{"id":2140,"date":"2023-07-23T12:53:51","date_gmt":"2023-07-23T12:53:51","guid":{"rendered":"https:\/\/statorials.org\/cn\/r-%e7%94%b1%e4%ba%8e%e5%a5%87%e7%82%b9%e8%80%8c%e6%9c%aa%e5%ae%9a%e4%b9%89\/"},"modified":"2023-07-23T12:53:51","modified_gmt":"2023-07-23T12:53:51","slug":"r-%e7%94%b1%e4%ba%8e%e5%a5%87%e7%82%b9%e8%80%8c%e6%9c%aa%e5%ae%9a%e4%b9%89","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r-%e7%94%b1%e4%ba%8e%e5%a5%87%e7%82%b9%e8%80%8c%e6%9c%aa%e5%ae%9a%e4%b9%89\/","title":{"rendered":"\u5982\u4f55\u4fee\u590d r: \u7531\u4e8e\u5947\u70b9\u800c\u672a\u5b9a\u4e49"},"content":{"rendered":"

<\/p>\n

\n

\u60a8\u5728 R \u4e2d\u53ef\u80fd\u9047\u5230\u7684\u9519\u8bef\u6d88\u606f\u662f\uff1a<\/span><\/p>\n

 Coefficients: (1 not defined because of singularities) \n<\/strong><\/pre>\n
\u5f53\u60a8\u4f7f\u7528 R \u4e2d\u7684glm()<\/strong>\u51fd\u6570\u62df\u5408\u6a21\u578b\u5e76\u4e14\u4e24\u4e2a\u6216\u591a\u4e2a\u9884\u6d4b\u53d8\u91cf\u5f7c\u6b64\u5177\u6709\u7cbe\u786e\u7684\u7ebf\u6027\u5173\u7cfb\uff08\u79f0\u4e3a\u5b8c\u7f8e\u591a\u91cd\u5171\u7ebf\u6027\uff09<\/a>\u65f6\uff0c\u4f1a\u51fa\u73b0\u6b64\u9519\u8bef\u6d88\u606f\u3002<\/span><\/p>\n
\u8981\u4fee\u590d\u6b64\u9519\u8bef\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528cor()<\/strong>\u51fd\u6570\u6765\u8bc6\u522b\u6570\u636e\u96c6\u4e2d\u76f8\u4e92\u5177\u6709\u5b8c\u7f8e\u76f8\u5173\u6027\u7684\u53d8\u91cf\uff0c\u7136\u540e\u53ea\u9700\u4ece\u56de\u5f52\u6a21\u578b\u4e2d\u5220\u9664\u8fd9\u4e9b\u53d8\u91cf\u4e4b\u4e00\u5373\u53ef\u3002<\/span><\/span><\/p>\n
\u672c\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u5904\u7406\u6b64\u9519\u8bef\u6d88\u606f\u3002<\/span><\/p>\n
\u5982\u4f55\u91cd\u73b0\u9519\u8bef<\/strong><\/h3>\n
\u5047\u8bbe\u6211\u4eec\u5c06\u903b\u8f91\u56de\u5f52\u6a21\u578b<\/a>\u62df\u5408\u5230 R \u4e2d\u7684\u4ee5\u4e0b\u6570\u636e\u6846\uff1a<\/span><\/p>\n
 #define data\n<\/span>df <- 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(6, 6, 8, 8, 6, 4, 10, 16, 18, 18, 18, 16, 18, 18, 18),\n                 x3 = c(4, 7, 7, 3, 8, 9, 9, 8, 7, 8, 9, 4, 9, 10, 13))\n\n#fit logistic regression model\n<\/span>model <- glm(y~x1+x2+x3, data=df, family=binomial)\n\n#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = y ~ x1 + x2 + x3, family = binomial, data = df)\n\nDeviance Residuals: \n       Min 1Q Median 3Q Max  \n-1.372e-05 -2.110e-08 2.110e-08 2.110e-08 1.575e-05  \n\nCoefficients: (1 not defined because of singularities)\n              Estimate Std. Error z value Pr(>|z|)\n(Intercept) -75.496 176487.031 0.000 1\nx1 14.546 24314.459 0.001 1\nx2 NA NA NA NA\nx3 -2.258 20119.863 0.000 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: 5.1523e-10 on 12 degrees of freedom\nAIC: 6\n\nNumber of Fisher Scoring iterations: 24\n<\/strong><\/span><\/pre>\n
\u8bf7\u6ce8\u610f\uff0c\u5728\u7cfb\u6570\u8f93\u51fa\u4e4b\u524d\uff0c\u6211\u4eec\u6536\u5230\u6d88\u606f\uff1a<\/span><\/p>\n
 Coefficients: (1 not defined because of singularities)\n<\/strong><\/span><\/pre>\n
\u8fd9\u8868\u660e\u6a21\u578b\u4e2d\u7684\u4e24\u4e2a\u6216\u591a\u4e2a\u9884\u6d4b\u53d8\u91cf\u5177\u6709\u5b8c\u7f8e\u7684\u7ebf\u6027\u5173\u7cfb\uff0c\u56e0\u6b64\u5e76\u975e\u6a21\u578b\u4e2d\u7684\u6240\u6709\u56de\u5f52\u7cfb\u6570\u90fd\u53ef\u4ee5\u4f30\u8ba1\u3002<\/span><\/p>\n
\u4f8b\u5982\uff0c\u8bf7\u6ce8\u610f\uff0c\u65e0\u6cd5\u5bf9\u9884\u6d4b\u53d8\u91cfx 2<\/sub><\/strong>\u8fdb\u884c\u7cfb\u6570\u4f30\u8ba1\u3002<\/span><\/p>\n
