YQT4JXDx23XM6kT4JXFc4J15zg8v+I+5fsXePbn4clfhye3XYskqZtyQ+95wDaEPt+bS445o5GKOuh0WN4ZbtoGbmy7lkV2CewSb9sFLkltn1fDeov3bdfUNzfCVl+FDQ8LrWiSJE3N3cDBbRcxx7yIcH509eLB+ELC+L7qXFWPFxFKkiaSO43dlcBvp1mIVFPZrBupfV1T7Bkf9nWnzndelieXJGmh5AboDwBvAB4yxVqkOuLw2Lce/HhEOjWrSErfvgdJkuZO7iwcmwGrgauBswjzQMcjXW9usK5cGwB7A9sBPyXMVb02OmYnwtR7H5ltaWpIcRq3qtDYx5HX4nkVzz/1XYxaUMZgLUlSQ3ID9BGD+18Bu5ccM+sAvRnw/4A/Lmy7DNgPuKGw7dnAu+lHgO7jSOso8TRukA6TqXaGLonneh6OPi9F93Us4t8XSZKmLjdAbzfVKsbzdmAH4EDgW8CewIeBLwDPYv2R6D4YNRLbBXXqLzs2tbBI10da4/CcmtM6NRd01Qh9V78LSZLmWt2FVFKeDxwJ/FkD71XHAcA7CYGZwf3NhDD9KkLf9qQeRd5UV/EI6DR1PRTVqT8+Ng6JqeA47mjtvInrrzpX6P7fC0mSOiM3QG8BHA/szPoXHj6e0H88a1ux/qqI3wE+BLwNOBm4bcLP+AqwY+axW0/4WRqtGCLjQBn3C3fVqNk2oDo0p9o+DNeSJDUodxaO4wkjzecCGwP3A2cD9wL3EPqOZ+1qYN/E9mMJdX2C/PMrsxPrwlrVbRkXUZmVeAR6qMuheZRiP/TweZk4XPdhNF6SpE66mXULhewPfH3weEPCKoWvbqGmwwnB4MuEJY03Lezbh9AD/WVCkJ52gHAhlfa1vfjJrG7F863z3WgdF1KRJM3EfaxbiXBzwmwcw9HdV9DOcsOrCL3OPycEhPhCx/2A60kvQtE0A/T4xvmziYPkItxI3Mffxyz+rveBAVqSNJHcFodbCP3PALcDdwK7DZ6vpZ1ZOh4g9Ds/itCjfU20/yvAE4A/AV4229JUwzj9uX2ZeSNHfK6pgFzW1iJJkqYgN0B/HngdYTVCCCPObySE6kNYP7zO2u2EvufYWkLf9kkzrUbTVhxpXaTAGPc3x6G67KJCSZLUoNxZON5KWIlwl8HztwAXAgexsr1DmpXiYiOLKmcKu9Sc2ZIkaQK5AfoewlzPQ1cRpm3blTAbhjNQKNcy9RdSiV8TzyzR15km4nMvfh9QLxAbniVJakhuC8fuhOnriu4Bvg38hnUj09Io44S+nD7gPioG5lHnXjUan/OdL8p3KknSxHID9PmULxSyGyFIS+OqEwpTx/Yt/KXmt45H4FOLq6RWbhznMyVJUoWqFo7DB7ehU1n/Qr0lwkwXP2m4Li2WsvAWty6ULZzStxaOsh8JdUNuqtUl57MN05IkVagK0DcDlw4ePwO4HFiTOO5i4JSG69J0tRmScj+76ri4L7hP4TlHWV906vvKWb0wdbwkSSpRFaDPGNwAdgCOBm6aekWahTZD0jjhOQ6Lw8eLpqydI94mSZKmKLcHeg/gLkK7xvB1Lwf+Edh7CnVpsfWtJaNJud9NfIwBW5KkhuQG6GcC1wFvHzx/FfAR4MXA2cALmi9NMzZvgTU1z/MiB+thD3jx/OPHZf3Oud9ZzgWbkiQtvNwA/S7CaoN/P3j+CuCjwFbAZwjtHeq2tsNpapq2OAQucqAbnn9ZO0tqFo6h3NHn1MwekiQpkhugnwp8GPghsCWwE/Apwn+4zwKePJXqNGtthujcVfUWXbEPuskp/Rb5x4kkSbXkBuh7gY0Gj/chLN994eD5pvgf3z6ZdUgd9XenOMOEf8/Wb+XI7XUetRCLP04kScqUu5T3d4C/Aq4H3gqcA9wNbAu8hDAyLY0jZ6q64XOtPwIdf0ej2jgMypIkTSg3QL+JcLHgacCdhIsHAb4KPBJ4XvOlqWfqzj0dT1e36CPQqQsIi9uqeqNdHEWSpAblBugrCaPNfwTcQAjRAG8kLLZyXfOlqQeKwW3cAJeajWORFM8/FZhTYbnsPSYJ0YZwSZIGcgP0gcAVrN+qcXqz5ahnmghcixqch+qc/6RzP+e0gEiStPByLyJ8H/DaaRYiJaRW3ltUqdUH4/2puaKrXjPOMZIkLbzcAP13hD7nP5hiLVJskZftTilb6CQ18pxqe/F7lCSpAbktHKuAHwBXAWcAtwD3R8e8ucG6pDKLejFhavaNeIq/Uf3Q8Qizfc2SJI0hN0C/ZnD/C+DpJccYoNWUONhVzXvcZ3EILpuybjnalnvRYGqmE0mSNEJugN5uqlVIKxnkgqofC6lZOIqvqzPavKij+pIkjWVUgF4FPBa4kbAaIcCDgRcQlvf+GfBpwsi01KQ4EGrlCHPxeWqKu9QofhV/tEiSlKnqIsJtgAuAn7Lu4sGNgS8BHwX+HPhbwhzRO06vRM2xac3PPOl0bH1UtoR3PNf2qD+Tsn3+SJEkKVPVCPSHgPuAZwA3DbY9D/hvwAHAmcBDgS8A7wL2m16ZlZaAPyQE/q2A1YR6rwd+jMFgmuJFPiaRuvBt0RdRKUqtyBh/P8XR6eFr4tHo1IqF8fGSJGkMmwK/A3YghOzh7SvAxcCGhW3PBG4tPJ+lFwHfY12IiG+XAi+cQR0PAMfN4HPmWVNBN36fsj/bRb5RuE99Z+NalB8qxwAXtV2EJKm7ygLvMBBfwsrRrY0I09etKRw73H4XoU/696ZS6fpeAnycEOrfRVgl8bZBPZsDTwIOAk4htKp8ZkZ1LarUaHTdUc7iiGlqxFXVJh1BdvRZkqQMZQH6YcCvgN0JI7wA+xPmgN6VMLI7tBfwWeDh0ymx1GuBTwCHsn7Aup5Q4ynAe4GjGC9AHwA8JeO44Y+IRRcHsLIV88qCXqrNwPC8UqpFI+6NrhOEc37QjPNaSZJ6qyxA30u4WPAEQlBdDbyHcFHhMDwvEaa3ew9w3nTLTHoS8AFGB6yvAYeN+RkvBrbNPHZWI+9dURxBjo0TyhyJDlIXDeb0N5epeo0zd0iSVNMjCMF4OAp4LStHY79R2P7EGdcG8G1CyN+w4pjVhFHqf59yLfZAp40zgmz/8+je57gPOtUX3TWzrN0eaEnSRKou+rsZ2IMwD/QmhGW8HyjsPxF4P3AOcPe0CqzwDkJLycWEVo3LgDsG+34f2B44kDBf9f4t1Kf6s3R0OQBOWzzKHD/uejtFl2uXJKm25wP/1tJn70kYhV7L+qN1a4HTB8dM26KNQE8r6Kbet+1R33m5lX0vOfxhspIj0JKkieROO7cFcDywM+svvvJ4wmIrbfjW4PZQYGtC2wmE0fObCDODqHlVFwIWDYNbvNx01ZLSSit+d8vR9jq9yvHx445cd33EW5KksVWtRFh0PGGk+VzCaoT3A2cTLja8h/YWURm6i7BoyrcHtx9jeJ62nEVO4hki4uejOHIaxIF3uK1sdcLc90o9H6cmSZIWSm6A3ocwHdyrgdcT5ol+A2Gaux8Bz51KdZp38cp3VcfFz1MXwxU5whlU9TwXt48Kz/4YkSSpIbkBenPg8sHj84FdBq/9HXAS8PLGK1PXxAGtLLCVtXXEcttE+q6s7cUp5yRJaklugL6F0P8McDtwJ7Db4PlawnzQWmyjAl0qCJaNYMc9vgpSPz5SI9FNjUaP89375yVJ6r3cAP154HWEtg0IS3y/kRCqDwGuab409cSoCweH+4rtHC6aEozqGU+N4uf0macuRKx6/5RxXiNJUi/kzsLxVsKiJLsMnr8FuBA4CLgPOLj50tRxwyCcO/9zWX/vIokvuMyZbaNsJcFRLTBlvdW5xg3KtuVIkjovN0DfAxxZeH4VYdq4XYGrgRsbrkvdF4fBUcemXrdIyr6vsu+jOFJf9kMlp4981oG2qc8yiEuSWpPbwrE7Yfq6onsIU8b9hnUj01IsN+TELRyLJh5tLvs+lqNtZb3mlOyPjbqYc14t4t8RSdKcyA3Q5xNGnFN2IwRpqUrVinqwcrS0a2FuGuJR5TgwN92DXPW+0/jz8M9YktRZVS0chw9uQ6cSFk4pWgKeAPyk4brUD6n+3FSvbvFx3JqwKEErDrBxa0a8gEquuiP6VX3WTX5mnX+ZcLRZkjRXqgL0zcClg8fPIMwDvSZx3MXAKQ3XpX6Jw3NVb/SiXkw4ajGZsh8go8zyYr86F47mMjxLkuZOVYA+Y3AD2AE4Grhp6hWpz4phsNjLm5rLeJFGn1PKzn/cID3O58/iNU2ch6PUkqSZyu2B3oPy8Px84N+aKUc9UmexjtQFcYbn9efHjo+hZN88KuuBbyL4Gp4lSTOVO43dFsDxhIVT4tD9eOCnTRalXqjTA5uakm2RR6HLgmaqlzw1S0fV87bUnQ98XuqWJGk9uSPQxxNGms8lTGd3P3A24aLCe4D9plKdFkHqosFFDs+wso94VCtHHDJHPZ9nky7uIknSTOQG6H2A9wKvBl4P3EpY1nt34EfAc6dSnbouJwCnWhHqLMLSdWXnt8zKHxNVx3bRov44kiT1QG6A3pwwCweEOaF3Gbz2d8BJwMsbr0x9MO6FaIv0T/lVvc2pBVRSr0m91zQDahPvPcm804ZvSVKrcgP0LYT+Z4DbgTsJC6gArAW2a7guLZbU9Gep0ehFEF9slxp9LvtBMasWiGlMU5c651l8viRJteUG6M8DryO0bQBcAryREKoPAa5pvjQtkKqQvAjhOdW3XPadTPJ9zPOodNdmFZEkLbDcWTjeCqwmtG4AvAW4EDgIuA84uPnS1AO5fcyp1QkXaQQ6Pr/U89QCJXX7xLs2Ki1J0lzKDdD3AEcWnl8FbA3sClwN3NhwXeqHcYNd3AMdH9vnQJ26gDKnZSOHi5ZIktSAqgC9e8brfwc8ZnC7oJGK6tkA2JvQg/1T4ExCT3bRTsDTgY/MtjRNoGwEuo/huWzO62mE1LIfKnVCcRfCsyFfktSa5Zq3WdsMOC+q4QfAo6Pjjmb69T0AHDflz1gkxdkmqh734ZY679T3UHXcuPr2YyTXMcBFbRchSequqosIty/c/gz4FXAa8DxCL/QBhIsLrwa2mW6ZSW8HdgAOBB4+uH8k8AXyW1M0fbkhrarvt88j0MUwXTYKPa3R6Wl/n337s5IkqZYvAqeQni3g08CpM68otGy8Ndr2LMIqia8tbHMEul2T/AvFqBHo4uMu3uL6U+ffB02cR5PfhSPQkqSJ5E5jtyfwJdKjhGcO9s/aVoRVEIu+A3wIeBuwxcwrUkrVlGyUbC+OOA+fpy4q7HrALJ5n6jvqSx9v1aIpdd9DkqTW5QboNYR2iZSdCIurzNrVwL6J7ccC9wKfIP/8NH1VM0mkgmNZiJ7VYiGzkBss2/ihMI3PbOLPq+s/miRJPZDbK3w6oRXiOuBjhIC6CXAYcBTwvqlUV+39wImEkegzgJMJQf+OQV1nEuq+Y4LPOBbYMeO4JWDTCT5n0RRHlCE9E0RZiC7ui/uGu9YjnVt3zkwZo/bn1pOaOq+J927KvNQhSVpguQH6LcAfEtoj/g9wK7AlYYT3NOBvplJdtY8CGw4++wDg34ErB/u+CjwHOIHJLnBcS34A9z/s+coCc9lx8QWFy9Hjoa6E59TUdal2lfg1JI5JjcxPUled7ZIkKcMzgdcD7wT+mrCQyjzYHNg4sX0DYA/g0Cl/vhcRjqfq4rk4GFddeNe1iwpJ3MfnX7ZvlK78iIDqCyinyYsIJUkTqTvd23mD27wp68FeC5w7uGn+lI26pkaeibbFz7vY0lFW2yTtE9MaLZ5GG4cj25KkTurjRXargbOAbdsuRNlSF9PFITl1XN1ZOuYlsKXOM6Wp1oxx5dYpSdJC6WOAXgL2wYv6uii1YEhVaE4F56q+6jZHo0cF0Hns5y6reR5qkySpNX0M0Oq21MWBqZBcdhFd6gK7+H3aUDUaXja7yDya59okSZoJA7Tm0axaOtqUulCwyRk1pmWea5MkaSb6+B/DJcKS3pcCd83iAw+CBw6Ab7wEzpnF5y2KN4fZXngnvHnU9uK24eP4ednjWYvrHj4vPp5EU+/TV6fAnifDtl+C7dquRZLUTX0M0DP3RVjeFW7aBm5su5Y+ugR2AdgFLqmzrfh8+LgtxZqG9VRt64ou1nwjbPUV2OBw2LrtWiRJWmTOAz19k84ZnTq+rXmfy+qh4rGa4zzQkqSJ2AOteZQKjqme5tT2sgsMU7N2TPsCw7i2suA8qiaDtCRJc8QArXlUtgBKHIaL26E8lMbPZzVndPGzllgZ7uPjWAdZAAAMTklEQVTnVZ9pq5UkSXPEAK15NWqBEZjenNF1VzCsCr7x+6RWUow54ixJ0hwzQGvejVruehpzRufOdVzVYjEMz1WjzKPaUiRJ0hwyQGveFVs2UkGzzWXAc3u1Uz3Q8WscdZYkqSMM0OqCsqBc3B/va6ulIx75Xo7ui58ff07V+0mSpDlhgFaX5Ibotlo6iu+fqqOshmKdqfeTJElzxACtrikLqMV9UB6ic1s64sAbHxffF6UuHExtK3u9JEmaYwZoddW0WzpSI9BxS0bq81NT1xUfx+8tSZI6xgCtLptVS0fcA1224Ek840a8v+oYSZLUEQZodd2oEF01Gp3b0gErR5/LAnX8fnF4L7sgMOcYSZI0JzZouwCpAaMuyCtrnYi3T9IXXdaWUTazR1m9uRcXSpKkljgCrT7JGY0e1dKR2pYK2nHoHhWeY6lFVlL1SpKkOWOAVt9UzdIx3A/rB+aqlo5RI9BEr81pyVjKOCZ3vyRJmiEDtLooJ1DWnaUjtb2sraPs8VK0PXe2jVHHOBItSdIc6WuAfjBwErBdy3VoOsZZ1KRq3zhzRo8KyFWfXfUDwAsKJUmac30N0BsBLwUe0XYhmgvjtnQUt496TvQ8dRFgzoWBVRcdpj5PkiTNWJdn4bihYt/wh8HngHsHjx893XI05+IZNeJ9qRHnsvmhU+8JK0ed615UmMt2DkmSWtblAH0ycDRw1+Dx2sK+jYHDgXOBn8++NLWgTr9xWYgevk9qf9W2uFXDkCtJkubWnsC1wIXAkwrbNyMEmr1mVMcDwHEz+iyNltvmULWwSc6iJ3U+S/PjGOCitouQJHVX13ugvwXsBFwOfBd4Dd0/J83OOBcAjmr/kCRJPdeHsPlr4GXAi4C3AWcD27RakdpWNYVdmTrT3cXbXPREkqQF0uUe6NipwPnAicAFDb3n54CnZRy3BGzR0GeqOVUX9NVVtgx4Tq+0JEnqkT4FaICbgecChwBPAa6f8P0+DpyTcdwHsadyXtWZM3rUvpwQXTXbhyRJkgbWAAe0XYRmxn7nbvMiQknSRPrQAx1bDZwFbNt2Ieq8sqDsyLIkSQusjwF6CdgH2LTtQtR5BmVJkrSePgZoaV5VtX7YFiJJUkcYoKVm5ATgnAsVJUnSnOtjgL4f+BPgJ20XooUyztzTkiSpg/o2jR2EAHNu20VoYTmSLElSz/VxBFqSJEmaGgO0JEmSVIMBWpIkSarBAC1JkiTV0MeLCNvyX4Dftl3EGDYHdgDuaruQBqwGHgbc0XYhDdkSuLXtIhryMOCbbRcx8MS2C5AkdZszBjTjWuCxbRchKdtZwH5tFyFJkrrpHcA5bRfRkBcD17ddREN2I0zJ+JC2C2nApoRz2aXtQiRJaoI90JIkSVINBmhJkiSpBgO0JEmSVIMBWpIkSarBAC1JkiTVYICWJEmSajBAS5IkSTUYoCVJkqQaDNC6GfhZ20U05BbguraLaMgdhEVh7mu7kAbcSziXviyxLkmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSpFFWAYcB3wPuBq4G/gnYrM2iGvIPwA1tFzGBpwCfBW4Cbgb+L7BlqxWNZxVwBPAD4DfAVcD/AjZusyhJkqRxHQEsAx8FDgL+Bvg18B1gdYt1TWpP4AG6G6AfC9wOfBt4OXAccAfwjTaLGtMbCH/HPgAcDPxv4D7g/W0WJUmSNK4bgC9H215ACDz7zr6cRjwMuIbwQ6CrAfo9wA+BTQrbDgEuBh7dSkXjuxL4QrTtvYR/8ejyjzRJ0oJb1XYBasXmwKOAM6Lt3xrcbz/bchrzPuAXwMltFzKm1cChwEeA3wIbEP43+q/ArnTvR8HGhD+PopsJ57nh7MuRJEka38bAdsCm0fbhCPTzZl7R5P6S0Gf7JOCDdC9sAmxN+P5fDpxFOJ87CQF6ixbrGtfbgDXA/oR/HdgD+DlwUos1SZIkNeZZwG2EC742aLmWurYCfgm8ZvC8qwH6aYQAfQ/wMcKPgiMJPdEX0L1/MVoCvkI4p+Htu8BGbRYlSZI0qd8nXOR1P/AfwDbtllPbEqGX+2usC5hdDdD/lRAyT4m2D/9lYO+ZVzS+JcJI8x3AUcCfAn9F+HM5G1s4JElSRz2b8E/qtwKvpZuh5sWEi9J2JEzBtxlwInDj4PGD2yuttp0IQfnF0faHDbYfPfOKxrc7oeYXRdv3GGx/4cwrkiSpIV37p3o1Z1/CRYRnEwLbbe2WM7anEELy9xP77gA+TrgwrwtuHNzH8yQPf9ismWEtk3r84P4/o+2XDO63nWEtkiRJE1sNXEcI0F3rq409Adgrup1K6Inei3CxZJd8hzDn81Jh23DO7p1bqWg8w9H0V0Xb/2Kw/Tkzr0iSpIYsjT5EPbQHYaGOT5IeuT27ZHtXfJBwAV7X5k2GECxPA84k9EJvDxwDfAZ4aYt11bVEWE3xAML0gt8lzJDyBuAKwoI3a1urTpIkqaZDWTkzQnw7vLXKmtHViwiHngOcS1gQ5grCKpFdbLfahLB09+WEmUV+DPwj/VguXpIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSQWPBS4C1gL7Aq8BloEDBvsPAA6PXpPaNq7tBp+3e7T9cOADDX3GOMrqqnIt8LeDx08Evgds1GhVkiSp1Kq2C9DCOAzYEXghcCFwA/B14NbB/mkH6JQ/AI4D/n6KnzFtPwEuBt7QdiGSJC0KA7Rm5SHAdcAXgTuA04A/By5osaa/Br4J3NhiDU34IHA08KC2C5EkSVIzziC0KQxv+xLC8zLwOODcaP/uJduGDgTOB+4CriG0M2xY2P8g4HjgasII9xeBvaL32RC4CfiLqNbLgKOAYwmju7cA/0xokXjn4PPuBE5mZWBdBRwJXDqo6/vAm4HVNevKOb9rWdfCAbAE/BA4FEmSJPXCVsCJhOD4OEKQLAborYFPAd8dPN+4ZBvAKwav+yRwMKH94nfAvxY+74zBtncChwCfJYx6F4PqHoR+7AdHtV5GGCn/FLDP4P2XCS0nnyOE/3cMth1ZeN3wuPcT2lTeDdwPfKhmXTnndy0rAzTAewmj+pIkSeqJ9wBXFp4XAzTAv7B+O0e87UHAL4FPRMcdMXivpwLPHDw+orB/CTiVlUH1WEI4j11GGEUetjctEQL15awbTV4CrgJOGDzfCrgX+Ifovd5CCOmPy6wr5/wgHaD/B/ArVo54S5KkKbAHWl2yHbAl8LFo+8mD+z8Gng48AJxU2L+ceM2TCS0aKecN3mP42l8SWkruj7YNw+pOhBaP4ijxsK7VwNMy68o5vzJXA79HGLmXJElTtEHbBUg1PGZw/1XWBdyiLYBNCf3Fv4323RA93wr4ccnn3Je5behRg/ubou3D59sAj8ioK+f8ytw2uN+K9c9VkiQ1yACtLhnOlnEwcEVi/22E/uMtgU1YGVbjkdnlBuv6+eB+GJIpPIdQ99qMunLOr8zwfPxXJUmSpsz/2GqeLI3YdiWwBth+8Hh4G86Q8VDCHNOrgJdF73Fo9L6/oHpEt47vE0aoXxRtfxFhJPk/M+vKOb8yw3OJR8ElSVLDHIHWvLgfeAKwN/AfhAviUtveTpiZYgvCVG9/BLwK+Cnh4rprgNMJs2E8jhBu9wd2jj7vcuC/N1T7LYRZMN5EuBDwPGA3wuImHyb0J5NR15qM8yuzLfBrDNCSJEm9MWoWjj0JM2D8BtilYtsSYRT3EuBuwgwZ7wU2K7z3Jqybb/k2wkwXjyZ/Grv3RdsuJixWUnQu8JHC81XA6wnLat89eJ83sXJWjJy6cs7vWtLT2J2OJEmSNCXDhVT2b7uQBiwRAvvLRh0oSZIkTeJvCQumdN1TCSPaLuUtSZKkqXo4YQaNrs+dfCJh0RZJkiRp6g4HPtB2ERN4IqHveqO2C5EkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZKk+fP/Aasz0x5DMCYFAAAAAElFTkSuQmCC"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mcp.fnc(m_lmer_log)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, let looks pretty good and we could proceed with this model. However, <a href=\"http://www.imachordata.com/do-not-log-transform-count-data-bitches/#comment-8536698873534339918\">previous research</a> has suggested that log-transforming count data is not always reasonable and one should consider generalized (mixed-effects) linear regression models."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Poisson GLMER\n",
"\n",
"Let us start with a Poisson GLMER, which is a first choice when working with count data."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0226215 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?"
