genkuroki/Fisher検定とカイ二乗検定のR言語による比較.ipynb

## Fisher検定とカイ二乗検定のR言語による比較.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Fisher検定とカイ二乗検定のR言語による比較\n\n黒木玄\n\n2019-10-10\n\nSee also https://oku.edu.mie-u.ac.jp/~okumura/stat/fisher-chisq.html"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# 警告の表示を抑圧\noptions(warn=-1)\n\n# Fisher検定のmid-p版を使いたいのでexact2x2を使う\n# https://www.rdocumentation.org/packages/exact2x2/versions/1.6.3/topics/exact2x2\n# https://cran.r-project.org/web/packages/exact2x2/vignettes/midpAdjustment.pdf\nlibrary(exact2x2)",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Loading required package: exactci\nLoading required package: ssanv\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Fisher検定とカイ二乗検定の使用例"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "a = matrix(c(2, 16, 12, 20), 2, 2)",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fisher.test(a)",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "\n\tFisher's Exact Test for Count Data\n\ndata:  a\np-value = 0.0563\nalternative hypothesis: true odds ratio is not equal to 1\n95 percent confidence interval:\n 0.02044086 1.18096371\nsample estimates:\nodds ratio \n 0.2143069 \n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fisher.exact(a)",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "\n\tTwo-sided Fisher's Exact Test (usual method using minimum likelihood)\n\ndata:  a\np-value = 0.0563\nalternative hypothesis: true odds ratio is not equal to 1\n95 percent confidence interval:\n 0.0303 1.1031\nsample estimates:\nodds ratio \n 0.2143069 \n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fisher.exact(a, midp=TRUE)",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "\n\tCentral Fisher's Exact Test (mid-p version)\n\ndata:  a\np-value = 0.05118\nalternative hypothesis: true odds ratio is not equal to 1\n95 percent confidence interval:\n 0.02887862 1.00696660\nsample estimates:\nodds ratio \n 0.2143069 \n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "chisq.test(a, correct=FALSE)",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "\n\tPearson's Chi-squared test\n\ndata:  a\nX-squared = 3.9793, df = 1, p-value = 0.04606\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "chisq.test(a, correct=TRUE)",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "\n\tPearson's Chi-squared test with Yates' continuity correction\n\ndata:  a\nX-squared = 2.778, df = 1, p-value = 0.09557\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Fisher検定とカイ二乗検定のempirical cumulative distribution functionの比較"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### prob = c(0.04, 0.06, 0.36, 0.54), n = 50"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "prob = c(0.04, 0.06, 0.36, 0.54)\nn = 50\n\nniters = 100000\npfis = pfismidp = pchi = pchic = numeric(niters)\nfor (i in 1:niters) {\n    # 多項分布でサンプルを生成\n    a = matrix(rmultinom(1, size=n, prob=prob), 2, 2)\n    \n    # P値の計算\n    pfis[i]     = fisher.test(a)$p.value\n    pfismidp[i] = fisher.exact(a, midp=TRUE)$p.value\n    pchi[i]     = chisq.test(a, correct=FALSE)$p.value\n    pchic[i]    = chisq.test(a, correct=TRUE)$p.value\n}",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# プロットするxの最大値\naxmax = 0.1",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot(ecdf(pfis), asp=1,\n     xlab=\"x\", ylab=\"y = prob(pval <= x)\", \n     xlim=c(0, axmax), ylim=c(0, axmax),\n     verticals=TRUE, main=\"ECDF for fisher.test(a)\")\nabline(0,1)\naxis(side=1, at=seq(0, axmax, length=11) , tck=1, lty=\"dotted\")\naxis(side=2, at=seq(0, axmax, length=11) , tck=1, lty=\"dotted\")",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "Plot with title \"ECDF for fisher.test(a)\"",
            "image/png": 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iShDueRcGNfYEnBufbWE5wrTajsKxtA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\nAAAASUVORK5CYII="
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot(ecdf(pfismidp), asp=1,\n     xlab=\"x\", ylab=\"y = prob(pval <= x)\", \n     xlim=c(0, axmax), ylim=c(0, axmax),\n     verticals=TRUE, main=\"ECDF for fisher.exact(a, midp=TRUE)\")\nabline(0,1)\naxis(side=1, at=seq(0, axmax, length=11) , tck=1, lty=\"dotted\")\naxis(side=2, at=seq(0, axmax, length=11) , tck=1, lty=\"dotted\")",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "Plot with title \"ECDF for fisher.exact(a, midp=TRUE)\"",
