{"id":2076,"date":"2023-07-23T19:30:51","date_gmt":"2023-07-23T19:30:51","guid":{"rendered":"https:\/\/statorials.org\/ja\/glm-fit-0-%e3%81%be%e3%81%9f%e3%81%af-1-%e3%81%8b%e3%82%99%e7%99%ba%e7%94%9f%e3%81%99%e3%82%8b%e7%a2%ba%e7%8e%87%e3%82%92%e6%95%b0%e5%80%a4%e7%9a%84%e3%81%ab%e5%bd%93%e3%81%a6%e3%81%af%e3%82%81\/"},"modified":"2023-07-23T19:30:51","modified_gmt":"2023-07-23T19:30:51","slug":"glm-fit-0-%e3%81%be%e3%81%9f%e3%81%af-1-%e3%81%8b%e3%82%99%e7%99%ba%e7%94%9f%e3%81%99%e3%82%8b%e7%a2%ba%e7%8e%87%e3%82%92%e6%95%b0%e5%80%a4%e7%9a%84%e3%81%ab%e5%bd%93%e3%81%a6%e3%81%af%e3%82%81","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/glm-fit-0-%e3%81%be%e3%81%9f%e3%81%af-1-%e3%81%8b%e3%82%99%e7%99%ba%e7%94%9f%e3%81%99%e3%82%8b%e7%a2%ba%e7%8e%87%e3%82%92%e6%95%b0%e5%80%a4%e7%9a%84%e3%81%ab%e5%bd%93%e3%81%a6%e3%81%af%e3%82%81\/","title":{"rendered":"\u6271\u3044\u65b9: glm.fit: \u6570\u5024\u7684\u306b\u8abf\u6574\u3055\u308c\u305f\u78ba\u7387 0 \u307e\u305f\u306f 1 \u304c\u767a\u751f\u3059\u308b"},"content":{"rendered":"

<\/p>\n

\n

R \u3067\u767a\u751f\u3059\u308b\u53ef\u80fd\u6027\u306e\u3042\u308b\u8b66\u544a\u30e1\u30c3\u30bb\u30fc\u30b8\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n

 Warning message:\nglm.fit: fitted probabilities numerically 0 or 1 occurred \n<\/strong><\/pre>\n
\u3053\u306e\u8b66\u544a\u306f\u3001\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3057\u3066\u3044\u308b\u3068\u304d\u306b\u3001\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u5185\u306e 1 \u3064\u4ee5\u4e0a\u306e\u89b3\u6e2c\u5024\u306e\u4e88\u6e2c\u78ba\u7387\u304c 0 \u304b 1 \u304b\u3092\u533a\u5225\u3067\u304d\u306a\u3044\u5834\u5408\u306b\u767a\u751f\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u308c\u306f\u8b66\u544a\u30e1\u30c3\u30bb\u30fc\u30b8<\/strong>\u3067\u3042\u308a\u3001\u30a8\u30e9\u30fc\u3067\u306f\u306a\u3044\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u3053\u306e\u30a8\u30e9\u30fc\u304c\u767a\u751f\u3057\u305f\u5834\u5408\u3067\u3082\u3001\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u306f\u9069\u5408\u3057\u307e\u3059\u304c\u3001\u5143\u306e\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u3092\u5206\u6790\u3057\u3066\u3001\u3053\u306e\u8b66\u544a\u30e1\u30c3\u30bb\u30fc\u30b8\u304c\u8868\u793a\u3055\u308c\u308b\u539f\u56e0\u3068\u306a\u308b\u5916\u308c\u5024\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u78ba\u8a8d\u3059\u308b\u3068\u5f79\u7acb\u3064\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u3053\u306e\u8b66\u544a\u30e1\u30c3\u30bb\u30fc\u30b8\u306b\u5b9f\u969b\u306b\u5bfe\u51e6\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u8b66\u544a\u3092\u518d\u73fe\u3059\u308b\u65b9\u6cd5<\/strong><\/h3>\n