]
}
],
"source": [
"m_poisson = glmer(body_length~1+session_index+session_comments+(1|author),data=data,family=poisson())"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Generalized linear mixed model fit by maximum likelihood (Laplace\n",
" Approximation) [glmerMod]\n",
" Family: poisson ( log )\n",
"Formula: body_length ~ 1 + session_index + session_comments + (1 | author)\n",
" Data: data\n",
"\n",
" AIC BIC logLik deviance df.resid \n",
" 85369382 85369430 -42684687 85369374 999996 \n",
"\n",
"Scaled residuals: \n",
" Min 1Q Median 3Q Max \n",
"-70.037 -3.721 -0.068 1.093 259.619 \n",
"\n",
"Random effects:\n",
" Groups Name Variance Std.Dev.\n",
" author (Intercept) 0.9972 0.9986 \n",
"Number of obs: 1000000, groups: author, 527435\n",
"\n",
"Fixed effects:\n",
" Estimate Std. Error z value Pr(>|z|) \n",
"(Intercept) 4.688e+00 1.382e-03 3392 <2e-16 ***\n",
"session_index -2.831e-03 2.722e-05 -104 <2e-16 ***\n",
"session_comments 4.067e-03 2.066e-05 197 <2e-16 ***\n",
"---\n",
"Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
"\n",
"Correlation of Fixed Effects:\n",
" (Intr) sssn_n\n",
"session_ndx -0.010 \n",
"sssn_cmmnts -0.016 -0.644\n",
"convergence code: 0\n",
"Model failed to converge with max|grad| = 0.0226215 (tol = 0.001, component 1)\n",
"Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?\n"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(m_poisson)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mcp.fnc(m_poisson)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The convergence message shows low magnitude and for such large data this might be a false positive as emphasized in the lme4 docu. \n",
"\n",
"The residual plots for generalized linear models are generally quite difficult to interpret as the residuals do not necessarily need to follow a normal distribution any longer. What we can do though as a first step here, is to look at overdispersion (see http://glmm.wikidot.com/faq)."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"overdisp_fun <- function(model) {\n",
" ## number of variance parameters in \n",
" ## an n-by-n variance-covariance matrix\n",
" vpars <- function(m) {\n",
" nrow(m)*(nrow(m)+1)/2\n",
" }\n",
" model.df <- sum(sapply(VarCorr(model),vpars))+length(fixef(model))\n",
" rdf <- nrow(model.frame(model))-model.df\n",
" rp <- residuals(model,type=\"pearson\")\n",
" Pearson.chisq <- sum(rp^2)\n",
" prat <- Pearson.chisq/rdf\n",
" pval <- pchisq(Pearson.chisq, df=rdf, lower.tail=FALSE)\n",
" c(chisq=Pearson.chisq,ratio=prat,rdf=rdf,p=pval)\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<dl class=dl-horizontal>\n",
"\t<dt>chisq</dt>\n",
"\t\t<dd>82516289.7393142</dd>\n",
"\t<dt>ratio</dt>\n",
"\t\t<dd>82.5166198057934</dd>\n",
"\t<dt>rdf</dt>\n",
"\t\t<dd>999996</dd>\n",
"\t<dt>p</dt>\n",
"\t\t<dd>0</dd>\n",
"</dl>\n"
],
"text/latex": [
"\\begin{description*}\n",
"\\item[chisq] 82516289.7393142\n",
"\\item[ratio] 82.5166198057934\n",
"\\item[rdf] 999996\n",
"\\item[p] 0\n",
"\\end{description*}\n"
],
"text/markdown": [
"chisq\n",
": 82516289.7393142ratio\n",
": 82.5166198057934rdf\n",
": 999996p\n",
": 0\n",
"\n"
],
"text/plain": [
" chisq ratio rdf p \n",
"8.251629e+07 8.251662e+01 9.999960e+05 0.000000e+00 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"overdisp_fun(m_poisson)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As expected, we have to deal with overdispersion. There are generally two ways of doing so: (i) adding an individual-level random effect or (ii) using a negative binomial model. Let us focus on the latter here. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00384051 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00587644 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0012443 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.001644 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00560769 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00286067 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0021682 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00100559 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00145904 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00276077 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0010975 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00124608 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.001501 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00152316 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00144913 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00201902 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0022362 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00222531 (tol = 0.001, component 1)Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?Warning message:\n",
"In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?"