            "image/png": 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63OR0jr9nrBecKXRNorBqldBEMT4pG9ekJkpCy125uXp4pOmyn/2gVC4nZpgjwm\nFRKTy2RzpNPKrHbHUkznw/a67fR0X6yogB1tVaonqasJmyN9P2/oXlL+pzEddacJfTU6KyGV\nl4bKy6wr1++283HBZJp1amdafxlchhoGyEhbajcZPNiXyKXpbER3SWDoPRhlLiSeB/tAF1XV\naW73MqlJ6mqChVQ9ITv23yUq8GBfIjTVpq6LRw1ChTThCVk8RpHGpRnYjujS35BESApTu+cT\nsttxOzP0giKqYNNchWQGX0Rz6W/Yvp3Bz6tCIT2fkLWOMC/HOhyvaEiXjq6qpBFSCgKF9DhT\nh1PGg30JUFaTsxXS8wlZh38iO+HBvulRBZtmmdq1D5Qca330TOdIl/XtCVmn+1XxYB+zy85x\ngmO9HT1DIUUchPWM6KJRVY33PmSe15FShzQFCClfNNVioxdpCvsJ6y1CHC6Q2tkOkhprn5DE\nxkplCiERGcZ12XuMzFhfE5uZCum7XItbE/9zF52juyj0VKHeJe8mVLcIuXyvnacL4IGiGsxA\nRZdgIe0mfIm+pwsu06xSu6EDBMY6qCOBsdKaNk2WE75E39MFl2lOQhrcLzFWCOky7RYhTxdg\nMqqqL4e87kI4Io3ftOrpAkxFWe1loSPMkRIaRnJp2ykt1uqAASFJjJXUVPaq3eyF5C0yf5ch\nhsPjkbxYiU1b15HWuI4kCTVVZxrfzaA7q6vI7s6GmSO65hqayeMqbINAIa0dHkPyAKmdp6H3\nuri/S2fDpmg6OuopXlK9RjHtXf6mBULyM/S+UOvv0t0QQrKYLI31SVdf1A/0aRBdbV3t5JTZ\nhQrpvF7Rfllx1wVwRXatQUg2k0hVgdTOw9CltGSxeshISr1GM4WQiAxpXToVxh5r76So81JG\nrGmvI0VB/0jPjsgqGxyA7hs5JHU1SYQ0Wnu51C4fMmvMksflI6EamjsbPqf9l7GYQppnauda\nEm+s7vMhKo+BlglTuwlfoj+hQiGkaYbOBaUXUlyPgZbphLSZcPf3vwWHkOaI1OoKE5IuAoVU\nTHlC9rcw7z9VCZgjUSK2tuYjI7pbhNzq6D9j/nM5GKndBMMppbDGGjgepa7X6Kavqd19RHKY\nJF35eTfFL4RE6VJu53zVz+TxaFZCumxvX6Lv/GDfyiz+IrWjQ3BVzSChe0J3Z4NrjX29jR84\ni6onQWxNzWJi1IBfSJfLH6R2VC6nlsAVK8UKw7xSuzhASG6GkwtIISQejySWWoT0b1VUVVt8\n/IvlYlZIraa5LHk3YRTS71ujdt+juJgXQmtpPteOmjAK6cMs/vuqtn7+LsxHDBchpupSEK+b\ndnw9TrCkEhJSuwEW5uux/WUWMVyEmGprcC9jhljbOtJWr4ilrbAAABeJSURBVHym3qswvbdE\nkLqYEWKraI553SXDEWkmyK2hWcqIe470t7pnFXOkYJeSr820hKSqXr1dBgupfm7YzfC9UcVv\nv51iWymB50+ILfdPprEGt6HGejWMQrr8+6iuIy2KFa4jhSC6emaY1pWwCsnPBWghu3bmOEG6\nZCgkb1N+Q09LIzrWzkqD4FgJXUJICQ39LwUJjhVC8jPxFZLdZmZpwRSkV81MMzsISRnyawZC\n8jJBaseagphAl9Fj7Q5IcmOldAkhJTScbil+3tGT2ImNldRlfkLKGfnVMsu7gypChTQJPNgX\nhPxamedtdhWMQuJ5sC/f1E5ButQnJKmx0rpkvmkVD/b5W5qB7Xgep1v2DUhSY6V1ySkkPEYR\ngo4qmW1mxymkl9rFdaRpKKmRucoowxEpz9TOWF/G8Ohn2SMjsbGSumSeI+HBPj/L9oFCY+0d\nj4TGSuwyVEjL7cnd0P5gX2BUOaOgOhpNqyDaCAQK6VptE7SEB/u8kF8b5pXU4aQgUEjn789p\nWprugstUbArSc5SsWE0bGpezSu1KDtsltZYgJPtBkmLtyAhC8jc5linb+H+RDXExV8RXxZiQ\nZgKJkPb1/zZ3/mdjHi7mivia6NGR+JhjEC6k8/Y6HC3356ua3P795XQXLKYiU5CBIwTFOqoj\nQbFGdBkspEO52LCp/5Es3UcRhGQ/QFCsEJK36ct1pOtgtDvfdxT+cQy7mDEaquFVQTPN6y7h\n15HWe7JQBlzMFxW1MO+Z0ZPQ60hkgQy64DIVl4LYrtzE8TjV0mmFQUiskV1y3mvH4yIbIdl2\nJov1qRnnpTpp9RrHZX5CygVhVXCfAQ2SOsDUQEgykVUDNgVBSBXZCSmP1G6kTO5YQ2Qkql6j\nuYSQEhpa7l+L5XKaYa2QwNFIUr3Gc5mfkHJAxuk75HPI6h5ASPIQcvYQ0hSyE5L+1M6hPI5Y\nqWQkpl6juoSQEhr2W7oUl0pIretI1C6JDCEkThdSEXPqyOEmASHJQs6ZQ0iTSCCkXWGWI0/T\nzja1cywreqyEawoi6jW6S1YhHdem2F22VavYn6adq5Cc+zmZx/6jekSkul7ju+QU0rFqko35\nPF9Oa/s3PMw0iZBy2kjopsMopE+zuVw29dN/Z7OM4UI3Us4aMyMPGIV0WzZdN15QuwgxTZ6C\nTFmapvE4dFCfjvTWK4tLfiF91zmd/bH0OQppSjERYx1aY1BbrzwueVO7z/sDtecqzaN3oRgZ\np4wVb18YhXQunh9vI9+TMr/2E3LG0JEvrNeRNnf5FNbxaIap3cQyYsVqkZHOemVzmd+dDSob\nfGoREJI0l/kJSSNiThd5nTesQjps11ULrTeHWC40IuhsISRfOBcblo1Gwi1CD0vOz7Ixw2EZ\n6atXVpecrbgxxXf9JeGnfRFt+Vtdg/uYx4t1cDRSV6+8LjmFVJjjY/sY7YKsNkSdKnI6b9jv\nbOh7QeZCH7LOFELyJrsRSVcK4mkbKVbbMoOuemV3yTxH2tf/aBZzpJul9+Du7dG+F0Li9Ont\nbdVYE1pa/49F/UXT2f8YATE0fl6XvvEzrS0ZhXQ5bKrrSMV6i+tIF2lniQtIQbAKicOFtym7\nofcMKU6sI0ISFas8lxBSMkMjK9axexokxSrQZX5C0oK0U8TNQWGkEpK9sfJvSWln2F5qABPJ\nTkg6UhDD79JuOKojQbFKdJlfaqeiwQ2/S7vh+IAkJ1aRLvMTkgaknR7yunAgJH7EnR1UFA7v\nBVmGB/vkpyCmZyuyS7uhk4yExCrVJaeQ8GBf+3AhsToNR0JileqS+aZVhgf7pCPw1JDXEcAo\nJDzYdxF5ZpggUcAopJeGmul1JGN5FcnlqKGbjmTEKtZlfiOS6AY31pdRXI4aOg5IImKV65J5\njsTwYJ9kRJ4WEjsSOJe/JzzY5+tCNDLPCkIigfc6EsODfXJTkO5xaWO9qcdRSHLrVYTL/O5s\nENvgfdOTyC77DE1bPxAShcv8hCSV9KfUlc4UIQE7EBIPyc9oTEbJA1ROdkKSmYL0H8MZa6iQ\nZNarGJcQEovhwCGMsQYPSCLrVY7L/IQkEQGng8wuMhBSfNKfDWQUneyEJC8FGd4fd1pmbl/l\n2i+j5nUkKpdCDJHaUbgQ1+CW3RFjbUqGZAQSV6+yXOYnJGkkOhXkcMxASHERpqN8KlYa2QlJ\nVgpiLzRerPRCklWv4lxCSFENk92/BiExu8xPSJJIdxov2kFmxwCEFI+EZ9EahCCj6GQnJDkp\niMMaNbXLx/6hMUhTuqQpVggpnqH/LXjhhoOpnKbOqSnWDIUkhaSngDkROxBSHBKfAXTETXZC\nkpGCuBUWffmbzqOQehXrklVIv3+Mef97K8RainYhOZaFOVIUw9yF9Luo2rb+Zsis//Vl8vAx\nR2KHUUgfZn1V03rxXhWSsZCSR48LsPwwCmlRG/4s3n6yTu3cC4oV67CONKVLmmJlFdK9XX/f\n33MW0oRyiGM1ne+ro/OYvl5lu+QU0pv5vW+955vaJQu95w5VxdWoDkYhrc2f29aPec9VSOki\nh5CSwrn8/fFo2b8jjaw2tZtWCOmKu5OQNKVLmmJlviD79finSD9/shTSxDIgpCiG+QtJkosY\niJofIbNjBUKiI1XUAzKCkDhhFdK/VVE1cPHxL5aLhCnI9AKIYp0gI03pkqZYeW8Remu08nsU\nFyGmoYacH0kvhl0FDQ9GmjqnpliZbxFa/PdVbf38XZiPGC4SImbdO1kc84ZRSAvz9dj+MosY\nLtKRMGLoSAIJbhHqviBzEWIaZOhnTBDrxAUGTemSplgzHJGSNLinbfj6xtR1Ok2dU1Os3HOk\nvz/VVm5zJCnXj1KFAXjXmt4bbf72297b7hN6fkwq3x0d4SfZj2EU0uXfR3UdaVGscryOxO9y\n+nCULlZGw9xTOx4X7JXo2IUpXUJI4lzmJyRuEoaK6ZEgIKQw0kWKZQZRpBKSve3VpHYmxGNI\nrJ4y0pQuaYoVQgozNEEeA2L1XfbW1Dk1xYrULgjmMOuFdnNpC4k3CtALhOQPb5RmANYgwBDZ\nCYlvWDetv5FdkuhIU7qkKVZmIWX1YJ/pbER1STMgaeqcmmJlFRLPg31c8M+PkNdJhlFIWT3Y\nxx0hdCQcRiHl9BiFGdiO5bKln/o6UlSPZJZI7ai9vbS87utIZvBFHJftgWgWnVNTrBmOSByk\nnR/xOgdu8M6R8niwL+H8iNkzcIZz+dv+YB+JC45hvX1g7BSkZzSaRbqkKVbu60g5PNjXOS5e\ng98mRN3xaBadU1OsGd7ZEBue0LDarQ0IaRoskfXLSG6lgAyFFHdY7zuIPgUZFdIs0iVNsUJI\n0wx7jyFv8PHxaBadU1OsGQopJqnyOg63IAwIyZ1U6wwsbkEY2Qkp3rA+OKPx9dg/43IR0izS\nJU2xQkjuhoP7IwuJ1KWmzqkp1gyFFAumkJDTKQVCciOBjng8AhqyE1KcYd22ky4FcV5jmEW6\npClWCMnN0Ftk01w653Wz6JyaYs1QSDHgOGPj/e2pQAIQ0jjxo8FFWPVkJyT6Yd17Xdzd5VQZ\nzSJd0hQrhDRu6H2h1tnl9OFoFp1TU6wZCokanvkR0jrtQEh2UuhIztkDZ7ITEu2w7lJaYAri\no6NZpEuaYoWQ7IZOhQU1eM96HYQUaAghcbpwgTevuyCvUwyENEz0KDAzyofshEQ3rLuW5Osx\nQEazSJc0xQohDRs6F+TnMWihbhadU1OsrEKa0HUEpDmxQ0BalxeMQtppEhKvjtKfLwiEM7U7\nFqvYLqiG9SmleHkM09Es0iVNsTLPkY5mE9sFTSVOKmS6DIIfmZhF59QUK/diw84cY7ugIKZ7\n00NEd4CJ7FbtZHq/DUKQUbZkJ6TwYX1qCePH98knSEWzSJc0xQohdQ0jzPwhJFbD/IV02K6r\n3rPeHGK5CIbetUVGSOuygVFI52WjB9kXwtN1sDjzIwgpexiFtDHFd71od9oX9oXwZKmdj/WI\nTQwdzSJd0hQrq5CKxtr30RQxXISYGl9jHyHd1vF83Dm4jGCIWOlN/e+1G3pB5iKQGG7bCkI+\nlyfZjUgBxJ8f0TsAQuCdI+1P1ZbMOZL3SGsr0yoipEtRDHNP7S6rRrdanq0ujPePCbCl/umZ\nGImNVVO9SozVMArpcthU15GK9VbedSR6l0jr5gSrkCS5iOyRbqkbqCA7IfmZmgCfPYa9C95O\nlt4uIxsiVnrTDIVkQnxCSMIMZyUke6rDnAchrwOhQEhk3m5rRRDRLEFqd7cIywd6BDSuI6RL\nUQxnldpFczHZ1LT++ngcllGcgXcWnVNTrBkKKYkrLxmBnOC9ICvvwb6oOqIoG+iAUUg8D/ZN\nMzW9mxM9essI6VIUw9xTO4EP9pmB7WkevYcjdM4ohrkLSd5jFBRukNaBEkYhvXQuCdeRIujo\nAhnNlOxGJHdTY33pVkTYMIR0KYph7qmdsAf72gcSCGmyuYfLRIaIld7U29uEB/t8XbgT7gLz\nIvCE9zqSnAf7gj1ARqBJdnc2uJn2HDXxAhSFipAuRTHMPrVjceFk2neQ++SKbLUbnTOKIYTE\n5SKkvvqhiw3oZI5CCviog4xAP9kJadx04IhRwwEVBQhJkSFipTdVLqShA0YMh2WkqcERaySX\n+QkpTtH0IgJ5MTchUdwIBB2BDtkJyW5q2TuY8o3JSFMKglgjuZyZkGw7h9YgRgcjTQ2OWCO5\nzE9IxMUinwNOzElIU0vFtAg4k52Qhk3H1rdbL51lpCkFQayRXM5ISGNlvuyfskinqcERaySX\n+QmJoMjx9QUAWsxFSO4lQkbAg+yE5J1+1Yd4iEhTCoJYI7mciZBcyiuP8RqMNDU4Yo3kMj8h\n+ReHnA74MwchOZUGFYEQWIX0b1VUnbP4+BfLRY+pU14XoCJNKQhijeSSU0i/b40u+h7FRZ+p\nyzpD0GCkqcERaySXnEL6MIv/vqqtn78L8xHDhU9RyOkAAYxCWpivx/aXWcRwMbUkiAgQwSgk\nni/RN5ZXrXCIZKQpBUGskVzmNyKZwRevh9ENRpoaHLFGcsk8R/r7U21xzZEGiyGUEQAlnJ/A\n740e+/YbxYVDKcMpHWQEvOG9jvRRXUdaFCuO60jtQszz/4CRykhTCoJYI7nM784G09m4vRyF\nP1h0ziiGEBKli9bqXSwVAVCR3S1C3RKgIRCfTG8RuhUwns8NT6kmemS1RKzSXOZ3i5CpfzlI\nqDUYzaLBEWsklxlekHXH3wkALTK7RQgiAmnIbEQKENEsUhDEGsllZrcIhYxDs2hwxBrJpaBb\nhF67/P/+5/nXYl/6wF/8jfOXUUjxbxHyHY0ACIRzRGJwESCiWaQgiDWSy8yEFDIQzaLBEWsk\nl3kJqTRDLgdSkEpIMVbQIB2QjIyEZPxN0xgi1jiGSO2CXBh/00SGiDWOIYQU4gJ5HUiJzC47\n3QV0BJLCKqR4D/aZni3vIrgMEWscw9xTu4gP9pneTe8ymAwRaxzD3IUU78E+5HUgNYxCivYY\nBXQEksMopFgP9hnLK+9iOAwRaxzD3FO7SCOSsb70LofBELHGMcxdSHEe7ENeByTAufwd47u/\noSMgAt7rSOQP9nUPRAoSxRCx0psKurOh5zg0eBRDxEpvKkdIyOuAFDQLCToCYlAspP6DkIJE\nMUSs9KYcQgJAGR69nF44hPBHl6A+ZuEy+5OEkFJ7nIfL7E8SQkrtcR4usz9JCCm1x3m4zP4k\nIaTUHufhMvuThJBSe5yHy+xPEkJK7XEeLrM/SQgptcd5uMz+JCGk1B7n4TL7k4SQUnuch8vs\nTxJCSu1xHi6zP0kIKbXHebjM/iRlCwkAJUBIABAAIQFAAIQEAAEQEgAEQEgAEAAhAUAAhAQA\nARASAARASAAQACEBQACEBAABEBIABEBIABAAIQFAAIQEAAGihPSxMIuP3/43OvuabApTbM5D\nb+yGT9Lmcf1m8Whzef405vPo4/LKv8FgLR5Hvvrd5vLrjzF/fia7HPm6eYvHX2tLWpvyurna\nDxn2FLs2w/uokSSk+t/SvvW+0dnXZFXtXA68cRzuYTaPH9XmYqD6bS6rfwlqBpRkc3nldzEU\nrMXj0S4km8u/trO0uLzrqJjq8WdRexzQrq1e682t40lePyPuNWLtPEQIEtI/s/i6fC3Mv543\nOvuaHExxvBwLc+h94/p36CRtHr/Mn9/yM+1Pr6XN5cZ8lr/Wk12WDAZr83gccObgcnHd/C36\n/y29vV5L9s9NV49/Kl8fHvW6M6tzOdr3fkL1dJDrKzO0jx5BQvowf6+//zOrnjc6+5psTDne\nfz8/q5pvXKt/UEg2j0VtNGBrc1mY87ChzWX1dyhYm8fd0Mf0qMv/qm79axZTXVaci34B2zwa\n73pdVfI6mY