\u5982\u4f55\u5904\u7406\u9519\u8bef<\/strong><\/span><\/h3>\n
\u4e3a\u4e86\u786e\u5b9a\u54ea\u4e9b\u9884\u6d4b\u53d8\u91cf\u5bfc\u81f4\u6b64\u9519\u8bef\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528cor()<\/strong>\u51fd\u6570\u751f\u6210\u76f8\u5173\u77e9\u9635<\/a>\u5e76\u68c0\u67e5\u54ea\u4e9b\u53d8\u91cf\u5f7c\u6b64\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u6070\u597d\u4e3a1<\/strong> \uff1a<\/span><\/p>\n
 #create correlation matrix<\/span>\ncor(df)\n\n           y x1 x2 x3\ny 1.0000000 0.9675325 0.9675325 0.3610320\nx1 0.9675325 1.0000000 1.0000000 0.3872889\nx2 0.9675325 1.0000000 1.0000000 0.3872889\nx3 0.3610320 0.3872889 0.3872889 1.0000000\n<\/strong><\/span><\/pre>\n
\u4ece\u76f8\u5173\u77e9\u9635\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u53d8\u91cfx 1<\/sub><\/strong>\u548cx 2<\/sub><\/strong>\u5b8c\u5168\u76f8\u5173\u3002<\/span><\/p>\n
\u8981\u89e3\u51b3\u6b64\u9519\u8bef\uff0c\u6211\u4eec\u53ef\u4ee5\u7b80\u5355\u5730\u4ece\u6a21\u578b\u4e2d\u5220\u9664\u8fd9\u4e24\u4e2a\u53d8\u91cf\u4e4b\u4e00\uff0c\u56e0\u4e3a\u5b83\u4eec\u5b9e\u9645\u4e0a\u5e76\u4e0d\u5728\u56de\u5f52\u6a21\u578b\u4e2d\u63d0\u4f9b\u552f\u4e00\u6216\u72ec\u7acb\u7684\u4fe1\u606f\u3002<\/span><\/p>\n
\u4f8b\u5982\uff0c\u5047\u8bbe\u6211\u4eec\u5220\u9664x 2<\/sub><\/strong>\u5e76\u62df\u5408\u4ee5\u4e0b\u903b\u8f91\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n
 #fit logistic regression model\n<\/span>model <- glm(y~x1+x3, data=df, family=binomial)\n\n#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = y ~ x1 + x3, family = binomial, data = df)\n\nDeviance Residuals: \n       Min 1Q Median 3Q Max  \n-1.372e-05 -2.110e-08 2.110e-08 2.110e-08 1.575e-05  \n\nCoefficients:\n              Estimate Std. Error z value Pr(>|z|)\n(Intercept) -75.496 176487.031 0.000 1\nx1 14.546 24314.459 0.001 1\nx3 -2.258 20119.863 0.000 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: 5.1523e-10 on 12 degrees of freedom\nAIC: 6\n\nNumber of Fisher Scoring iterations: 24<\/strong><\/span><\/pre>\n
\u8bf7\u6ce8\u610f\uff0c\u8fd9\u6b21\u6211\u4eec\u6ca1\u6709\u6536\u5230\u201c\u7531\u4e8e\u5947\u70b9\u800c\u672a\u5b9a\u4e49\u201d\u9519\u8bef\u6d88\u606f\u3002<\/span><\/p>\n
\u6ce8\u610f<\/strong>\uff1a\u5220\u9664 x 1<\/sub>\u6216 x 2<\/sub>\u5e76\u4e0d\u91cd\u8981\u3002\u6700\u7ec8\u6a21\u578b\u5c06\u5305\u542b\u60a8\u51b3\u5b9a\u4fdd\u7559\u7684\u53d8\u91cf\u7684\u76f8\u540c\u7cfb\u6570\u4f30\u8ba1\uff0c\u5e76\u4e14\u6a21\u578b\u7684\u6574\u4f53\u62df\u5408\u4f18\u5ea6\u5c06\u76f8\u540c\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\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\u9519\u8bef\u6d88\u606f\u662f\uff1a Coefficients: (1 not defined because  […]<\/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-2140","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u4fee\u590d R \u4e2d\u7684\u95ee\u9898\uff1a\u7531\u4e8e\u5947\u70b9\u800c\u672a\u5b9a\u4e49 - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u7ea0\u6b63 R \u4e2d\u7684\u4ee5\u4e0b\u9519\u8bef\uff1a\u7cfb\u6570\uff1a\uff081 \u7531\u4e8e\u5947\u70b9\u800c\u672a\u5b9a\u4e49\uff09\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\/r-\u7531\u4e8e\u5947\u70b9\u800c\u672a\u5b9a\u4e49\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5982\u4f55\u4fee\u590d R \u4e2d\u7684\u95ee\u9898\uff1a\u7531\u4e8e\u5947\u70b9\u800c\u672a\u5b9a\u4e49 - 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