{"id":505,"date":"2023-07-29T16:45:15","date_gmt":"2023-07-29T16:45:15","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e6%96%b9%e5%b7%ae%e8%86%a8%e8%83%80%e5%9b%a0%e5%ad%90-r\/"},"modified":"2023-07-29T16:45:15","modified_gmt":"2023-07-29T16:45:15","slug":"%e6%96%b9%e5%b7%ae%e8%86%a8%e8%83%80%e5%9b%a0%e5%ad%90-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e6%96%b9%e5%b7%ae%e8%86%a8%e8%83%80%e5%9b%a0%e5%ad%90-r\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u8ba1\u7b97\u65b9\u5dee\u81a8\u80c0\u56e0\u5b50 (vif)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u5f53\u4e24\u4e2a\u6216\u591a\u4e2a\u9884\u6d4b\u53d8\u91cf\u5f7c\u6b64\u9ad8\u5ea6\u76f8\u5173\u65f6\uff0c\u5c31\u4f1a\u51fa\u73b0\u56de\u5f52\u5206\u6790\u4e2d\u7684<a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u5b66\u4ee5\u7b80\u5355\u76f4\u63a5\u7684\u65b9\u5f0f\u89e3\u91ca\u6982\u5ff5\uff0c\u6211\u4eec\u4f7f\u5b66\u4e60\u7edf\u8ba1\u53d8\u5f97\u66f4\u5bb9\u6613\/\" target=\"_blank\" rel=\"noopener\">\u591a\u91cd\u5171\u7ebf\u6027<\/a>\uff0c\u4ee5\u81f4\u5b83\u4eec\u4e0d\u4f1a\u5728\u56de\u5f52\u6a21\u578b\u4e2d\u63d0\u4f9b\u552f\u4e00\u6216\u72ec\u7acb\u7684\u4fe1\u606f\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5982\u679c\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u7a0b\u5ea6\u8db3\u591f\u9ad8\uff0c\u5219\u5728\u62df\u5408\u548c<a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u5b66\u4ee5\u7b80\u5355\u76f4\u63a5\u7684\u65b9\u5f0f\u89e3\u91ca\u6982\u5ff5\uff0c\u6211\u4eec\u4f7f\u5b66\u4e60\u7edf\u8ba1\u53d8\u5f97\u66f4\u5bb9\u6613\/\" target=\"_blank\" rel=\"noopener\">\u89e3\u91ca\u56de\u5f52\u6a21\u578b<\/a>\u65f6\u53ef\u80fd\u4f1a\u51fa\u73b0\u95ee\u9898\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u68c0\u6d4b\u591a\u91cd\u5171\u7ebf\u6027\u7684\u6700\u5e38\u89c1\u65b9\u6cd5\u662f\u4f7f\u7528\u65b9\u5dee\u81a8\u80c0\u56e0\u5b50 (VIF)\uff0c\u5b83\u8861\u91cf\u56de\u5f52\u6a21\u578b\u4e2d\u9884\u6d4b\u53d8\u91cf\u4e4b\u95f4\u7684<a href=\"https:\/\/statorials.org\/cn\/\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570-1\/\" target=\"_blank\" rel=\"noopener\">\u76f8\u5173\u6027<\/a>\u548c\u76f8\u5173\u5f3a\u5ea6\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">VIF \u503c\u4ece 1 \u5f00\u59cb\uff0c\u6ca1\u6709\u4e0a\u9650\u3002\u89e3\u91ca VIF \u7684\u4e00\u822c\u89c4\u5219\u662f\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u503c 1 \u8868\u793a\u7ed9\u5b9a\u9884\u6d4b\u53d8\u91cf\u4e0e\u6a21\u578b\u4e2d\u7684\u4efb\u4f55\u5176\u4ed6\u9884\u6d4b\u53d8\u91cf\u4e4b\u95f4\u4e0d\u5b58\u5728\u76f8\u5173\u6027\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\">1 \u5230 5 \u4e4b\u95f4\u7684\u503c\u8868\u793a\u7ed9\u5b9a\u9884\u6d4b\u53d8\u91cf\u4e0e\u6a21\u578b\u4e2d\u7684\u5176\u4ed6\u9884\u6d4b\u53d8\u91cf\u4e4b\u95f4\u5b58\u5728\u4e2d\u7b49\u76f8\u5173\u6027\uff0c\u4f46\u901a\u5e38\u6ca1\u6709\u4e25\u91cd\u5230\u9700\u8981\u7279\u522b\u6ce8\u610f\u7684\u7a0b\u5ea6\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5927\u4e8e 5 \u7684\u503c\u8868\u793a\u7ed9\u5b9a\u9884\u6d4b\u53d8\u91cf\u4e0e\u6a21\u578b\u4e2d\u7684\u5176\u4ed6\u9884\u6d4b\u53d8\u91cf\u4e4b\u95f4\u5b58\u5728\u6f5c\u5728\u7684\u4e25\u91cd\u76f8\u5173\u6027\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u56de\u5f52\u7ed3\u679c\u4e2d\u7684\u7cfb\u6570\u4f30\u8ba1\u548c p \u503c\u53ef\u80fd\u4e0d\u53ef\u9760\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c<a href=\"https:\/\/statisticalhorizons.com\/multicollinearity\" target=\"_blank\" rel=\"noopener\">\u53ef\u4ee5\u5b89\u5168\u5730\u5ffd\u7565<\/a>\u9ad8 VIF \u503c\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u5982\u4f55\u5728R\u4e2d\u8ba1\u7b97VIF<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4e3a\u4e86\u8bf4\u660e\u5982\u4f55\u8ba1\u7b97 R \u4e2d\u56de\u5f52\u6a21\u578b\u7684 VIF\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u5185\u7f6e\u7684<em>mtcars<\/em>\u6570\u636e\u96c6\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view first six lines of <em>mtcars<\/em><\/span>\nhead(mtcars)\n\n# mpg cyl disp hp drat wt qsec vs am gear carb\n#Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4\n#Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4\n#Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1\n#Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1\n#Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2\n#Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u9996\u5148\uff0c\u6211\u4eec\u5c06\u4f7f\u7528<em>mpg<\/em>\u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf\uff0c\u4f7f\u7528<em>disp<\/em> \u3001 <em>hp<\/em> \u3001 <em>wt<\/em>\u548c<em>drat<\/em>\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\u6765\u62df\u5408\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit the regression model<\/span>\nmodel &lt;- lm(mpg ~ disp + hp + wt + drat, data = mtcars)\n\n<span style=\"color: #008080;\">#view the output of the regression model\n<\/span>summary(model)\n\n#Call:\n#lm(formula = mpg ~ disp + hp + wt + drat, data = mtcars)\n#\n#Residuals:\n# Min 1Q Median 3Q Max \n#-3.5077 -1.9052 -0.5057 0.9821 5.6883 \n#\n#Coefficients:\n#Estimate Std. Error t value Pr(&gt;|t|)    \n#(Intercept) 29.148738 6.293588 4.631 8.2e-05 ***\n#available 0.003815 0.010805 0.353 0.72675    \n#hp -0.034784 0.011597 -2.999 0.00576 ** \n#wt -3.479668 1.078371 -3.227 0.00327 ** \n#drat 1.768049 1.319779 1.340 0.19153    \n#---\n#Significant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n#\n#Residual standard error: 2.602 on 27 degrees of freedom\n#Multiple R-squared: 0.8376, Adjusted R-squared: 0.8136 \n#F-statistic: 34.82 on 4 and 27 DF, p-value: 2.704e-10\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u6a21\u578b\u7684<a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u5b66\u4ee5\u7b80\u5355\u76f4\u63a5\u7684\u65b9\u5f0f\u89e3\u91ca\u6982\u5ff5\uff0c\u6211\u4eec\u4f7f\u5b66\u4e60\u7edf\u8ba1\u53d8\u5f97\u66f4\u5bb9\u6613\/\" target=\"_blank\" rel=\"noopener\">R\u5e73\u65b9\u503c\u4e3a<\/a><strong>0.8376<\/strong> \u3002\u6211\u4eec\u8fd8\u53ef\u4ee5\u770b\u5230\uff0c <a href=\"https:\/\/statorials.org\/cn\/\u4e86\u89e3\u56de\u5f52\u4e2d\u603b\u4f53\u663e\u7740\u6027\u7684-f-\u68c0\u9a8c\u7684\u7b80\u5355\u6307\u5357\/\" target=\"_blank\" rel=\"noopener\">\u603b\u4f53 