2Ty+tn4PvNibXzUCFISIUph/uvZ7bQeKOzr8nanC4vH8vNN671Pigkm8cbA7ZW\nl7Vhf9Jjd/nzaPxJHndmN3CGYy7/mK9hw9GTXJtzr6HN4y117Zeu1eVdgr166Gm5j7uFtfNQ\nIUhInc+qxhvWzzGb4eU4KIYRw4pf8+5nuRno23bDd/MzFKzNcG32n9cZeb+h1fLNXFaLKomd\nHGvZvQdc2gxXt9Suf3ig6wOXr/abPv+Hz53shTRo5WBYdtG/PpbfxqePXTvZf4PB2oVUMZC6\n2Ou1WhnpHx7GqmdoQLIbrsvVhsXApM5muawGp4OrkDpvQkjphPSzGEgHRix362Jg2mIzrJIP\nHyEZ832dsPgMgqach//+6dfgyEkey0WVyR5va28eot+a9flyHJjxQkhP5Anpd9Gf2LmMZZ/9\n3dpm+FauQvsIqebcXDV2tDTVHOmnf2l4xGW9DjDVcF2mdlfp9g9JVpfV4LmGkMZYtM+38UZn\nX5POAlvrjcEatHkseR+89DDmsuzWvYOZxeWfKo0cCnbc45Cp7SytfWzEZTHYd2we30w5Ifsd\nuKpjdXm+TgS3A7H2dpDbK2vnoUKQkOrFlZ/2Ws/Pc9Xux7Zqd2ov9TzeGKxBm8frn7f3oSv+\noy4HvVpc2v85vbfHkXq1WNpdWi5e2Tzah4fxszz2j7u9HeSheEvnoUKQkFbVJ/Lf58XBxhud\nfU22VY6xf87vW28MCsnm8fp7KK8bcVlfRzr1N7jFpV1ILh77e/Z4vf70n6q9Xi1r7jaP9fAw\ncOXK4Sx3/WfZ20FuNWntPFQIElKkOxssQrJ5HOhcDi6rOxvO6/5+NnZnw2Cwdo+barGhf85i\nP8u333LG8t9Ul+VYMXg7oc3jhylvevsY6NWj9XpYlgsrDi5LbnU5tzsbrulzSdV/6ypovNHY\n7LJ8LgTVho03Lrbk2OLxj214sLssbOtStpO0BmvxeK49Dl1Isrlcedfrcmjxe8Tju82jw1kO\nJJQdl40Na+chQpKQ6vuCq826ChpvNDa7nKs7hBuGjTcuNiFZPFrzrBGX183lUN5jO0lrsDaP\nZ5tHu8u/7571apu7Wz3aWtLq8vR5ldHQSmHHZWPD2nmIkCQkANQCIQFAAIQEAAEQEgAEQEgA\nEAAhAUAAhAQAARASAARASAAQACEBQACEBAABEBIABEBIABAAIQFAAIQEAAEQEgAEQEgAEAAh\nAUAAhAQAARASAARASAAQACEBQACEBAABEBIABEBIABAAIQFAAIQEAAEQEgAEQEgAEAAhAUAA\nhAQAARASAARASAAQACEBQACEBAABEBIABEBIABAAIalkZQ7X3wfzmToQcANCUsnJFNffRXFO\nHQi4ASHpZGe2l635Th0GuAMhKWVldmadOgjwAEJSyskYc0odBHgAIWllYzapQwBPICSlYESS\nBYSklPV1jrRKHQR4ACHp5Pua2G3NLnUY4A6EpJJzUV1HQnInBghJJZ+3OxuQ3EkBQgKAAAgJ\nAAIgJAAIgJAAIABCAoAACAkAAiAkAAiAkAAgAEICgAAICQACICQACICQACAAQgKAAAgJAAIg\nJAAIgJAAIABCAoAACAkAAiAkAAiAkAAgAEICgAAICQACICQACICQACAAQgKAAAgJAAIgJAAI\ngJAAIABCAoAACAkAAiAkAAj4Px5oTSpICH/vAAAAAElFTkSuQmCC"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot(ecdf(pchi), asp=1,\n     xlab=\"x\", ylab=\"y = prob(pval <= x)\", \n     xlim=c(0, axmax), ylim=c(0, axmax),\n     verticals=TRUE, main=\"ECDF for chisq.test(a, correct=FALSE)\")\nabline(0,1)\naxis(side=1, at=seq(0, axmax, length=11) , tck=1, lty=\"dotted\")\naxis(side=2, at=seq(0, axmax, length=11) , tck=1, lty=\"dotted\")",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "Plot with title \"ECDF for chisq.test(a, correct=FALSE)\"",
            "image/png": 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B5Je4Q+EpmLKFOQaFzmFKtMR8M1d+Qu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\nQEgAEAAhAUDA/wG2rzwuTgm7XAAAAABJRU5ErkJggg=="
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot(ecdf(pchic), asp=1,\n     xlab=\"x\", ylab=\"y = prob(pval <= x)\", \n     xlim=c(0, axmax), ylim=c(0, axmax),\n     verticals=TRUE, main=\"ECDF for chisq.test(a, correct=TRUE)\")\nabline(0,1)\naxis(side=1, at=seq(0, axmax, length=11) , tck=1, lty=\"dotted\")\naxis(side=2, at=seq(0, axmax, length=11) , tck=1, lty=\"dotted\")",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "Plot with title \"ECDF for chisq.test(a, correct=TRUE)\"",
            "image/png": 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D0S0In0KRHhGOnl97pMLwG0In5GqDtr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\nf2wSAnteiYL8AAAAAElFTkSuQmCC"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "plot(ecdf(pfis), asp=1,\n     xlab=\"x\", ylab=\"y = prob(pval <= x)\", \n     xlim=c(0,1), ylim=c(0,1),\n     verticals=TRUE, main=\"ECDF for chisq.test(correct=FALSE)\")\nabline(0,1)\naxis(side=1, at=seq(0, 1, length=11) , tck=1, lty=\"dotted\")\naxis(side=2, at=seq(0, 1, length=11) , tck=1, lty=\"dotted\")"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "ecdf(pfis)(0.05)",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "0.02006",
            "text/markdown": "0.02006",
            "text/latex": "0.02006",
            "text/plain": "[1] 0.02006"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "ecdf(pfismidp)(0.05)",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "0.02582",
            "text/markdown": "0.02582",
            "text/latex": "0.02582",
            "text/plain": "[1] 0.02582"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "ecdf(pchi)(0.05)",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "0.0409845127184651",
