{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8a0ae87a",
   "metadata": {},
   "source": [
    "# Chapter 12: Modeling categorical relationships"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f0dfef5",
   "metadata": {
    "Rmd_chunk_options": "echo=FALSE,warning=FALSE,message=FALSE",
    "jupyter": {
     "output_hidden": false
    },
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘sfsmisc’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:dplyr’:\n",
      "\n",
      "    last\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse)\n",
    "library(ggplot2)\n",
    "library(kableExtra)\n",
    "library(BayesFactor)\n",
    "library(sfsmisc)\n",
    "library(cowplot)\n",
    "theme_set(theme_minimal(base_size = 14))\n",
    "\n",
    "library(knitr)\n",
    "\n",
    "set.seed(123456) # set random seed to exactly replicate results\n",
    "\n",
    "# load the NHANES data library\n",
    "library(NHANES)\n",
    "\n",
    "# drop duplicated IDs within the NHANES dataset\n",
    "NHANES <-\n",
    "  NHANES %>%\n",
    "  dplyr::distinct(ID,.keep_all=TRUE)\n",
    "\n",
    "NHANES_adult <-\n",
    "  NHANES %>%\n",
    "  drop_na(Weight) %>%\n",
    "  subset(Age>=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7560e7d2",
   "metadata": {},
   "source": [
    "## Table 12.1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f1fdf81a-0a3b-4269-bfbf-b6f03f97a6ce",
   "metadata": {
    "Rmd_chunk_options": "candyDf, echo=FALSE",
    "jupyter": {
     "output_hidden": false
    },
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 3 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Candy Type</th><th scope=col>count</th><th scope=col>nullExpectation</th><th scope=col>squared difference</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>chocolate</td><td>30</td><td>33.33333</td><td>11.1111111</td></tr>\n",
       "\t<tr><td>licorice </td><td>33</td><td>33.33333</td><td> 0.1111111</td></tr>\n",
       "\t<tr><td>gumball  </td><td>37</td><td>33.33333</td><td>13.4444444</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3 × 4\n",
       "\\begin{tabular}{llll}\n",
       " Candy Type & count & nullExpectation & squared difference\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t chocolate & 30 & 33.33333 & 11.1111111\\\\\n",
       "\t licorice  & 33 & 33.33333 &  0.1111111\\\\\n",
       "\t gumball   & 37 & 33.33333 & 13.4444444\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3 × 4\n",
       "\n",
       "| Candy Type &lt;chr&gt; | count &lt;dbl&gt; | nullExpectation &lt;dbl&gt; | squared difference &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| chocolate | 30 | 33.33333 | 11.1111111 |\n",
       "| licorice  | 33 | 33.33333 |  0.1111111 |\n",
       "| gumball   | 37 | 33.33333 | 13.4444444 |\n",
       "\n"
      ],
      "text/plain": [
       "  Candy Type count nullExpectation squared difference\n",
       "1 chocolate  30    33.33333        11.1111111        \n",
       "2 licorice   33    33.33333         0.1111111        \n",
       "3 gumball    37    33.33333        13.4444444        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "candyDf <-\n",
    "  tibble(\n",
    "    `Candy Type` = c(\"chocolate\", \"licorice\", \"gumball\"),\n",
    "    count = c(30, 33, 37)\n",
    "  )\n",