F \u7edf\u8ba1\u91cf<\/a>\u4e3a<strong>34.82<\/strong> \uff0c\u76f8\u5e94\u7684 p \u503c\u4e3a<strong>2.704e-10<\/strong> \uff0c\u8fd9\u8868\u660e\u603b\u4f53\u56de\u5f52\u6a21\u578b\u663e\u7740\u3002\u6b64\u5916\uff0c<\/span><span style=\"color: #000000;\">\u9884\u6d4b\u53d8\u91cf<em>hp<\/em>\u548c<em>wt<\/em>\u5728 0.05 \u663e\u7740\u6027\u6c34\u5e73\u4e0a\u5177\u6709\u7edf\u8ba1\u663e\u7740\u6027\uff0c\u800c<em>disp<\/em>\u548c<em>drat<\/em>\u5219\u4e0d\u7136\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u5e93\u7684<strong>vive()<\/strong>\u51fd\u6570<strong>\u8ba1\u7b97<\/strong>\u6a21\u578b\u4e2d\u6bcf\u4e2a\u9884\u6d4b\u53d8\u91cf\u7684 VIF\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load the <em>car<\/em> library<\/span>\nlibrary(car)\n\n<span style=\"color: #008080;\">#calculate the VIF for each predictor variable in the model\n<\/span>lively(model)\n\n# disp hp wt drat \n#8.209402 2.894373 5.096601 2.279547 \n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u770b\u5230<em>disp<\/em>\u548c<em>wt<\/em>\u7684 VIF \u5927\u4e8e 5\uff0c\u8fd9\u53ef\u80fd\u4ee4\u4eba\u62c5\u5fe7\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u67e5\u770b VIF \u503c<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4e3a\u4e86\u53ef\u89c6\u5316\u6bcf\u4e2a\u9884\u6d4b\u53d8\u91cf\u7684 VIF \u503c\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6c34\u5e73\u6761\u5f62\u56fe\uff0c\u5e76\u5728 5 \u5904\u6dfb\u52a0\u4e00\u6761\u5782\u76f4\u7ebf\uff0c\u4ee5\u4fbf\u6211\u4eec\u53ef\u4ee5\u6e05\u695a\u5730\u770b\u5230\u54ea\u4e9b VIF \u503c\u8d85\u8fc7 5\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create vector of VIF values<\/span>\nvive_values &lt;- vive(model)\n\n<span style=\"color: #008080;\">#create horizontal bar chart to display each VIF value\n<\/span>barplot(vif_values, main = \"VIF Values\", horiz = TRUE, col = \"steelblue\")\n\n<span style=\"color: #008080;\">#add vertical line at 5\n<\/span>abline(v = 5, lwd = 3, lty = 2)<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u8fd9\u79cd\u7c7b\u578b\u7684\u56fe\u8868\u5bf9\u4e8e\u5177\u6709\u8bb8\u591a\u9884\u6d4b\u53d8\u91cf\u7684\u6a21\u578b\u6700\u6709\u7528\uff0c\u56e0\u6b64\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u4e00\u6b21\u67e5\u770b\u6240\u6709 VIF \u503c\u3002\u7136\u800c\uff0c\u5728\u672c\u4f8b\u4e2d\u5b83\u662f\u4e00\u4e2a\u6709\u7528\u7684\u56fe\u8868\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6839\u636e\u60a8\u8ba4\u4e3a\u54ea\u4e2a VIF \u503c\u592a\u9ad8\u800c\u65e0\u6cd5\u5305\u542b\u5728\u6a21\u578b\u4e2d\uff0c\u60a8\u53ef\u4ee5\u9009\u62e9\u5220\u9664\u67d0\u4e9b\u9884\u6d4b\u53d8\u91cf\uff0c\u5e76\u67e5\u770b\u6a21\u578b\u7684\u76f8\u5e94<a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u5b66\u4ee5\u7b80\u5355\u76f4\u63a5\u7684\u65b9\u5f0f\u89e3\u91ca\u6982\u5ff5\uff0c\u6211\u4eec\u4f7f\u5b66\u4e60\u7edf\u8ba1\u53d8\u5f97\u66f4\u5bb9\u6613\/\" target=\"_blank\" rel=\"noopener\">R \u5e73\u65b9\u503c<\/a>\u6216<a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u5b66\u4ee5\u7b80\u5355\u76f4\u63a5\u7684\u65b9\u5f0f\u89e3\u91ca\u6982\u5ff5\uff0c\u6211\u4eec\u4f7f\u5b66\u4e60\u7edf\u8ba1\u53d8\u5f97\u66f4\u5bb9\u6613\/\" target=\"_blank\" rel=\"noopener\">\u6807\u51c6\u8bef\u5dee<\/a>\u662f\u5426\u53d7\u5230\u5f71\u54cd\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u53ef\u89c6\u5316\u9884\u6d4b\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u6027<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u4e3a\u4ec0\u4e48\u9884\u6d4b\u53d8\u91cf\u53ef\u80fd\u5177\u6709\u9ad8 VIF \u503c\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u76f8\u5173\u77e9\u9635\u6765\u663e\u793a\u6bcf\u5bf9\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u76f8\u5173\u7cfb\u6570\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define the variables we want to include in the correlation matrix<\/span>\ndata &lt;- mtcars[, c(\"disp\", \"hp\", \"wt\", \"drat\")]\n\n<span style=\"color: #008080;\">#create correlation matrix<\/span>\ncor(data)\n\n# disp hp wt drat\n#available 1.0000000 0.7909486 0.8879799 -0.7102139\n#hp 0.7909486 1.0000000 0.6587479 -0.4487591\n#wt 0.8879799 0.6587479 1.0000000 -0.7124406\n#drat -0.7102139 -0.4487591 -0.7124406 1.0000000\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u56de\u60f3\u4e00\u4e0b\uff0c <em>disp<\/em>\u53d8\u91cf\u7684 VIF \u503c\u5927\u4e8e 8\uff0c\u8fd9\u662f\u6a21\u578b\u4e2d\u6240\u6709\u9884\u6d4b\u53d8\u91cf\u4e2d\u6700\u9ad8\u7684 VIF \u503c\u3002\u4ece\u76f8\u5173\u77e9\u9635\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230<em>disp<\/em>\u4e0e\u5176\u4ed6\u4e09\u4e2a\u9884\u6d4b\u53d8\u91cf\u9ad8\u5ea6\u76f8\u5173\uff0c\u8fd9\u89e3\u91ca\u4e86\u4e3a\u4ec0\u4e48\u5b83\u7684 VIF \u503c\u5982\u6b64\u4e4b\u9ad8\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u60a8\u53ef\u80fd\u9700\u8981\u4ece\u6a21\u578b\u4e2d\u5220\u9664<em>disp<\/em> \uff0c\u56e0\u4e3a\u5b83\u7684 VIF \u503c\u5f88\u9ad8<em>\uff0c\u5e76\u4e14<\/em>\u5728 0.05 \u663e\u7740\u6027\u6c34\u5e73\u4e0b\u4e0d\u5177\u6709\u7edf\u8ba1\u663e\u7740\u6027\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u76f8\u5173\u77e9\u9635\u548c VIF \u5c06\u4e3a\u60a8\u63d0\u4f9b\u7c7b\u4f3c\u7684\u4fe1\u606f\uff1a\u5b83\u4eec\u90fd\u4f1a\u544a\u8bc9\u60a8\u67d0\u4e2a\u53d8\u91cf\u4f55\u65f6\u4e0e\u56de\u5f52\u6a21\u578b\u4e2d\u7684\u4e00\u4e2a\u6216\u591a\u4e2a\u5176\u4ed6\u53d8\u91cf\u9ad8\u5ea6\u76f8\u5173\u3002<\/span><\/p>\n<p><strong><span style=\"color: #000000;\">\u8fdb\u4e00\u6b65\u9605\u8bfb\uff1a<\/span><\/strong><br \/> <strong><a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u5b66\u4ee5\u7b80\u5355\u76f4\u63a5\u7684\u65b9\u5f0f\u89e3\u91ca\u6982\u5ff5\uff0c\u6211\u4eec\u4f7f\u5b66\u4e60\u7edf\u8ba1\u53d8\u5f97\u66f4\u5bb9\u6613\/\" target=\"_blank\" rel=\"noopener\">\u56de\u5f52\u4e2d\u7684\u591a\u91cd\u5171\u7ebf\u6027\u548c VIF \u6307\u5357<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u5b66\u4ee5\u7b80\u5355\u76f4\u63a5\u7684\u65b9\u5f0f\u89e3\u91ca\u6982\u5ff5\uff0c\u6211\u4eec\u4f7f\u5b66\u4e60\u7edf\u8ba1\u53d8\u5f97\u66f4\u5bb9\u6613\/\" target=\"_blank\" 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