            "text/markdown": "0.0409845127184651",
            "text/latex": "0.0409845127184651",
            "text/plain": "[1] 0.04098451"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "ecdf(pchic)(0.05)",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "0.00930643913126501",
            "text/markdown": "0.00930643913126501",
            "text/latex": "0.00930643913126501",
            "text/plain": "[1] 0.009306439"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### prob = c(0.04, 0.16, 0.16, 0.64), n = 25"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "prob = c(0.04, 0.16, 0.16, 0.64)\nn = 25\n\nniters = 100000\npfis = pfismidp = pchi = pchic = numeric(niters)\nfor (i in 1:niters) {\n    # 多項分布でサンプルを生成\n    # 多項分布でサンプルを生成\n    a = matrix(rmultinom(1, size=n, prob=prob), 2, 2)\n    \n    # P値の計算\n    pfis[i]     = fisher.test(a)$p.value\n    pfismidp[i] = fisher.exact(a, midp=TRUE)$p.value\n    pchi[i]     = chisq.test(a, correct=FALSE)$p.value\n    pchic[i]    = chisq.test(a, correct=TRUE)$p.value\n}",
      "execution_count": 18,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# プロットするxの最大値\naxmax = 0.1",
      "execution_count": 19,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot(ecdf(pfis), asp=1,\n     xlab=\"x\", ylab=\"y = prob(pval <= x)\", \n     xlim=c(0, axmax), ylim=c(0, axmax),\n     verticals=TRUE, main=\"ECDF for fisher.test(a)\")\nabline(0,1)\naxis(side=1, at=seq(0, axmax, length=11) , tck=1, lty=\"dotted\")\naxis(side=2, at=seq(0, axmax, length=11) , tck=1, lty=\"dotted\")",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "Plot with title \"ECDF for fisher.test(a)\"",
            "image/png": 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IEMkv0FJedKEupxHgk39kWWFJzrYDnB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\nDkzi6SKUS0ECUdpRSEyG6haYDhwYxXd3hMEZJBBG4pCgqeumIUw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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot(ecdf(pfismidp), asp=1,\n     xlab=\"x\", ylab=\"y = prob(pval <= x)\", \n     xlim=c(0, axmax), ylim=c(0, axmax),\n     verticals=TRUE, main=\"ECDF for fisher.exact(a, midp=TRUE)\")\nabline(0,1)\naxis(side=1, at=seq(0, axmax, length=11) , tck=1, lty=\"dotted\")\naxis(side=2, at=seq(0, axmax, length=11) , tck=1, lty=\"dotted\")",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "Plot with title \"ECDF for fisher.exact(a, midp=TRUE)\"",
            "image/png": 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d2CUUgfqhiHPlbq31/+V23TuwApI7pX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\nAABJRU5ErkJggg=="
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot(ecdf(pchi), asp=1,\n     xlab=\"x\", ylab=\"y = prob(pval <= x)\", \n     xlim=c(0, axmax), ylim=c(0, axmax),\n     verticals=TRUE, main=\"ECDF for chisq.test(a, correct=FALSE)\")\nabline(0,1)\naxis(side=1, at=seq(0, axmax, length=11) , tck=1, lty=\"dotted\")\naxis(side=2, at=seq(0, axmax, length=11) , tck=1, lty=\"dotted\")",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "Plot with title \"ECDF for chisq.test(a, correct=FALSE)\"",
            "image/png": 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Bcb9s0c6VSoPI1r01m1W8svESL82PkhE1vq/zUtL6kfMhPSsn/tcto155GKco/z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\nAABJRU5ErkJggg=="
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot(ecdf(pchic), asp=1,\n     xlab=\"x\", ylab=\"y = prob(pval <= x)\", \n     xlim=c(0, axmax), ylim=c(0, axmax),\n     verticals=TRUE, main=\"ECDF for chisq.test(a, correct=TRUE)\")\nabline(0,1)\naxis(side=1, at=seq(0, axmax, length=11) , tck=1, lty=\"dotted\")\naxis(side=2, at=seq(0, axmax, length=11) , tck=1, lty=\"dotted\")",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "Plot with title \"ECDF for chisq.test(a, correct=TRUE)\"",