    "# kable(candyDf, caption='Counts of different candies in our bag.')\n",
    "\n",
    "# compute chi-squared statistic\n",
    "\n",
    "candyDf <- candyDf %>%\n",
    "  mutate(nullExpectation =c(1 / 3, 1 / 3, 1 / 3) * sum(candyDf$count),\n",
    "         `squared difference`=(count - nullExpectation)**2)\n",
    "candyDf\n",
    "# kable(candyDf, digits=3, caption='Observed counts, expectations under the null hypothesis, and squared differences in the candy data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acd99778-33d6-46ba-b14f-afa2643b836c",
   "metadata": {},
   "source": [
    "## Figure 12.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9cd39837-6574-4afd-a651-6969b57d517f",
   "metadata": {
    "Rmd_chunk_options": "candyDf, echo=FALSE",
    "jupyter": {
     "output_hidden": false
    },
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“\u001b[1m\u001b[22mRemoved 11 rows containing non-finite values (`stat_bin()`).”\n",
      "Warning message:\n",
      "“\u001b[1m\u001b[22mRemoved 2 rows containing missing values (`geom_bar()`).”\n"
     ]
    },
    {
     "data": {
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VqxcQlFXSntKO22GUbOVxQfYtaRnK8QOK2h8xXViDzB2neELFtxxpO8sBC2voDm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\noMsKGv2y7G8syGoM5uQEG68+5W2rWbBgaUf+os3Ls7KWb1V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GPuO48YJxxqj1I+ySkesjMGwDAc7BxKnVq9zu2nPaGQwStKGWMBwMp61i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\nICAvSW5mbyAxMiAwIFIKPj4Kc3RhcnR4cmVmCjY3MzM1CiUlRU9GCg==",
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ItrMQ0mQhPMrHt9aALcf3F+gp1Vksoyi/NL0/56t/jS/Yo/70v/AH9b/GgCxRVf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\nv/fz/wCtViigCv5tx/zwX/v5/wDWqCOSf7ZMfJXOxMjzP972q/V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4oA739m/wAS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\ngqzQAUUUUAV5v+Pu3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Il19/D2lPrq4xqj\nWURuhxj/AFu3d096APFPgjeWfwv+IHxB0DxPqNrpd+8OlX8Ut7OsQuIFsY4XdGYgMEkhdSR0\n/Gun/Zcje48A6zrSoyWOveI9U1axDArm2luWMbAHoGA3D1BB716P4j8E+HfGH2f+3tA0vW/s\n53Q/2jZx3HlH1Xepx+Fa8USQRJHGixxoAqoowFA6ADsKAGXMDXEYVZXiIOdydarf2ZL/AM/s\n/wCdX6KAPE/jR+ztD8dNRtdP1bxx4t0XQYoHjvtI0DUBaQ6ojlcx3DBS5TAIwrLkMa9H8KeA\ntM8DeG9N8P6BGNJ0XToFtrSytRtjhjUYCgf16k8nmtaL/kMz/wDXIVfoAof2ZL/z+z/nR/Zk\nv/P7P+dX6KAM9tNlCn/TZ+nrUdtp8j20TC8mUFAcA8DitJ/uN9Kjsv8Ajzg/65r/ACoArf2Z\nL/z+z/nR/Zkv/P7P+dX6KAM86ZIR/wAfs/51xHij4KeCPE97Amt+FNC1ozFtzahpcE5/8fQ1\n6PWfe/8AISsPq/8AIUAeKar+wp8CNZJNx8LvC8ef+fXS4rf/ANFha5uX/gmv+z1JIZE8Ax2j\nn+Ky1K9tz/5DmWvp+igD5fH/AATm+EEHFivinTB2Fp4q1FcfnOacP+Cf3gqH/j18b/Eqx9Ps\n/jG8GPzc19PUUAfLtv8AsI6RiT7P8X/jFZgOVxB42uR0+uamP7DKgHZ8e/jimTn/AJHeVv8A\n0JDX0pZ9Jf8Arq386sUAfMg/YgnV9y/tB/HBcdB/wlykfrAaRP2I75GyP2i/jgT/ALXiiE/z\ntq+nKKAPlfUv2LdVgtwx/aK+M8g3AbX8QQEf+k9XZP2L9eJGz9o/4xqP9rWrY/8AtuK+jNa/\n48h/vr/Or9AHy4P2JNaW4a4H7RfxdE7LtMo1a23Eemfs+cVh6P8A8E6LHw9Y6tZaV8Y/iDpd\nnq6GPUYLJ9PhS9UqVImC2oEgwzD5s8MfWvr+igD4q0//AIJi6PpHgbUfBlh8XvHlh4S1F2kv\nNEtTYxWlwx25Z4ltwpJ2Jzj+EelQD/gmPpbeB4fAa/Gb4iw+ELNhNBo8N1ax28bGR5MqqwDH\nzs7fVjX25VeP/j+n/wBxP5tQB8fav/wTh/t3w/pWh33x7+Kc2k6Uix2VqNTgVYFVNigbYgeF\n45rR1f8AYD1PXbywur39or4tyz2PNuy6vEmzkHosQB5Udc9K+uKKAPk65/YS1271mDVJP2kv\njB9shUKhXWolTHPWMRBD1PUGll/Yg8R217camP2lvjE8/lnMT63G0BG3H+pMXljp/d9T3