{"id":2079,"date":"2023-07-23T19:30:51","date_gmt":"2023-07-23T19:30:51","guid":{"rendered":"https:\/\/statorials.org\/cn\/glm-fit-%e6%8b%9f%e5%90%88%e5%87%ba%e7%8e%b0%e6%95%b0%e5%ad%97-0-%e6%88%96-1-%e7%9a%84%e6%a6%82%e7%8e%87\/"},"modified":"2023-07-23T19:30:51","modified_gmt":"2023-07-23T19:30:51","slug":"glm-fit-%e6%8b%9f%e5%90%88%e5%87%ba%e7%8e%b0%e6%95%b0%e5%ad%97-0-%e6%88%96-1-%e7%9a%84%e6%a6%82%e7%8e%87","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/glm-fit-%e6%8b%9f%e5%90%88%e5%87%ba%e7%8e%b0%e6%95%b0%e5%ad%97-0-%e6%88%96-1-%e7%9a%84%e6%a6%82%e7%8e%87\/","title":{"rendered":"\u5982\u4f55\u5904\u7406\uff1aglm.fit\uff1a\u6570\u503c\u8c03\u6574\u6982\u7387\u4e3a0\u62161\u53d1\u751f"},"content":{"rendered":"

<\/p>\n

\n

\u60a8\u5728 R \u4e2d\u53ef\u80fd\u9047\u5230\u7684\u8b66\u544a\u6d88\u606f\u662f\uff1a<\/span><\/p>\n

