{"id":4484,"date":"2023-07-10T17:15:06","date_gmt":"2023-07-10T17:15:06","guid":{"rendered":"https:\/\/statorials.org\/ja\/r-%e3%83%ad%e3%82%b7%e3%82%99%e3%82%b9%e3%83%86%e3%82%a3%e3%83%83%e3%82%af%e5%9b%9e%e5%b8%b0%e3%82%aa%e3%83%83%e3%82%b9%e3%82%99%e6%af%94\/"},"modified":"2023-07-10T17:15:06","modified_gmt":"2023-07-10T17:15:06","slug":"r-%e3%83%ad%e3%82%b7%e3%82%99%e3%82%b9%e3%83%86%e3%82%a3%e3%83%83%e3%82%af%e5%9b%9e%e5%b8%b0%e3%82%aa%e3%83%83%e3%82%b9%e3%82%99%e6%af%94","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r-%e3%83%ad%e3%82%b7%e3%82%99%e3%82%b9%e3%83%86%e3%82%a3%e3%83%83%e3%82%af%e5%9b%9e%e5%b8%b0%e3%82%aa%e3%83%83%e3%82%b9%e3%82%99%e6%af%94\/","title":{"rendered":"A: \u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3067\u30aa\u30c3\u30ba\u6bd4\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ja\/\u30ed\u30b7\u3099\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u306f\u3001<\/a>\u5fdc\u7b54\u5909\u6570\u304c\u30d0\u30a4\u30ca\u30ea\u306e\u5834\u5408\u306b\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3067\u304d\u308b\u65b9\u6cd5\u3067\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">R \u3067\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3059\u308b\u3068\u3001\u30e2\u30c7\u30eb\u306e\u6982\u8981\u306e\u4fc2\u6570\u306f\u3001\u5404\u4e88\u6e2c\u5909\u6570\u306e 1 \u5358\u4f4d\u306e\u5897\u52a0\u306b\u95a2\u9023\u4ed8\u3051\u3089\u308c\u305f\u5fdc\u7b54\u5909\u6570\u306e<strong>\u5bfe\u6570\u30aa\u30c3\u30ba\u306e<\/strong>\u5e73\u5747\u5909\u5316\u3092\u8868\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3060\u3057\u3001\u4ee3\u308f\u308a\u306b\u30e2\u30c7\u30eb\u5185\u306e\u4e88\u6e2c\u5909\u6570\u306e<strong>\u30aa\u30c3\u30ba\u6bd4<\/strong>\u3092\u8a08\u7b97\u3057\u305f\u3044\u5834\u5408\u3082\u3088\u304f\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u5185\u306e\u5404\u4e88\u6e2c\u5b50\u5909\u6570\u306e\u30aa\u30c3\u30ba\u6bd4\u3092\u3059\u3070\u3084\u304f\u8a08\u7b97\u3059\u308b\u306b\u306f\u3001\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>exp(coef(model))\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u5404\u30aa\u30c3\u30ba\u6bd4\u306e 95% \u4fe1\u983c\u533a\u9593\u3092\u8a08\u7b97\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>exp(cbind(Odds_Ratio = coef(model), confint(model)))<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066 R \u306e\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u30aa\u30c3\u30ba\u6bd4\u3092\u8a08\u7b97\u304a\u3088\u3073\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u4f8b: R \u306e\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3067\u306e\u30aa\u30c3\u30ba\u6bd4\u306e\u8a08\u7b97<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u3053\u306e\u4f8b\u3067\u306f\u3001R \u306e<strong>ISLR<\/strong>\u30d1\u30c3\u30b1\u30fc\u30b8\u306e<strong>\u30c7\u30d5\u30a9\u30eb\u30c8<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u6982\u8981\u3092\u8aad\u307f\u8fbc\u3093\u3067\u8868\u793a\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">library<\/span> (ISLR)\n\n<span style=\"color: #008080;\">#view first five rows of Default dataset\n<\/span>head(Default)\n\n  default student balance income\n1 No No 729.5265 44361.625\n2 No Yes 817.1804 12106.135\n3 No No 1073.5492 31767.139\n4 No No 529.2506 35704.494\n5 No No 785.6559 38463.496\n6 No Yes 919.5885 7491.559<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f\u300110,000 \u4eba\u306e\u500b\u4eba\u306b\u95a2\u3059\u308b\u6b21\u306e\u60c5\u5831\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u30c7\u30d5\u30a9\u30eb\u30c8:<\/strong>\u500b\u4eba\u304c\u30c7\u30d5\u30a9\u30eb\u30c8\u3057\u305f\u304b\u3069\u3046\u304b\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u5b66\u751f:<\/strong>\u500b\u4eba\u304c\u5b66\u751f\u3067\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u6b8b\u9ad8:<\/strong>\u500b\u4eba\u304c\u4fdd\u6709\u3059\u308b\u5e73\u5747\u6b8b\u9ad8\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u53ce\u5165\uff1a<\/strong>\u500b\u4eba\u306e\u53ce\u5165\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u5b66\u751f\u306e\u30b9\u30c6\u30fc\u30bf\u30b9\u3001\u9280\u884c\u6b8b\u9ad8\u3001\u53ce\u5165\u3092\u4f7f\u7528\u3057\u3066\u3001\u7279\u5b9a\u306e\u500b\u4eba\u304c\u50b5\u52d9\u4e0d\u5c65\u884c\u306b\u306a\u308b\u78ba\u7387\u3092\u4e88\u6e2c\u3059\u308b\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>glm<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066 family=&#8217;binomial&#8217; \u3092\u6307\u5b9a\u3059\u308b\u3068\u3001R \u304c\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u9069\u5408\u3055\u305b\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit logistic