r6w\nqC+/48rj/rm38qAPljRv2J/Elo8l7D+0p8XxJcjc0cusQyxLk5wkbxFUH+6B6dKlsf2IfEun\nQXEUX7S/xhdZxhjPrUUzDr91niJXr/CR+lfUGnf8eEH+4Ks0AfK0X7EXia302Wyj/aX+MHlS\nHJeTWYnkHTpIYt46dmFNm/Yf8TXGkDTpP2lfi48HctqsJduScF/K3nr3avquigD441//AIJ7\nXmu+H08OXnx8+JU+jT58+zlubYxT4A/1iiEb8kA/PnkA9axtA/4JaaH4O8R2+veGPix4y8Ma\nxEjRG+0WCwspmQkFlLw26FgdoyGyDX2xN/x92/0b+QqxQB8AeIv+CO/gPxV4hu9a1T4h+K73\nUL2Z7m7uJoLEyTyuxZ5GYQDLMzEkkEkmui8e/wDBMK1+KOtW+o+LPjl8R9dktkMdubq7gLQq\ncZCER4UEjJAUZ/Wvt2igD4r0P/gmq/h/w3f+G7P4+/E+28O3hLPp0GoxKhO4HPKHn5V5AB4/\nCvof4GfClvhP8MdJ8Ix+JdX19NLMsY1LVZvMuZ90jPl27437R7KBXptUNH/1E3/XVqAD+zJf\n+f2f86vKMADOcd6WigAooooAKKKKACvGv2jZU0e5+GniG7YR6Ro/iq3lv534jt45IpYVlc9A\nqvIgJPTdXstQ3llb6jaTWt3BFdW0ylJIZkDo6nqGU8EH0NAHi2t61p/i79pnwU+kX1tew+HN\nD1K71O5t5VeOFLgwpCrsDgEmN2AJzgE9Kj8eeI9Jtvjb8JvFx1Ozm8MXlnqemxask6NaieVY\nni/eg7fnETgHPJXFetaD4M8P+FbGay0TQtN0eynJMtvYWccEchIwSyoADx60r+DdAk8Or4fb\nQ9NbQVQRrpZtIzahQchRFjbjPbFAHyj8d3Xxn40+IniXQ2GqaP4a0nQ4L+WzPmJJJFqJu5lB\nXIYxw/MwHToa9K+MmvaX8SfFfwo0Pw1qtpqd9JrZ1kyWM6yiG0itZg0jFSdqkyooJ6k4Fe16\nL4d0nw3pi6bpGmWel6cudtpZW6QxDPXCKAOfpVTw94G8N+Ep7mbQ/D+laNNcnM8mn2UcDS9/\nmKKN340AfNGkeNdG0z9iC+8PXN1bx+IYNGuvDkmjGRftQvyXgWLyvvFi5BAx0OelfTfg7TJ9\nF8I6Hp10c3NpYwW8pznLrGqnn6g1FJ4E8NS+Il19/D2lPrq4xqjWURuhxj/W7d3T3rdoAq3N\nm9xIGW4khAGNqHiov7Ml/wCf2f8AOr9FAH59eGf+CY//AAlX7WHiT4u+Otcsbzw9NrMmq6bo\nNgJGklfdmM3DsqhQpAbYu7cQMtjIP2h/wqPw9/z7v/39f/4qum0T/jxH+8386v0AcV/wqPw9\n/wA+7/8Af1//AIqj/hUfh7/n3f8A7+v/APFV2tFAHFf8Kj8Pf8+7/wDf1/8A4qj/AIVH4e/5\n93/7+v8A/FV2tFAHDW/wn8PzQJIbZgWGcCV//iqk/wCFR+Hv+fd/+/r/APxVdbYf8ecP+6Ks\nUAcV/wAKj8Pf8+7/APf1/wD4qj/hUfh7/n3f/v6//wAVXa0UAcV/wqPw9/z7v/39f/4qo5/h\nN4figkcWzEqpbHmv2H+9Xc1De/8AHnP/ANc2/lQBxdr8KPD9xbRyG2YFhnAlf/4qpf8AhUfh\n7/n3f/v6/wD8VXWab/x4Qf7gqzQBxX/Co/D3/Pu//f1//iqP+FR+Hv8An3f/AL+v/wDFV2tF\nAHFf8Kj8Pf8APu//AH9f/wCKqOf4TeH4oZHFsxKqTjzX9P8AeruahvP+PSf/AHG/lQBx6/CT\nw+yg/Z35H/PV/wD4ql/4VH4e/wCfd/8Av6//AMVXZx/cX6CnUAcV/wAKj8Pf8+7/APf1/wD4\nqj/hUfh7/n3f/v6//wAVXa0UAcV/wqPw9/z7v/39f/4qq998K9AtLV5RbMxXHBlf1x/ervap\naz/yDZvw/mKAOWX4SeH2UH7O/Iz/AK1//iqX/hUfh7/n3f8A7+v/APFV2cf+rX6CnUAcV/wq\nPw9/z7v/AN/X/wDiqP8AhUfh7/n3f/v6/wD8VXa0UAcV/wAKj8Pf8+7/APf1/wD4quUs/wBl\nj4XeHvFtp4u03wZpFj4mtJvNh1W2tVjuFdsqzF1wSSGbrnqa9gqve/8AHv8A8DT/ANCFAHPX\nvw70vUNTOoTmZr0sreesrqwKgBSMEYIwPyrhvjb+zJ4f+OPw68QeEdU1TVdOh1uOKK5v7O4L\nXG2OWOUDMm5SCYlBBB46YOCPYqKAPm/9lf8AYk8P/sqaBr2k6H4n1vVYdWu0une5l8hkKptC\n/uioPrkive7XQjZxlI7+7YE5zLM0h/NiT+FatFAHxV+3N/wT51f9rzxX4a1iy8c2/h9NHsZL\nTyLyxa4MhaTcWDB1xxgYx2r6Q8AfDl/hx8PfCHh+TUpr2fSNMstKaVZHELGGFI96RkkLnZkd\n+frXotUNX+5bf9d1/rQAf2ZL/wA/s/51Na2r27MWnkmz2c9Ks0UAFFFFABRRRQAUUUUAFFFF\nABRRRQAUUUUAFFFFAFCL/kMz/wDXIVfqhF/yGZ/+uQq/QAUUUUANf7jfSo7L/jzg/wCua/yq\nR/uN9Kjsv+POD/rmv8qAJqKKKACs+9/5CVh9X/kK0Kz73/kJWH1f+QoA0KKKKACiiigCvZ9J\nf+urfzqxVez6S/8AXVv51YoAKKKKAKGtf8eQ/wB9f51fqhrX/HkP99f51foAKKKKACq8f/H9\nP/uJ/NqsVXj/AOP6f/cT+bUAWKKKKACoL7/jyuP+ubfyqeoL7/jyuP8Arm38qAG6d/x4Qf7g\nqzVbTv8Ajwg/3BVmgAooooArzf8AH3b/AEb+QqxVeb/j7t/o38hVigAooooAKoaP/qJv+urV\nfqho/wDqJv8Arq1AF+iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAoaJ/x4j/AHm/\nnV+qGif8eI/3m/nV+gAooooAKKKKAK9h/wAecP8AuirFV7D/AI84f90VYoAKKKKACob3/jzn\n/wCubfyqaob3/jzn/wCubfyoAZpv/HhB/uCrNVtN/wCPCD/cFWaACiiigAqG8/49J/8Acb+V\nTVDef8ek/wDuN/KgCSP7i/QU6mx/cX6CnUAFFFFABVLWf+QbN+H8xV2qWs/8g2b8P5igC3H/\nAKtfoKdTY/8AVr9BTqACiiigAqve/wDHv/wNP/QhViq97/x7/wDA0/8AQhQBYooooAKKKKAC\nqGr/AHLb/ruv9av1Q1f7lt/13X+tAF+iiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo\nAqpasuoSTkjYyBQO9WqwZPH3hiHxEugSeI9JTXWIC6W19ELok8jEW7d+lb1ABRRRQAjDKkeo\nplvGYoI0OCVUKcewqSigAooooAKq3Fq013bSggLFuyD15FWq4/VvjJ4A0DUrjT9T8c+G9Ov7\ndtk1rd6vbxSxN6MjOCD7EUAdhRUFle2+p2VveWdxFdWlxGssM8Dh45EYZVlYcEEEEEdc1PQA\nUUUUARQRGIPkg7nLce9S0UUAF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GOVMm46ihQP3It0oCQAlBqrOJT2AooU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\nRq51NiTR7Gw4GVFC9VXZ1DoblkO1VKdGDCTXRfb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       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "application/pdf": {
       "height": 420,
       "width": 420
      },
      "image/jpeg": {
       "height": 420,
       "width": 420
      },
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       "height": 420,
       "width": 420
      },
      "image/svg+xml": {
       "height": 420,
       "isolated": true,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chisqVal <-\n",
    "  sum(\n",
    "    ((candyDf$count - candyDf$nullExpectation)**2) / candyDf$nullExpectation\n",
    "  )\n",
    "\n",
    "xvals <- seq(0.01, 20, 0.01)\n",
    "dfvals <- c(1, 2, 4, 8)\n",
    "chisqDf <-\n",
    "  data.frame(xvals, dfvals) %>%\n",
    "  complete(xvals, dfvals)\n",
    "chisqDf <-\n",
    "  chisqDf %>%\n",
    "  mutate(chisq = dchisq(x = xvals, df = dfvals),\n",
    "         dfvals= as.factor(dfvals)) %>%\n",
    "  group_by(dfvals) %>%\n",
    "  mutate(chisqNorm = chisq / max(chisq),\n",
    "         Df=dfvals\n",
    "         )\n",
    "\n",
    "\n",
    "p1 <- ggplot(chisqDf, aes(xvals, chisqNorm, group = Df, linetype = Df)) +\n",
    "  geom_line() +\n",
    "  theme(legend.position = c(0.8, 0.7)) +\n",
    "  labs(\n",
    "    fill = \"Degrees of freedom\",\n",
    "    color = \"Degrees of freedom\",\n",
    "    x = \"Chi-squared values\"\n",
    "  ) + ylab('Density')\n",
    "\n",
    "\n",
    "# simulate 50,000 sums of 8 standard normal random variables and compare\n",
    "# to theoretical chi-squared distribution\n",
    "\n",
    "# create a matrix with 50k columns of 8 rows of squared normal random variables\n",
    "d <- replicate(50000, rnorm(n = 8, mean = 0, sd = 1)**2)\n",
    "# sum each column of 8 variables\n",
    "dMean <- apply(d, 2, sum)\n",
    "\n",
    "# create a data frame of the theoretical chi-square distribution\n",
    "# with 8 degrees of freedom\n",
    "csDf <-\n",
    "  data.frame(x = seq(0.01, 30, 0.01)) %>%\n",
    "  mutate(chisq = dchisq(x, 8))\n",
    "\n",
    "pval <- pchisq(chisqVal, df = 2, lower.tail = FALSE) #df = degrees of freedom\n",
    "\n",
    "p2 <- ggplot(data.frame(dMean),aes(dMean)) +\n",
    "  geom_histogram(aes(y=after_stat(density)),bins=100, fill='gray') +\n",
    "  geom_line(data=csDf,aes(x,chisq),linetype='dotted',linewidth=1.5)+\n",
    "  xlim(0,30) + ylim(0,.12) +\n",
    "  labs(\n",
    "    y = \"Density\",\n",
    "    x = \"Sum of squared random normal variables\"\n",
    "  )\n",
    "\n",
    "plot_grid(p1, p2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75444b40",
   "metadata": {},
   "source": [
    "## Table 12.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "218a6a6e-9bd5-4632-b6a3-fc1534bfbf24",