            "image/png": 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wTyRPqUiHCM9PZ3XaaXALkifkZkd9Yu\negkiWjLVXOc1knlph+tI8SUTzXVmG7kbSZQESIcUpgOMBKSTxGxgNdLnqmoOkKq3z1ASKJfC\nBEbMdX4DmZd2fy+db+zDyYYokgnmyvmnPg0jvanFf1/N0u/HAqe/gRWpTAXWC7Jf9+UvXJAF\nNiQzE9hvEdK9IJPwCS2pXEpCUjkHZ17a8eyRMDmDBMbJ1TE2cyPVx0gfv80SjpGADSlNA85z\nIq+ds3Yvf0/N9miLP/yU/KME5GD9oxiNdP58a64jLaoVriPFkUSugSTzu7MBAx4kkF9SJZSr\nW6hsI4E8SG0KwEhAIsnNgFhGMu+4UdpJC2SWVD6KRZV2MFIUyURyVV6KRRkpugSQS4rDDyMB\naSQ5+tkZCeVSkEBGSTX4n0HSO5DZSHiwL7ZkArmqp4Xgkv6BrEbiebAPpE2qQ89oJDzYByZJ\nduQZjYTHKOJLSs9VjSwHlCQJZDVS79IRriNFkRSeqxp9EUySJjDDPRJIl5SHnfcYCQ/2gXGS\nHnXO09/mB/tIJFAuBQpkkByuKDlXklCP60h4sC+ypOBcn9YTnCtNqOw7G0CapD7kMBKQQPIj\nnp2RUC4FCQwsqVtJaq5koTASUSByNa4jNFe6UNlGAumRw3DDSCA2WYx2dkZCuRQkMKDk2AoS\ncyUNhZGIApGr8X2BudKGyjYSSItchhpGAjHJZqSzMxLKpSCBgSRNb0rLlTwURiIKRK7OJnOX\nDBOYoZFAOuQ0zDASiEVWo5ydkYovl8IEBpB0Pi/uLhksEEaKGVh2rs4Xat0lwwVmaCSQBrkN\nMYwEYpDdCGdnpKLLpXCBxJI2rUnJNVgojEQUWG6uVo0JyTVcqGwjAfnkOLyMRlLVPrQESIEs\nR5fTSEqtT2ElfEJTL5fCBhJK2rYkIdegoe5G+qnUxspKMJK0QDpJ64YE5Bo21N1I59Naqfef\ncBJAPLkOLa+RzufD+lLh7Q7mHVOuvQ2yHVluI9VW2rTffxlCwic05XIpfCCR5JxWYucaPNTT\nSDWH3XoJIxWY66xGUurXSEYKJgFEk/OwwkiAi6xHNbs7G4orl3gCCSTntpBSv8JIMQPLynV2\nAyn1K7OR9tt1c8JuvZm4WSjrIqBMch9SRiOdlp2vvlwFkQBSyX5EGY20UdX3oVk6Xu4VCiHh\nE5piucQX6CnpEp1Sv7IaqVKH+/JBVSEkfEKTm5ysgX6STsEp9Wu809/B7mwA8ihhOLPbIwFx\nFDGavMdIP8dmCcdIsSRj5Opcwbgq5l7anVeds3ZL4+3fl853/FEesdw/yDWfXBWjkc77TXMd\nqVpvcR2pFEoZSlYjSZIALBQzktkZyTmUP7CAXFVCuXpJwkgRA/PPVflIptSv8YykjK0UUxDk\nTUnDCCOBUBQ1iijt4gVmnqvylEypX2GkmIF556oG/4dX9I+EkYA0ShtC3guyeLCvFIobQUYj\n8TzYl3W5FC9wZqTSLoZUJIlMo7TDg33xJZlyVSPL4RRpItMwEh6jKIUSh4/RSL1LR7iOlC9F\njl52e6Rsy6W4gTMilfFlCEWyyDRKOzzYF1+SIdfhipJzpZNkPf0948E+VwkQm1KHjvc6Eh7s\ny51iRy67OxuyLJfiB1pGatYSmyupJIwUMTC/XHUrSc2VVjI/I4F4lDxsMBKgouhRy85I2ZVL\nMgItIkfWEJkruSSMFDEwr1zHVpCYK71kfkYCcSh9yGAkQEHxI5adkbIql+QETkQa3hWXaxBJ\nGCliYD65mt6UlmsYyfyMBPjBcPkbab+53Iq6mvoQBh8JIByM1tnXSN+Pj2FY/tAlhdJOXKAh\ncqJNUbkGk/Qy0nGlVrvD5XmI035bLx/d0/DPyju0iAEPkOtUk5JyDSfpY6Qftek+VHTcKLKd\nEoqFZMBQtXgYaT18Nu/07pvNUAJIByN1JbuzdlmUS/ICRyIt2hOTa1BJGCliYPq52jQnJdew\nkr5Ger+Vd0fzR6d6SACxYJgeeBpJVd/N/zvz59T5SACpYJQ6eBppX6n18XIevLK4Ivv3tqj/\nXb0o9fofeVbeoUWUIJS5WrYlItfgkv7HSFulNkptLeJ+F/Vu62/RXr99pc7KO7SIASfM1bYp\nCbmGlyQ42VBXdWpnE/dPVX/1P/9+a0/9U2/EWQFeMER9iPZIxs9NvcWpv+s/dZWnFsRZAVYw\nQgP8j5FW9THS2uYYqTkfsVCdF5RZeYcWUYJQ5Tqjnei5skh6n7W7VnXf1XRD/9TX5WOLvy7L\nf+aDJBhJWmA/ck4zsXPlkfQ10v1GVYvbg77U4u3rXC1qJ328qA/irAAfGJ5nOO9s+FgwfPUl\nCA5GRwPvLUL//XtpPkV/9RtMItlySXZgJ3JmG0X0K+61ixmYZq5zmyiiXzM0EggLhkYPq5E+\nV1VzgFS9fYaSAGHByIzAaKS/l8439uEWoSiSvrly/t1NqV9Ze+ZNLf5rLiKdfz8WwW4RSm9y\nJhHYRrqEF9Gv/ka63KFg+QjFor0W2/CFW4QSBMMyDqOReqsFu0UIBAOjYiC7PVJy5VIagXWk\nY2wR/cpqpPoY6aO9EItjpFiSHrm6hhbRr6xGOr92ztq9/D012+PRMn5E/GBMJvqH0Ujnz7fm\nOtKiWuE6UmJgRCZgNZKbBFMof2BCuTofIRXSrzBSzMB0clUJ5RpHMj8jAXowHNPEMpI5BiMn\nCYyGBb5GctYNZSSUINSBil/SLzDJ0i4MMJKcQMUv6RmYoJFWqvmasYkHx70kQFwwFHb4GelH\nXT76+5vuG8aeJUBUMBKWeJZ21WVftKosIzke7EMJQhmoNEuBJf0DEyztzpu6tjtZfc4qHuyT\nIDkzUGkXg0oSBKZopGNd1f0ou29h5nmwD9CBYbDH96zd6v38bnmqAQ/2JQZGYQa+RvpRJ9tT\nDb1LR7iOFEVyTqAyvAokSROYYml3Pldr21MNeLAvvuSMQGV8GUSSKDBNI20sTzVwPdgHaMAQ\nzMPbSEfLUw3nqQf7PLMCpGAEZsJ6ixDHg30oQSgCn9eTm6sISdxrFzFQbq6a1cTmKkMyPyMB\nf9D984GRwBD0vgPZGQkliG+gfh2ZuYqRhJEiBsrMdWQVkbnKkczPSMAPdL0bMBLogp53xMNI\n/Y9GJR0BlHaRAsffl5erKEkYKWKgvFwNb4vLVZYkSjvwAN3uDowEbqDXPaAy0n7tm8mkRODQ\nIkoQU6C5UVm5ipP0NtIGx0gpDbj7QZCoXOVJ+hrp4SPST+RCkcEOutwPTyNV6vu8UsfjSu3J\nUjpjVPlBj3viaaRLRbet90YH2g9bRWnHHDjdoJxcRUpSGOlH7c7EX+0CI/EGut+CJzEwRSOt\n69LuqJbnvRgjgfmgu/3xNNLPxUCry8mGd7KUzhhZXtDbBPie/t5eXr0r648ScpBgCi2iBNEF\n2jUmI1exkhHubNgtlVpPnCyHkfgCLdsSkatcSV8jnebENYGr9rKTeQ+GYoMNdDUNvmftVvYX\nYhsjbdSmNt9xcznTR5oVcAI9TYSnkZaXnYvltdjGSFXzHX/nk1oSZ+UdWkQJMgy0byh+rqIl\nvY+RjtvaS8utTYnXGOl2mhwfoh9FUhlfckgyBCZppPOlTquUTYmn2hN81xfGj95HwcECupkO\nmrN2O5u7v5Vab3ftt86eJj56HyPMAXqZEIo9UlPdfU/HPZ63UKoyFoMo7RgC5zWCfiUPfT5G\nqjZW30hxOOx263VzymFjPqiCkcIHzmwD/UoeOjxr9076BMWTBAgCupgW7+tI0yWdAxjl0KCH\niWG8s+F83m/XzVHSeurSE0q7wIHzG0C/kof2Q74v3ni3ub/htOx8Cp75OUAYKWwgzblaqYFJ\nGul665yy+BChjaq+D83S8afC6e+IoHvp8TRSbY7Lzqg2hvHeuYZKHe7LB1yQjQd6NwCeRrqZ\n42C+d66NU2MvKLLyDi2iBFHOwehX8lCtHyzubODZI2HAJwIdY9Gv5KH90u62R5o+SLqUge2F\nWxwjxQNdGwbfkw3b5hhpX9l8Gteqc9Zuab5FSOEnzI8SkEOWP8rPSPO+2mW/aa4jVestriPF\nkUSugSR5jRQyK+/QEgbcY4TQr+ShZHaJKlEi6NZwwEjlgF4NiIeR1sPzBacZHxJpLjJQ2gUI\nVD6K6Ff60FvIj+o9VXTczPlqFxiJW1J5KaJf6UPvIceVWu0OFzOd9tt62erpvlBZATPo0rD4\nHSN9P27oXlJ+0xhGnRr0aGB8TzbsN5fLrCvbz7ZzkWAKzboEUYP/GSTjBSZX