regression model<\/span>\nmodel &lt;- glm(default~student+balance+income, family=' <span style=\"color: #ff0000;\">binomial<\/span> ', data=Default)\n\n<span style=\"color: #008080;\">#disable scientific notation for model summary<\/span>\noptions(scipen=999)\n\n<span style=\"color: #008080;\">#view model summary<\/span>\nsummary(model)\n\nCall:\nglm(formula = default ~ student + balance + income, family = \"binomial\", \n    data = train)\n\nDeviance Residuals: \n    Min 1Q Median 3Q Max  \n-2.5586 -0.1353 -0.0519 -0.0177 3.7973  \n\nCoefficients:\n                 Estimate Std. Error z value Pr(&gt;|z|)    \n(Intercept) -11.478101194 0.623409555 -18.412 &lt;0.0000000000000002 ***\nstudentYes -0.493292438 0.285735949 -1.726 0.0843 .  \nbalance 0.005988059 0.000293765 20.384 &lt;0.0000000000000002 ***\nincome 0.000007857 0.000009965 0.788 0.4304    \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n    Null deviance: 2021.1 on 6963 degrees of freedom\nResidual deviance: 1065.4 on 6960 degrees of freedom\nAIC: 1073.4\n\nNumber of Fisher Scoring iterations: 8\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u306e\u4fc2\u6570\u306f\u3001\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u5bfe\u6570\u30aa\u30c3\u30ba\u306e\u5e73\u5747\u5909\u5316\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u3001<strong>\u6b8b\u9ad8<\/strong>\u304c 1 \u5358\u4f4d\u5897\u52a0\u3059\u308b\u3068\u3001\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u5bfe\u6570\u78ba\u7387\u304c\u5e73\u5747<strong>0.005988<\/strong>\u5897\u52a0\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee3\u308f\u308a\u306b\u3001\u5404\u4e88\u6e2c\u5b50\u5909\u6570\u306e\u30aa\u30c3\u30ba\u6bd4\u3092\u8a08\u7b97\u3059\u308b\u306b\u306f\u3001\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate odds ratio for each predictor variable<\/span>\nexp(coef(model))\n\n  (Intercept) studentYes balance income \n0.00001903854 0.52373166965 1.00575299051 1.00000303345 \n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u5404\u30aa\u30c3\u30ba\u6bd4\u3068\u3001\u5404\u30aa\u30c3\u30ba\u6bd4\u306e 95% \u4fe1\u983c\u533a\u9593\u3092\u8a08\u7b97\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate odds ratio and 95% confidence interval for each predictor variable<\/span> \nexp(cbind(Odds_Ratio = coef(model), confint(model)))\n\n               Odds_Ratio 2.5% 97.5%\n(Intercept) 0.00001903854 0.000007074481 0.0000487808\nstudentYes 0.52373166965 0.329882707270 0.8334223982\nbalance 1.00575299051 1.005308940686 1.0062238757\nincome 1.00000303345 0.999986952969 1.0000191246\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u5404\u4fc2\u6570\u306e\u30aa\u30c3\u30ba\u6bd4\u306f\u3001\u4ed6\u306e\u3059\u3079\u3066\u306e\u4e88\u6e2c\u5909\u6570\u304c\u4e00\u5b9a\u306e\u307e\u307e\u3067\u3042\u308b\u3068\u4eee\u5b9a\u3057\u305f\u5834\u5408\u3001\u500b\u4eba\u306e\u30c7\u30d5\u30a9\u30eb\u30c8\u78ba\u7387\u306e\u5e73\u5747\u5897\u52a0\u3092\u8868\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u3001\u4e88\u6e2c\u5b50\u5909\u6570\u306e<strong>\u6b8b\u9ad8<\/strong>\u306e\u30aa\u30c3\u30ba\u6bd4\u306f<strong>1.0057<\/strong>\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u308c\u306f\u3001<em>\u5b66\u751f\u306e\u30b9\u30c6\u30fc\u30bf\u30b9\u3068\u53ce\u5165\u304c<\/em>\u4e00\u5b9a\u3067\u3042\u308b\u3068\u4eee\u5b9a\u3059\u308b\u3068\u3001\u500b\u4eba\u306e\u6b8b\u9ad8\u304c 1 \u30c9\u30eb\u5897\u3048\u308b\u3054\u3068\u306b\u3001\u305d\u306e\u500b\u4eba\u304c\u30ed\u30fc\u30f3\u3092\u6ede\u7d0d\u3059\u308b\u78ba\u7387\u304c<strong>1.0057<\/strong>\u500d\u5897\u52a0\u3059\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ed6\u306e\u4e88\u6e2c\u5909\u6570\u306e\u30aa\u30c3\u30ba\u6bd4\u3082\u540c\u69d8\u306b\u89e3\u91c8\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u4ed6\u306e\u4e00\u822c\u7684\u306a\u30bf\u30b9\u30af\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ja\/r-\u30ed\u30b7\u3099\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u4e88\u6e2c\/\" target=\"_blank\" rel=\"noopener\">R \u306e\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3067 Predict() \u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/prz-\u30ed\u30b7\u3099\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u51fa\u529b-r-\u3092\u89e3\u91c8\u3059\u308b\/\">R \u306e\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u51fa\u529b\u3067 Pr(&gt;|z|) \u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/r-\u3066\u3099\u30ed\u30b7\u3099\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u3092\u30d5\u309a\u30ed\u30c3\u30c8\u3059\u308b\/\" target=\"_blank\" rel=\"noopener\">R 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