   "metadata": {
    "Rmd_chunk_options": "policeCT, echo=FALSE",
    "jupyter": {
     "output_hidden": false
    },
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 2 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>searched</th><th scope=col>Black</th><th scope=col>White</th><th scope=col>Black (relative)</th><th scope=col>White (relative)</th></tr>\n",
       "\t<tr><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>FALSE</td><td>36244</td><td>239241</td><td>0.129529827</td><td>0.85500622</td></tr>\n",
       "\t<tr><td> TRUE</td><td> 1219</td><td>  3108</td><td>0.004356497</td><td>0.01110746</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 2 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " searched & Black & White & Black (relative) & White (relative)\\\\\n",
       " <lgl> & <int> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t FALSE & 36244 & 239241 & 0.129529827 & 0.85500622\\\\\n",
       "\t  TRUE &  1219 &   3108 & 0.004356497 & 0.01110746\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 2 × 5\n",
       "\n",
       "| searched &lt;lgl&gt; | Black &lt;int&gt; | White &lt;int&gt; | Black (relative) &lt;dbl&gt; | White (relative) &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| FALSE | 36244 | 239241 | 0.129529827 | 0.85500622 |\n",
       "|  TRUE |  1219 |   3108 | 0.004356497 | 0.01110746 |\n",
       "\n"
      ],
      "text/plain": [
       "  searched Black White  Black (relative) White (relative)\n",
       "1 FALSE    36244 239241 0.129529827      0.85500622      \n",
       "2  TRUE     1219   3108 0.004356497      0.01110746      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load police stop data after preprocessing using code/process_CT_data.py\n",
    "stopData <-\n",
    "  read_csv(\"https://raw.githubusercontent.com/statsthinking21/statsthinking21-figures-data/main/CT_data_cleaned.csv\",show_col_types = FALSE) %>%\n",
    "  rename(searched = search_conducted)\n",
    "\n",
    "# compute and print two-way contingency table\n",
    "summaryDf2way <-\n",
    "  stopData %>%\n",
    "  count(searched, driver_race) %>%\n",
    "  arrange(driver_race, searched)\n",
    "\n",
    "summaryContingencyTable <-\n",
    "  summaryDf2way %>%\n",
    "  spread(driver_race, n)\n",
    "\n",
    "# Compute and print contingency table using proportions\n",
    "# rather than raw frequencies\n",
    "summaryContingencyTable <-\n",
    "  summaryContingencyTable %>%\n",
    "  mutate(\n",
    "    `Black (relative)` = Black / nrow(stopData), #count of Black individuals searched / total searched\n",
    "  `White (relative)` = White / nrow(stopData)\n",
    "  )\n",
    "\n",
    "summaryContingencyTable\n",
    "\n",
    "\n",
    "# kable(summaryContingencyTable, digits=3, caption='Contingency table for police search data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8136eac",
   "metadata": {},
   "source": [
    "## Chi-squared test result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "739fb479-17cc-4d0f-b80c-b9d4a43e57f2",
   "metadata": {
    "Rmd_chunk_options": "echo=FALSE",
    "jupyter": {
     "output_hidden": false
    },
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's Chi-squared test\n",
       "\n",
       "data:  summaryDf2wayTable\n",