\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3092 R \u306e\u6b21\u306e\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u306b\u5f53\u3066\u306f\u3081\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #create data frame\ndf <- data. frame<\/span> (y = c(0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1),\n                 x1 = c(3, 3, 4, 4, 3, 2, 5, 8, 9, 9, 9, 8, 9, 9, 9),\n                 x2 = c(8, 7, 7, 6, 5, 6, 5, 2, 2, 3, 4, 3, 7, 4, 4))\n\n#fit logistic regression model\n<\/span>model <- glm(y ~ x1 + x2, data=df, family=binomial)\n\n#view model summary\n<\/span>summary(model)\n\nWarning message:\nglm.fit: fitted probabilities numerically 0 or 1 occurred \n\nCall:\nglm(formula = y ~ x1 + x2, family = binomial, data = df)\n\nDeviance Residuals: \n       Min 1Q Median 3Q Max  \n-1.729e-05 -2.110e-08 2.110e-08 2.110e-08 1.515e-05  \n\nCoefficients:\n              Estimate Std. Error z value Pr(>|z|)\n(Intercept) -75.205 307338.933 0 1\nx1 13,309 28512,818 0 1\nx2 -2.793 37342.280 0 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n    Null deviance: 2.0728e+01 on 14 degrees of freedom\nResidual deviance: 5.6951e-10 on 12 degrees of freedom\nAIC: 6\n\nNumber of Fisher Scoring iterations: 24<\/span><\/span><\/strong><\/span><\/pre>\n
\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u306f\u30c7\u30fc\u30bf\u306b\u3046\u307e\u304f\u9069\u5408\u3057\u307e\u3057\u305f\u304c\u3001\u78ba\u7387\u3092 0 \u307e\u305f\u306f 1 \u306b\u6570\u5024\u7684\u306b\u8abf\u6574\u3059\u308b<\/strong>\u8b66\u544a\u30e1\u30c3\u30bb\u30fc\u30b8\u304c\u8868\u793a\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n
\u8fd1\u4f3c\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u5143\u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u89b3\u6e2c\u5024\u306e\u5fdc\u7b54\u5024\u3092\u4e88\u6e2c\u3059\u308b\u3068\u3001\u307b\u307c\u3059\u3079\u3066\u306e\u4e88\u6e2c\u78ba\u7387\u304c 0 \u3068 1 \u3068\u533a\u5225\u3067\u304d\u306a\u3044\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n
 #use fitted model to predict response values\n<\/span>df$y_pred = predict(model, df, type=\" response<\/span> \")\n\n#view updated data frame\n<\/span>df\n\n   y x1 x2 y_pred\n1 0 3 8 2.220446e-16\n2 0 3 7 2.220446e-16\n3 0 4 7 2.220446e-16\n4 0 4 6 2.220446e-16\n5 0 3 5 2.220446e-16\n6 0 2 6 2.220446e-16\n7 0 5 5 1.494599e-10\n8 1 8 2 1.000000e+00\n9 1 9 2 1.000000e+00\n10 1 9 3 1.000000e+00\n11 1 9 4 1.000000e+00\n12 1 8 3 1.000000e+00\n13 1 9 7 1.000000e+00\n14 1 9 4 1.000000e+00\n15 1 9 4 1.000000e+00<\/strong><\/span><\/pre>\n
\u8b66\u544a\u306e\u5bfe\u51e6\u65b9\u6cd5<\/strong><\/span><\/h3>\n
\u3053\u306e\u8b66\u544a\u30e1\u30c3\u30bb\u30fc\u30b8\u3092\u51e6\u7406\u3059\u308b\u306b\u306f\u3001\u6b21\u306e 3 \u3064\u306e\u65b9\u6cd5\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n
 (1) \u7121\u8996\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/strong><\/span><\/p>\n