2s0GD/ZFklRP\nC8ElIwbmbiSeB/vAM+jO8HgbqXlCdurbJRrwYF8k0JsM+BppxhOyeIwijqQaWQ4oGTcwxdLu\n8YTsdjpOjb2gyMo7NNcBV6MvgklGDkzRSI8nZI17mN66FuujGCEDXcmDp5HuOxaL+4rxYF8E\n0JNMeJd2tz2SxZfIzniwb35W3qFZliDDFSXnShaYYml3Xl+fkLW6XxUP9jFLPq0nOFe6wNSM\nFOihPseswDPoRj5gpHxBLzLCeosQhwRKENNKUnMlDUyttAsIjOQfqF1HaK60gWka6ftyLm5N\n/OUuKEq8QRfyQnWLkM3n2jlKAAfQg8x4Gmk340P0HSW4QrMqQcZWkJgreWCKpd1yxofoO0pw\nheY04KPvC8yVPjBFI825RchRAswG3ccP2R5p+qZVRwkwF/ReBHCMFC8wkKTpTWm5BglMsbST\nd9au+AF3Npm7pLDAJI10fdQc15GkgK6LQ3Z3NhQOei4SnkZaWzyG5ABKO8dA5/Pi7pLyAlMs\n7WjPemsluEJzGHDnC7XukgIDUzTSUhmfdHUFBYoT6LZ4eBrptF7RfljxswSwBb0WEe/STtqD\nfeWWIDatSck1aGCKpR2MJGbArRoTkmvYwBSNFAgUKbNBl8UlipEmd1+YFXNBj0WG5s6G93nf\nMhbSSGWWILYtScg1eGCSpd2MD9Gf8alDMNK8QOuGBOQaPjBFI21m3P39ueAwUomgu+LjaaRq\nzhOyf5V6/W1awDESJegtAVDdImR3+vs/pf6zWRml3YzAOa3EzpUlMM3S7rZHsjhIqvl9VdUf\njEQpOauRlCZnSrn6n2zYXj9E3/rBvpVafKC0owNdJQO6Oxts7274epleEbPDFvSUEPiNdD7/\nQ2lHJTm3hZTKpZRyzfAWoaIGfHYDKU3OlHJlNtLnqmp2XNXbZyiJokA3yYHRSH8vnSLwNYhE\nWaCXBMFopDe1+O+rWfr9WKi3EBI+ocmVIC7RKZVLKeXKaqSF+rovf6lFCAmf0NQG3Ck4pcmZ\nUq6sRuqdq8O9dp6gi2SR3R6pENBDwuA9Rvpo7lnFMZK3pHNV4KpYSL86S3ob6TKktsP62jlr\n9/L31Ozg0q7jj08s9w9yzSdXxWik8+dbcx1pUa1wHckHdI88WI3kJgEGoHcEkp2RnEP5Ax0j\nVUK5RgnM/hjJUYIpNJUBVz6SKU3OlHKNZyRzDIqXUdA1MoGR0gI9IxSUdvECHSKVp2RK5VJK\nucJIMQPnR6rB/+EV/SOLyDVDI+UMukUuvkaaBR7s8wK9IhhGI/E82JdvCaK0iyEVSSKLyJXV\nSHiwzytSjSyHU6SJLCJXViPhMQof0CWyYTRS75wEriPNAz0inOz2SHmWIMr4MoQiWWQRuXIf\nI+HBPrfI4YqSc40fmKKRltujfaD5wT7PrHIG3SEfTyPVlpjhJTzY5wR6IwE8jXT6fp/npfkS\nXKFiSxDNWmJzFRGYYml3Yb9dUnsJRjKvJDVXGYGpGqnmcCnZpr9F1keiVNAVaUBipJ/2u82t\nv2zMQaJU0BOJ4G+k07beHS1/TrWb7L7+cr4ES6jIEmRkDZG5iglMsrTbX042bNovkqV7nAJG\nMq8gMVc5gSka6XKaYXe6vVG55zEuUTDohnTwvY60/iFLZUSiXNALCeF7HYkskVEJrlBxJYjh\nXXG5igpMsbQLBIxkflNarrICYSROCemgC9ICRpIJeiAxsjNSHiXIRJuichUXiNKOQiKLAZ9q\nUlKu8gJhJE4JyRS++UkCI8mj7K1PlOyMlH4JYtGemFxFBqK0o5BIfsBtmpOSq8xAGIlTQioF\nb3rSwEiyKHfLEyeCkXaVWk48TVtsaWfZlohcxQZmX9od1qranbcWT9OWaiTbpiTkKjcwdyMd\nGgdt1PvpfFybP+Gh0AKn0M3OAkYjvavN+bxpn/47qWUIibQpc6szgdFI7ZPo1w92CPYh+umW\nIDPaiZ6r6MDcS7vWO99tTWd+LL1EI81pJnausgNzN9L75dWnIfcAABT7SURBVOio5dSUefQS\nCVPgJmcFo5FO1b2eUxOfk1LerCpvizOD9TrS5mafyrg/KrC0m9lGEeVSSrlmeGdDkgM+t4ki\nJmdKuWZopBQpbHOzhNVI++26uSi73uxDSaRIWVubKZwnG5adb+zDLUL3SM6/ZSmVSynlyjqK\nG1V9tx8Sfvypgp3+Tm7AXcKLmJwp5cpqpEod7suHYBdkU6OgTc0a9jsbdC/IJNKjnC3NnOz2\nSGmVII6xRZRLKeXKfYz0037RLI6RrpHOO3dnRdfAtPqVX5L1lNGqc9Zuafwei8sMK+BHCcgB\nP0RjyWik837TXEeq1ltcRzqXspWFwGokDgnnUPZA5yOkGBuJXOlDYSSSQJVQrlEkU8o1QyOl\nQgGbWBSxjKSMreQ/y/LfwsLIzkhplCCKX9IvELnSh8ou7ZIYcMUv6RmIXOlDZRspBTLfvCKB\nkfjJe+sKhfeCLMODffJLEKVZCizpH4hc6UNd1fBg33B18blGlUwpV1Yj8TzYJ52MN61oGI2E\nB/vOOW9Z4TAaqXfpqNDrSMrwKpAkTSBypQ+VvUcSPeDK+DKIJFEgcqUP9ThGYniwTzKZbhY4\n857+nvFgn6uEaPLcKtDAex2J4cE+uSXI83pyc5UgmVKuGd7ZIHbANauJzVWEZEq5ZmgkqWS4\nSaADjMRDflsEemRnJJkliH4dmblKkUwpVxiJJ3BkFZG5ipFMKdcMjSSRzDYHaICRwpPX1gAt\n2RlJXgky/r68XCVJppQrjBQ+0PC2uFxFSaaUa4ZGkkZGmwIMwEhhyWdLgJHsjCSrBDE3KitX\naZIp5QojhQ10P3ySFohc6UNlG0kSmWwGsABGCkceWwGsyM5IckqQ6Qbl5CpRMqVcYaRwge63\n4EkMRK70obKNJIUMNgHMAEYKQ/pbAGaRnZFklCB2jcnIVapkSrnyGunvn1KvH9dGjK2kbiTL\ntkTkKlYypVxZjfS3aD6Iq/1kyKy/+jLx9IEDjEZ6U+vaTevFa9NIxkZKO3vgBKORFm3g7+Ll\nN+vSzr6h+LlKlkwpV1Yj3bzz9/qas5FmtBM9V9GSKeXKaqQX9Xdbes23tEs4dWCmPsBvfvRv\nOrTnmMda/bsu/arXXI2UbubATOeD67VvO7TomsrbPYWPUWN7SsQuQeY1gnIpSGAYSUFGOn/d\nvxTp91+WRprZRvGTM0wgkaQafDOe2UnZ3dkQk0TTLhZTXTS0jJrCQd4lZ3ESAUgz61KZMIDR\nSLr1HTKYH3Llc1W1Nze8fYaSiFiCzG+gvHKJJdAmUm8KjW80EbGPkf5eOrm8BpHwCfUN5PyT\nJHNySgmcZyT9uaLZVRzj8L+pxX9fzdLvx0K9hZCISIIpl8Rw12PNoI0RG7EaaaG+7stfahFC\nIh7pZVwST6aY2r9oQqYkHLKaH9LGqbEXZBI+oV6BbsGZlUtSAp8jTUYaCRRsJJ49UpQBd4xN\nenLKDXyK1Jhm0kjmk+PTmmFCGupjpI/fZim3Y6TE0i2OmecN3DRYQlpeOxvw8jd8d7iB6fwo\nATngx/AzMq9Ix00xGun8+dZcR1pUqxyvI6UgWWiutrsf3nnnocYgwT5uFuNDLSlicsoNhJE4\nJahIKNWSoTwc0guwhAiUICKdTAsn9EDFMpL5D0QypZ3yUUy7XJIbqI20ai3B0i4PIykvxeQn\np9DAoowUXYKCRNLMG8vLQbmWdtElCEgjy7yxvrIKI3GFzg5Ug/8ZJOMFSs11+k6fWa3ZrEQW\n6nFBNqcH+9TTQnDJiIFCc9Xf7JO5kXge7OMigRQLwHAXXbC76kYyYQlpyOrBPvkZFgGfUSYz\nYQlpyOkxiqlCIoBk3ECpuVofI9FJkoWS3JSW9nUkmmGTOjmFSMJII+TzqLnw9MpCQl135j5G\nyuPBPtnZFYeM4eA8/W1+sI9EgmO3Plwxu3JJiKRt4PN6mZd2mTzY97RejpNTgiSM5I+MvbUW\nwakViowRgZHmITezYpExJNkZKfDZVkpFweWSBEmUdv5INZJ2nRwnpwRJGMkfGXvrJ4SmlRXt\nRaH7pSHdNSKqy6+kwEj2yMwqKwa3mepuo3v6nYxhyc5I4XbrYyvkWC7FkqQxEko7AolgnTj6\nvvTJSRLII2nxCMTz72CkqBJzEZhSfowZyUzsrBtgJDvkZZQjTnskGWRnpDC7ddObwsslmkDB\nx0iekiSBMJJdYJhhg5EeK12dASPRImy/LSyd7Oh6Q82+jiQDGGkaWdlkSG8nk2hvZ2ck+t26\n83lxd0l5gSH6tbPX6e6SvBVR2lFIkHei84Vad0mBgU6R7XHO6Hs374ycokupXzM0EjWCUkkK\n82nq3rtSz2jPAkYyIycT8XSdoL8GdNa833nFk2YgsjMS7W7dprWUShC/XE2TvW8GXan2vPJI\nTUeSK28gjGQOtLsG4qqYmJHM+43+mTd77gGkuXIHZmgkSoSkIQPzgUzfGKYdTf/9cPkyAyON\nIyMLKZjn/tQuZ1ZjKZKdkeh267YtpVSCeOQ6t1QzesXGRin1K4w0HmjdUEoDTmEk4/vDtU3X\nkaYUXVPN3Ug2u3xPCUIEpCCKqXEbvptT2WYDo5F2KRkpfgbSmBy30qzTh7O0O1Sr0BJUu/U5\nraRUgswM7HjD+exApGtezJK8x0gHtQktQdOJsxpJacBnBA5PH7jucmCkAGo7dQgtQUHJJcoV\nbQ2Ofhknu7N2yavLQHs0i44ZJzsj+e/W57aQUglifW1MT0hJusACSjsGCe9OnN1ASgPuZCQe\nSbrA/I20366bkVlv9qEkvEH5csF3N1QejEY6LTujYz4RHm/w8p02dq64rQUjzYTRSBtVfbcn\n7Y4/lflEeLTSjvX8LmcJYnkhfPywCKUdeairWtU5931QVQgJn1DlGpzEgM810tNVIxiJPNRV\nbfDJsyEkPMm3irE75umvlW9vhCC7PZIHGc8cTc02Teykk4L3GOnn2CzJPEZy3tO6KjKWIC57\nJE/JeIG5l3bnVWecliejhHL+UR6x3D+MuQ6qtsn10K9zNRmNdN5vmutI1Xor7zpSBElO7Ao2\nlHXOsBpJkkR0RW7sDAIbOZKdkdxClYeme7K8kspHEcdI9KEZGslrjqUy4DBSQMloRpqo1Skk\n7Em8lLkeW6uJuizxrRQOjJT6DLO+AJT2ZkoHpd0tItUSZJaRUiqXUsoVRnoEJDrgGh+NOAlG\nCiiZn5EESwVBayQ9sVPNGt4LsvIe7Et+dsEyMmA0Es+DffNClXYxpCJJ5CNwpo9SKpdSypXV\nSAIf7FMjy+EUaSLvgXNruJQmZ0q5shpJ3mMUqZdAXe+oyetIICSMRuoNsYTrSMlPORwVySG7\nPZJ9qDK+DKFoEaludyl0l5+Mom7rzjVSSuVSSrlyHyNJerBvuGKkAe+ZQH/I82yVJyPx5JpI\nYO5GmvNgn6uEPTKKoYEPbI30tDZnzkAH73UkOQ/2CZl6fR+YfGR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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "plot(ecdf(pfis), asp=1,\n     xlab=\"x\", ylab=\"y = prob(pval <= x)\", \n     xlim=c(0,1), ylim=c(0,1),\n     verticals=TRUE, main=\"ECDF for chisq.test(correct=FALSE)\")\nabline(0,1)\naxis(side=1, at=seq(0, 1, length=11) , tck=1, lty=\"dotted\")\naxis(side=2, at=seq(0, 1, length=11) , tck=1, lty=\"dotted\")"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "ecdf(pfis)(0.05)",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "0.01428",
            "text/markdown": "0.01428",
            "text/latex": "0.01428",
            "text/plain": "[1] 0.01428"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "ecdf(pfismidp)(0.05)",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "0.01537",
            "text/markdown": "0.01537",
            "text/latex": "0.01537",
            "text/plain": "[1] 0.01537"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "ecdf(pchi)(0.05)",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "0.0454266173789547",
            "text/markdown": "0.0454266173789547",
            "text/latex": "0.0454266173789547",
            "text/plain": "[1] 0.04542662"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "ecdf(pchic)(0.05)",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "0.0102537242775556",
            "text/markdown": "0.0102537242775556",
            "text/latex": "0.0102537242775556",
            "text/plain": "[1] 0.01025372"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "@webio": {
      "lastKernelId": null,
      "lastCommId": null
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R",
      "language": "R"
    },
    "language_info": {
      "name": "R",
      "codemirror_mode": "r",
      "pygments_lexer": "r",
      "mimetype": "text/x-r-source",
      "file_extension": ".r",
      "version": "3.5.3"
    },
    "toc": {
      "nav_menu": {},
      "number_sections": true,
      "sideBar": true,
      "skip_h1_title": true,
      "base_numbering": 1,
      "title_cell": "目次",
      "title_sidebar": "目次",
      "toc_cell": false,
      "toc_position": {
        "height": "calc(100% - 180px)",
        "width": "241.594px",
        "left": "10px",
        "top": "150px"
      },
      "toc_section_display": true,
      "toc_window_display": false
    },
    "gist": {
      "id": "a980825ad3be8b811acd025bdee6d35d",
      "data": {
        "description": "Fisher検定とカイ二乗検定のR言語による比較",
        "public": true
      }
    },
    "_draft": {
      "nbviewer_url": "https://gist.github.com/a980825ad3be8b811acd025bdee6d35d"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}