 Warning message:\nglm.fit: fitted probabilities numerically 0 or 1 occurred \n<\/strong><\/pre>\n
\u5f53\u60a8\u62df\u5408\u903b\u8f91\u56de\u5f52\u6a21\u578b\u5e76\u4e14\u6570\u636e\u5e93\u4e2d\u4e00\u4e2a\u6216\u591a\u4e2a\u89c2\u6d4b\u503c\u7684\u9884\u6d4b\u6982\u7387\u65e0\u6cd5\u533a\u5206 0 \u6216 1 \u65f6\uff0c\u4f1a\u51fa\u73b0\u6b64\u8b66\u544a\u3002<\/span><\/p>\n
\u8bf7\u6ce8\u610f\uff0c\u8fd9\u662f\u4e00\u6761\u8b66\u544a\u6d88\u606f<\/strong>\uff0c\u800c\u4e0d\u662f\u9519\u8bef\u3002\u5373\u4f7f\u60a8\u6536\u5230\u6b64\u9519\u8bef\uff0c\u60a8\u7684\u903b\u8f91\u56de\u5f52\u6a21\u578b\u4ecd\u7136\u9002\u5408\uff0c\u4f46\u5206\u6790\u539f\u59cb\u6570\u636e\u6846\u4ee5\u67e5\u770b\u662f\u5426\u6709\u4efb\u4f55\u5f02\u5e38\u503c\u5bfc\u81f4\u51fa\u73b0\u6b64\u8b66\u544a\u6d88\u606f\u53ef\u80fd\u4f1a\u6709\u6240\u5e2e\u52a9\u3002<\/span><\/p>\n
\u672c\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u5904\u7406\u6b64\u8b66\u544a\u6d88\u606f\u3002<\/span><\/p>\n
\u5982\u4f55\u91cd\u73b0\u8b66\u544a<\/strong><\/h3>\n
\u5047\u8bbe\u6211\u4eec\u5c06\u903b\u8f91\u56de\u5f52\u6a21\u578b\u62df\u5408\u5230 R \u4e2d\u7684\u4ee5\u4e0b\u6570\u636e\u6846\uff1a<\/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
\u6211\u4eec\u7684\u903b\u8f91\u56de\u5f52\u6a21\u578b\u6210\u529f\u62df\u5408\u4e86\u6570\u636e\uff0c\u4f46\u6211\u4eec\u6536\u5230\u4e00\u6761\u8b66\u544a\u6d88\u606f\uff0c\u5c06\u6982\u7387\u6570\u5b57\u8c03\u6574\u4e3a 0 \u6216 1<\/strong> \u3002<\/span><\/p>\n
\u5982\u679c\u6211\u4eec\u4f7f\u7528\u62df\u5408\u7684\u903b\u8f91\u56de\u5f52\u6a21\u578b\u5bf9\u539f\u59cb\u6570\u636e\u5e93\u4e2d\u89c2\u6d4b\u503c\u7684\u54cd\u5e94\u503c\u8fdb\u884c\u9884\u6d4b\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u51e0\u4e4e\u6240\u6709\u7684\u9884\u6d4b\u6982\u7387\u90fd\u4e0e 0 \u548c 1 \u6ca1\u6709\u533a\u522b\uff1a<\/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
\u5982\u4f55\u5904\u7406\u8b66\u544a<\/strong><\/span><\/h3>\n
\u6709\u4e09\u79cd\u65b9\u6cd5\u53ef\u4ee5\u5904\u7406\u6b64\u8b66\u544a\u6d88\u606f\uff1a<\/span><\/p>\n
 (1) \u5ffd\u7565\u5b83\u3002<\/strong><\/span><\/p>\n
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u60a8\u53ef\u4ee5\u7b80\u5355\u5730\u5ffd\u7565\u6b64\u8b66\u544a\u6d88\u606f\uff0c\u56e0\u4e3a\u5b83\u5e76\u4e0d\u4e00\u5b9a\u8868\u660e\u903b\u8f91\u56de\u5f52\u6a21\u578b\u5b58\u5728\u95ee\u9898\u3002\u8fd9\u4ec5\u4ec5\u610f\u5473\u7740\u6570\u636e\u6846\u4e2d\u7684\u4e00\u4e2a\u6216\u591a\u4e2a\u89c2\u6d4b\u503c\u7684\u9884\u6d4b\u503c\u4e0e 0 \u6216 1 \u65e0\u6cd5\u533a\u5206\u3002<\/span><\/p>\n
 (2)\u589e\u52a0\u6837\u672c\u91cf\u3002<\/strong><\/span><\/p>\n
\u5728\u5176\u4ed6\u60c5\u51b5\u4e0b\uff0c\u5f53\u5904\u7406\u5c0f\u6570\u636e\u5757\u65f6\uff0c\u5982\u679c\u6ca1\u6709\u8db3\u591f\u7684\u6570\u636e\u6765\u63d0\u4f9b\u53ef\u9760\u7684\u6a21\u578b\u62df\u5408\uff0c\u5219\u4f1a\u51fa\u73b0\u6b64\u8b66\u544a\u6d88\u606f\u3002\u8981\u7ea0\u6b63\u6b64\u9519\u8bef\uff0c\u53ea\u9700\u589e\u52a0\u8f93\u5165\u6a21\u578b\u7684\u89c2\u6d4b\u503c\u7684\u6837\u672c\u91cf\u5373\u53ef\u3002<\/span><\/p>\n
 (3)\u53bb\u9664\u5f02\u5e38\u503c\u3002<\/strong><\/span><\/p>\n
\u5728\u5176\u4ed6\u60c5\u51b5\u4e0b\uff0c\u5f53\u539f\u59cb\u6570\u636e\u5e93\u4e2d\u5b58\u5728\u5f02\u5e38\u503c\u5e76\u4e14\u53ea\u6709\u5c11\u91cf\u89c2\u6d4b\u503c\u7684\u6982\u7387\u63a5\u8fd1 0 \u6216 1 \u65f6\uff0c\u5c31\u4f1a\u51fa\u73b0\u6b64\u9519\u8bef\u3002\u901a\u8fc7\u5220\u9664\u8fd9\u4e9b\u5f02\u5e38\u503c\uff0c\u8b66\u544a\u6d88\u606f\u901a\u5e38\u4f1a\u6d88\u5931\u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5904\u7406 R \u4e2d\u7684\u5176\u4ed6\u8b66\u544a\u548c\u9519\u8bef\uff1a<\/span><\/p>\n
\u5982\u4f55\u4fee\u590d R\uff1aExtractVars \u4e2d\u7684\u6a21\u677f\u516c\u5f0f\u65e0\u6548
 \u5982\u4f55\u5728 R \u4e2d\u4fee\u590d\uff1a\u53c2\u6570\u65e2\u4e0d\u662f\u6570\u5b57\u4e5f\u4e0d\u662f\u903b\u8f91\uff1a\u8fd4\u56de na<\/a>
 \u5982\u4f55\u4fee\u590d\uff1arandomForest.default(m, y, \u2026)\uff1a\u5916\u90e8\u51fd\u6570\u8c03\u7528\u4e2d\u7684 Na\/NaN\/Inf<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u60a8\u5728 R \u4e2d\u53ef\u80fd\u9047\u5230\u7684\u8b66\u544a\u6d88\u606f\u662f\uff1a Warning message: glm.fit: fitted prob […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-2079","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u5904\u7406\uff1aglm.fit\uff1a\u53d1\u751f\u6570\u503c\u8c03\u6574\u6982\u7387 0 \u6216 1 - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5904\u7406 R \u4e2d\u7684\u4ee5\u4e0b\u8b66\u544a\uff1aglm.fit\uff1a\u5df2\u53d1\u751f\u6570\u503c\u8c03\u6574\u6982\u7387 0 \u6216 1\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/cn\/glm-fit-\u62df\u5408\u51fa\u73b0\u6570\u5b57-0-\u6216-1-\u7684\u6982\u7387\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5982\u4f55\u5904\u7406\uff1aglm.fit\uff1a\u53d1\u751f\u6570\u503c\u8c03\u6574\u6982\u7387 0 \u6216 1 - 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