]
}
],
"source": [
"m_nb = glmer.nb(body_length~1+session_index+session_comments+(1|author),data=data[1:10000,])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Generalized linear mixed model fit by maximum likelihood (Laplace\n",
" Approximation) [glmerMod]\n",
" Family: Negative Binomial(10.056) ( log )\n",
"Formula: body_length ~ 1 + session_index + session_comments + (1 | author)\n",
" Data: data[1:10000, ]\n",
"\n",
" AIC BIC logLik deviance df.resid \n",
"123423.9 123460.0 -61707.0 123413.9 9995 \n",
"\n",
"Scaled residuals: \n",
" Min 1Q Median 3Q Max \n",
"-3.0470 -0.2285 -0.0117 0.2037 3.5386 \n",
"\n",
"Random effects:\n",
" Groups Name Variance Std.Dev.\n",
" author (Intercept) 1.053 1.026 \n",
"Number of obs: 10000, groups: author, 9851\n",
"\n",
"Fixed effects:\n",
" Estimate Std. Error z value Pr(>|z|) \n",
"(Intercept) 4.596476 0.012340 372.5 < 2e-16 ***\n",
"session_index -0.010722 0.004323 -2.5 0.0131 * \n",
"session_comments 0.020064 0.003021 6.6 3.1e-11 ***\n",
"---\n",
"Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
"\n",
"Correlation of Fixed Effects:\n",
" (Intr) sssn_n\n",
"session_ndx -0.048 \n",
"sssn_cmmnts -0.198 -0.860\n",
"convergence code: 0\n",
"Model is nearly unidentifiable: very large eigenvalue\n",
" - Rescale variables?\n"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(m_nb)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"We get multiple similar convergence warnings as above for the Gaussian model; yet, some are with higher magnitude. Note that this model takes a very long time to fit for this sample of 1 mio. data points; it does not really scale for larger data which in our case consists of more than 50 mio. data points. This is why we used a much smaller sample here.\n",
"\n",
"The inference on the coefficients is similar to the other models, the session_index has a negative effect on the body_length and the total number of session_comments has a positive effect. The effect size of the session_index is larger here compared to the other models which might be reasoned partly by the small sample size."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Final model choice"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given our extensive analysis regarding the appropriate model for our data, we conclude that we will proceed with a linear model on the log-transformed body_length. The model assumptions regarding the distribution of residuals hold for that model and all diagnostics are fine. Also, an important note is that the forthcoming inference we make based on the model results would be the same for all models we have applied in this notebook---only the effect size would slightly differ. We encourage fellow researchers to further refine these models in future work if applicable."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Significance of effects"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have decided on a model, we want to extend our inference. First, we want to study the significance of the fixed effects as well as potential additional random effects. \n",
"\n",
"There are several options for determining the significance of a fixed effect on the model. For example, a common approach is to use the ratio of the slope to its standard error and then use a t-test for determining its significance. Alternatively, one can also use F-tests for contrasting simpler to more complex models. Both methods require a specification of the degrees of freedom though which is not trivial in mixed models; methods such as the Satterthwaite approximation or Kenward-Roger approximation can be used for that task. \n",
"As stated in Baayen 2008, we can also directly use the t-statistics given to derive statistical significance. Because we have a huge dataset, a t-statistic above 2 can be regarded as significant at the 5\\% level in a two-tailed test. You can derive this directly from the results above.\n",
"\n",
"However, here, we follow an approach used for comparing models: the Bayesian Information Criterion (alternatively we could also work with AIC or LRT). Basically, the idea is to specify different (in this case nested) models that successively incorporate additional fixed or random effects and then compare their BIC scores. The lower the BIC, the better a model is (above a small threshold).\n",
"\n",
"The baseline model we start with is:\n",
"\n",
"body_length = 1 + (1|author)\n",
"\n",
"The reason why we incorporate the random effect for author from the beginning is that this is necessary based on the design of the experiment. Also, as pointed out in http://glmm.wikidot.com/faq, you should not compare the fit of a (g)lmer with one from a (g)lm.\n",
"\n",
"Okay, so let us get the BIC for the baseline model."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"m1 = lmer(log(body_length)~1+(1|author), data = data, REML=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Linear mixed model fit by maximum likelihood ['lmerMod']\n",
"Formula: log(body_length) ~ 1 + (1 | author)\n",
" Data: data\n",
"\n",
" AIC BIC logLik deviance df.resid \n",
" 2950707 2950743 -1475351 2950701 999997 \n",
"\n",
"Scaled residuals: \n",
" Min 1Q Median 3Q Max \n",
"-4.0358 -0.6179 -0.0096 0.6077 4.4681 \n",
"\n",
"Random effects:\n",
" Groups Name Variance Std.Dev.\n",
" author (Intercept) 0.2590 0.5090 \n",
" Residual 0.9083 0.9531 \n",
"Number of obs: 1000000, groups: author, 527435\n",
"\n",
"Fixed effects:\n",
" Estimate Std. Error t value\n",
"(Intercept) 4.616913 0.001259 3668"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(m1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, let us incorporate the index."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"m2 = lmer(log(body_length)~1+session_index+(1|author), data = data, REML=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Linear mixed model fit by maximum likelihood ['lmerMod']\n",