       "X-squared = 828.3, df = 1, p-value < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first, compute the marginal probabilities\n",
    "\n",
    "# probability of being each race\n",
    "summaryDfRace <-\n",
    "  stopData %>%\n",
    "  count(driver_race) %>% #count the number of drivers of each race\n",
    "  mutate(\n",
    "    prop = n / sum(n) #compute the proportion of each race out of all drivers\n",
    "  )\n",
    "\n",
    "# probability of being searched\n",
    "summaryDfStop <-\n",
    "  stopData %>%\n",
    "  count(searched) %>% #count the number of searched vs. not searched\n",
    "  mutate(\n",
    "    prop = n / sum(n) # compute proportion of each outcome out all traffic stops\n",
    "  )\n",
    "\n",
    "# We can use a linear algebra trick known as the \"outer product\"\n",
    "# (via the `outer()` function) to compute this easily.\n",
    "# second, multiply outer product by n (all stops) to compute expected frequencies\n",
    "expected <- outer(summaryDfRace$prop, summaryDfStop$prop) * nrow(stopData)\n",
    "\n",
    "# create a data frame of expected frequencies for each race\n",
    "expectedDf <-\n",
    "  data.frame(expected, driverRace = c(\"Black\", \"White\")) %>%\n",
    "  rename(\n",
    "    NotSearched = X1,\n",
    "    Searched = X2\n",
    "  )\n",
    "\n",
    "# tidy the data frame\n",
    "expectedDfTidy <-\n",
    "  gather(expectedDf, searched, n, -driverRace) %>%\n",
    "  arrange(driverRace, searched)\n",
    "\n",
    "# third, add expected frequencies to the original summary table\n",
    "# and fourth, compute the standardized squared difference between\n",
    "# the observed and expected frequences\n",
    "\n",
    "summaryDf2way <-\n",
    "  summaryDf2way %>%\n",
    "  mutate(expected = expectedDfTidy$n)\n",
    "\n",
    "summaryDf2way <-\n",
    "  summaryDf2way %>%\n",
    "  mutate(stdSqDiff = (n - expected)**2 / expected)\n",
    "\n",
    "chisq <- sum(summaryDf2way$stdSqDiff)\n",
    "pval <- pchisq(chisq, df = 1, lower.tail = FALSE)\n",
    "\n",
    "#kable(summaryDf2way, digits=2,caption='Summary of the 2-way contingency table for police search data')\n",
    "\n",
    "# first need to rearrange the data into a 2x2 table\n",
    "summaryDf2wayTable <-\n",
    "  summaryDf2way %>%\n",
    "  dplyr::select(-expected, -stdSqDiff) %>%\n",
    "  spread(searched, n) %>%\n",
    "  dplyr::select(-driver_race)\n",
    "\n",
    "chisqTestResult <- chisq.test(summaryDf2wayTable, 1, correct = FALSE)\n",
    "chisqTestResult"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f82013d9",
   "metadata": {},
   "source": [
    "## Table 12.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5d437d26",
   "metadata": {
    "Rmd_chunk_options": "stdRes, echo=FALSE",
    "jupyter": {
     "output_hidden": false
    },
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 4 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>searched</th><th scope=col>driver_race</th><th scope=col>Standardized residuals</th></tr>\n",
       "\t<tr><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>FALSE</td><td>Black</td><td> -3.330746</td></tr>\n",
       "\t<tr><td> TRUE</td><td>Black</td><td> 26.576456</td></tr>\n",
       "\t<tr><td>FALSE</td><td>White</td><td>  1.309550</td></tr>\n",