\u5834\u5408\u306b\u3088\u3063\u3066\u306f\u3001\u3053\u306e\u8b66\u544a\u30e1\u30c3\u30bb\u30fc\u30b8\u306f\u5fc5\u305a\u3057\u3082\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u554f\u984c\u304c\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u308b\u308f\u3051\u3067\u306f\u306a\u3044\u305f\u3081\u3001\u5358\u306b\u7121\u8996\u3057\u3066\u69cb\u3044\u307e\u305b\u3093\u3002\u3053\u308c\u306f\u5358\u306b\u3001\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u5185\u306e 1 \u3064\u4ee5\u4e0a\u306e\u89b3\u6e2c\u5024\u304c 0 \u307e\u305f\u306f 1 \u3068\u533a\u5225\u3067\u304d\u306a\u3044\u4e88\u6e2c\u5024\u3092\u6301\u3064\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/span><\/p>\n
 (2) \u30b5\u30f3\u30d7\u30eb\u30b5\u30a4\u30ba\u3092\u5897\u3084\u3059\u3002<\/strong><\/span><\/p>\n
\u307e\u305f\u3001\u4fe1\u983c\u6027\u306e\u9ad8\u3044\u30e2\u30c7\u30eb\u9069\u5408\u3092\u63d0\u4f9b\u3059\u308b\u306e\u306b\u5341\u5206\u306a\u30c7\u30fc\u30bf\u304c\u5b58\u5728\u3057\u306a\u3044\u5c0f\u3055\u306a\u30c7\u30fc\u30bf \u30d6\u30ed\u30c3\u30af\u3092\u64cd\u4f5c\u3059\u308b\u3068\u304d\u306b\u3001\u3053\u306e\u8b66\u544a\u30e1\u30c3\u30bb\u30fc\u30b8\u304c\u8868\u793a\u3055\u308c\u308b\u5834\u5408\u3082\u3042\u308a\u307e\u3059\u3002\u3053\u306e\u30a8\u30e9\u30fc\u3092\u4fee\u6b63\u3059\u308b\u306b\u306f\u3001\u30e2\u30c7\u30eb\u306b\u5165\u529b\u3059\u308b\u89b3\u6e2c\u5024\u306e\u30b5\u30f3\u30d7\u30eb \u30b5\u30a4\u30ba\u3092\u5897\u3084\u3059\u3060\u3051\u3067\u3059\u3002<\/span><\/p>\n
 (3) \u5916\u308c\u5024\u3092\u524a\u9664\u3057\u307e\u3059\u3002<\/strong><\/span><\/p>\n
\u4ed6\u306e\u30b1\u30fc\u30b9\u3067\u306f\u3001\u5143\u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u5916\u308c\u5024\u304c\u3042\u308a\u3001\u5c11\u6570\u306e\u89b3\u6e2c\u5024\u306e\u307f\u304c 0 \u307e\u305f\u306f 1 \u306b\u8fd1\u3044\u78ba\u7387\u3092\u6301\u3064\u5834\u5408\u306b\u3001\u3053\u306e\u30a8\u30e9\u30fc\u304c\u767a\u751f\u3057\u307e\u3059\u3002\u3053\u308c\u3089\u306e\u5916\u308c\u5024\u3092\u524a\u9664\u3059\u308b\u3068\u3001\u8b66\u544a\u30e1\u30c3\u30bb\u30fc\u30b8\u304c\u6d88\u3048\u308b\u3053\u3068\u304c\u3088\u304f\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n
\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u4ed6\u306e\u8b66\u544a\u3084\u30a8\u30e9\u30fc\u3092\u51e6\u7406\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n
 R \u3067\u306e\u4fee\u6b63\u65b9\u6cd5: ExtractVars \u306e\u7121\u52b9\u306a\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u5f0f
R \u3067\u4fee\u6b63\u3059\u308b\u65b9\u6cd5: \u5f15\u6570\u304c\u6570\u5024\u3067\u3082\u8ad6\u7406\u3067\u3082\u306a\u3044: na \u3092\u8fd4\u3059<\/a>
\u4fee\u6b63\u65b9\u6cd5:randomForest.default(m, y, \u2026): \u5916\u90e8\u95a2\u6570\u547c\u3073\u51fa\u3057\u306e Na\/NaN\/Inf<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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