"Formula: log(body_length) ~ 1 + session_index + (1 | author)\n",
" Data: data\n",
"\n",
" AIC BIC logLik deviance df.resid \n",
" 2949762 2949809 -1474877 2949754 999996 \n",
"\n",
"Scaled residuals: \n",
" Min 1Q Median 3Q Max \n",
"-4.0417 -0.6198 -0.0102 0.6085 4.4793 \n",
"\n",
"Random effects:\n",
" Groups Name Variance Std.Dev.\n",
" author (Intercept) 0.2560 0.5060 \n",
" Residual 0.9094 0.9536 \n",
"Number of obs: 1000000, groups: author, 527435\n",
"\n",
"Fixed effects:\n",
" Estimate Std. Error t value\n",
"(Intercept) 4.5984088 0.0013945 3297\n",
"session_index 0.0091623 0.0002972 31\n",
"\n",
"Correlation of Fixed Effects:\n",
" (Intr)\n",
"session_ndx -0.434"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(m2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Based on the lower BIC we can clearly see that the incorporation of the session_index improves our model, thus, we make inference on it (also the t-value indicates high significance).\n",
"\n",
"What is interesting here though, is that the coefficient of the session_index indicates a positive effect. This might be the case because we have not incorporated the total session length yet as a covariate."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"m3 = lmer(log(body_length)~1+session_index+session_comments+(1|author), data = data, REML=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Linear mixed model fit by maximum likelihood ['lmerMod']\n",
"Formula: log(body_length) ~ 1 + session_index + session_comments + (1 | \n",
" author)\n",
" Data: data\n",
"\n",
" AIC BIC logLik deviance df.resid \n",
" 2948155 2948214 -1474073 2948145 999995 \n",
"\n",
"Scaled residuals: \n",
" Min 1Q Median 3Q Max \n",
"-4.0579 -0.6176 -0.0097 0.6076 5.0837 \n",
"\n",
"Random effects:\n",
" Groups Name Variance Std.Dev.\n",
" author (Intercept) 0.2539 0.5039 \n",
" Residual 0.9091 0.9535 \n",
"Number of obs: 1000000, groups: author, 527435\n",
"\n",
"Fixed effects:\n",
" Estimate Std. Error t value\n",
"(Intercept) 4.5884874 0.0014146 3244\n",
"session_index -0.0039075 0.0004410 -9\n",
"session_comments 0.0119275 0.0002972 40\n",
"\n",
"Correlation of Fixed Effects:\n",
" (Intr) sssn_n\n",
"session_ndx -0.158 \n",
"sssn_cmmnts -0.176 -0.739"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(m3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As expected based on our initial empirical analysis in the paper, the session length is an important effect to incorporate. Now, also the session_index indicates a negative effect on the body_length.\n",
"\n",
"Finally, one might also argue that it is necessary to incorporate a random effect for the intercept based on different subreddits."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"m4 = lmer(log(body_length)~1+session_index+session_comments+(1|author)+(1|subreddit), data = data, REML=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Linear mixed model fit by maximum likelihood ['lmerMod']\n",
"Formula: log(body_length) ~ 1 + session_index + session_comments + (1 | \n",
" author) + (1 | subreddit)\n",
" Data: data\n",
"\n",
" AIC BIC logLik deviance df.resid \n",
" 2868841 2868912 -1434414 2868829 999994 \n",
"\n",
"Scaled residuals: \n",
" Min 1Q Median 3Q Max \n",
"-4.7714 -0.6148 -0.0017 0.6138 4.6789 \n",
"\n",
"Random effects:\n",
" Groups Name Variance Std.Dev.\n",
" author (Intercept) 0.1664 0.4080 \n",
" subreddit (Intercept) 0.2037 0.4513 \n",
" Residual 0.8724 0.9340 \n",
"Number of obs: 1000000, groups: author, 527435; subreddit, 16414\n",
"\n",
"Fixed effects:\n",
" Estimate Std. Error t value\n",
"(Intercept) 4.6150570 0.0052243 883.4\n",
"session_index -0.0038887 0.0004307 -9.0\n",
"session_comments 0.0112663 0.0002879 39.1\n",
"\n",
"Correlation of Fixed Effects:\n",
" (Intr) sssn_n\n",
"session_ndx -0.041 \n",
"sssn_cmmnts -0.032 -0.746"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(m4)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mcp.fnc(m4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again, the BIC shows an improvement in the model. Yet, the inference on the fixed effects does not change.\n",
"\n",
"We stop at this point as our scope of interest is covered. However, one can arbitrarily extend the existing model. For example, it might be of interest to also allow the slope for the session_index vary between different authors, subreddits or maybe even different session lengths."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Collinearity\n",
"\n",
"Finally, we also want to check for multicollinearity in our model; some explanations and code for checking that in lmer is provided in https://hlplab.wordpress.com/2011/02/24/diagnosing-collinearity-in-lme4/."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"source(\"mer-utils.R\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"4.61691059448745"
],
"text/latex": [
"4.61691059448745"
],
"text/markdown": [
"4.61691059448745"
],
"text/plain": [
"[1] 4.616911"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kappa.mer(m4)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"2.25116052478963"
],
"text/latex": [
"2.25116052478963"
],
"text/markdown": [
"2.25116052478963"
],
"text/plain": [
"[1] 2.251161"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max(vif.mer(m4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Both the condition number and VIF show no reasons to be concerned about collinearity in this model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.2.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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