       "\t<tr><td> TRUE</td><td>White</td><td>-10.449072</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 4 × 3\n",
       "\\begin{tabular}{lll}\n",
       " searched & driver\\_race & Standardized residuals\\\\\n",
       " <lgl> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t FALSE & Black &  -3.330746\\\\\n",
       "\t  TRUE & Black &  26.576456\\\\\n",
       "\t FALSE & White &   1.309550\\\\\n",
       "\t  TRUE & White & -10.449072\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 4 × 3\n",
       "\n",
       "| searched &lt;lgl&gt; | driver_race &lt;chr&gt; | Standardized residuals &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| FALSE | Black |  -3.330746 |\n",
       "|  TRUE | Black |  26.576456 |\n",
       "| FALSE | White |   1.309550 |\n",
       "|  TRUE | White | -10.449072 |\n",
       "\n"
      ],
      "text/plain": [
       "  searched driver_race Standardized residuals\n",
       "1 FALSE    Black        -3.330746            \n",
       "2  TRUE    Black        26.576456            \n",
       "3 FALSE    White         1.309550            \n",
       "4  TRUE    White       -10.449072            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute standardized residuals\n",
    "summaryDfResids <-\n",
    "  summaryDf2way %>%\n",
    "  mutate(`Standardized residuals` = (n - expected)/sqrt(expected)) %>%\n",
    "  dplyr::select(-n, -expected, -stdSqDiff)\n",
    "\n",
    "summaryDfResids\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ccc899a",
   "metadata": {},
   "source": [
    "## Bayes factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "346d1146",
   "metadata": {
    "Rmd_chunk_options": "echo=FALSE",
    "jupyter": {
     "output_hidden": false
    },
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bayes factor analysis\n",
       "--------------\n",
       "[1] Non-indep. (a=1) : 1.753219e+142 ±0%\n",
       "\n",
       "Against denominator:\n",
       "  Null, independence, a = 1 \n",
       "---\n",
       "Bayes factor type: BFcontingencyTable, independent multinomial\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute Bayes factor\n",
    "# using independent multinomial sampling plan in which row totals (driver race)\n",
    "# are fixed\n",
    "\n",
    "bf <-\n",
    "  contingencyTableBF(as.matrix(summaryDf2wayTable),\n",
    "  sampleType = \"indepMulti\",\n",
    "  fixedMargin = \"cols\"\n",
    ")\n",
    "bf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f5208ec",
   "metadata": {},
   "source": [
    "## Table 12.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75ff7d2d",
   "metadata": {
    "Rmd_chunk_options": "echo=FALSE",
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     "output_hidden": false
    },
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 3 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Depressed</th><th scope=col>NoSleepTrouble</th><th scope=col>YesSleepTrouble</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>None   </td><td>2614</td><td>676</td></tr>\n",
       "\t<tr><td>Several</td><td> 418</td><td>249</td></tr>\n",
       "\t<tr><td>Most   </td><td> 138</td><td>145</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3 × 3\n",
       "\\begin{tabular}{lll}\n",
       " Depressed & NoSleepTrouble & YesSleepTrouble\\\\\n",
       " <fct> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t None    & 2614 & 676\\\\\n",
       "\t Several &  418 & 249\\\\\n",
       "\t Most    &  138 & 145\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3 × 3\n",
       "\n",
       "| Depressed &lt;fct&gt; | NoSleepTrouble &lt;int&gt; | YesSleepTrouble &lt;int&gt; |\n",
       "|---|---|---|\n",
       "| None    | 2614 | 676 |\n",
       "| Several |  418 | 249 |\n",
       "| Most    |  138 | 145 |\n",
       "\n"
      ],
      "text/plain": [
       "  Depressed NoSleepTrouble YesSleepTrouble\n",
       "1 None      2614           676            \n",
       "2 Several    418           249            \n",
       "3 Most       138           145            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize depression as a function of sleep trouble\n",
    "depressedSleepTrouble <-\n",
    "  NHANES_adult %>%\n",
    "  drop_na(SleepTrouble, Depressed) %>%\n",
    "  count(SleepTrouble, Depressed) %>%\n",
    "  arrange(SleepTrouble, Depressed)\n",
    "\n",
    "depressedSleepTroubleTable <-\n",
    "  depressedSleepTrouble %>%\n",
    "  spread(SleepTrouble, n) %>%\n",
    "  rename(\n",
    "    NoSleepTrouble = No,\n",
    "    YesSleepTrouble = Yes\n",
    "  )\n",
    "\n",
    "depressedSleepTroubleTable\n",
    "\n",
    "#kable(depressedSleepTroubleTable, caption=\"Relationship between depression and sleep problems in the NHANES dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97c13a9d",
   "metadata": {},
   "source": [
    "## Chi-squared result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dbacd295",
   "metadata": {
    "Rmd_chunk_options": "echo=FALSE",
    "jupyter": {
     "output_hidden": false
    },
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's Chi-squared test\n",
       "\n",
       "data:  depressedSleepTroubleTable\n",
       "X-squared = 191.46, df = 2, p-value < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# need to remove the column with the label names\n",
    "depressedSleepTroubleTable <-\n",
    "  depressedSleepTroubleTable %>%\n",
    "  dplyr::select(-Depressed)\n",
    "\n",
    "depressedSleepChisq <- chisq.test(depressedSleepTroubleTable)\n",
    "depressedSleepChisq"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c13171a7",
   "metadata": {},
   "source": [
    "## Bayes factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1df4275a",
   "metadata": {
    "Rmd_chunk_options": "echo=FALSE",
    "jupyter": {
     "output_hidden": false
    },
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bayes factor analysis\n",
       "--------------\n",
       "[1] Non-indep. (a=1) : 1.796345e+35 ±0%\n",
       "\n",
       "Against denominator:\n",
       "  Null, independence, a = 1 \n",
       "---\n",
       "Bayes factor type: BFcontingencyTable, joint multinomial\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute bayes factor, using a joint multinomial sampling plan\n",
    "bf <-\n",
    "  contingencyTableBF(\n",
    "    as.matrix(depressedSleepTroubleTable),\n",
    "    sampleType = \"jointMulti\"\n",
    "  )\n",
    "bf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9f98742-e428-4418-a58d-499a0f672683",
   "metadata": {},
   "outputs": [],
   "source": []
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