{"id":1275,"date":"2023-07-27T01:33:14","date_gmt":"2023-07-27T01:33:14","guid":{"rendered":"https:\/\/statorials.org\/cn\/r-%e4%b8%ad%e7%9a%84%e6%9d%a0%e6%9d%86\/"},"modified":"2023-07-27T01:33:14","modified_gmt":"2023-07-27T01:33:14","slug":"r-%e4%b8%ad%e7%9a%84%e6%9d%a0%e6%9d%86","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r-%e4%b8%ad%e7%9a%84%e6%9d%a0%e6%9d%86\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u8ba1\u7b97\u6760\u6746\u7edf\u8ba1\u6570\u636e"},"content":{"rendered":"

<\/p>\n

\n

\u5728\u7edf\u8ba1\u5b66\u4e2d\uff0c\u5982\u679c\u67d0\u4e2a\u89c2\u6d4b\u503c<\/a>\u7684\u54cd\u5e94\u53d8\u91cf\u503c\u8fdc\u5927\u4e8e\u6570\u636e\u96c6\u4e2d\u7684\u5176\u4ed6\u89c2\u6d4b\u503c\uff0c\u5219\u8be5\u89c2\u6d4b\u503c\u88ab\u89c6\u4e3a\u5f02\u5e38\u503c<\/strong>\u3002<\/span><\/p>\n

\u540c\u6837\uff0c\u5982\u679c\u67d0\u4e2a\u89c2\u6d4b\u503c\u5177\u6709\u4e00\u4e2a\u6216\u591a\u4e2a\u4e0e\u6570\u636e\u96c6\u4e2d\u5176\u4ed6\u89c2\u6d4b\u503c\u76f8\u6bd4\u66f4\u4e3a\u6781\u7aef\u7684\u9884\u6d4b\u53d8\u91cf\u503c\uff0c\u5219\u8be5\u89c2\u6d4b\u503c\u88ab\u89c6\u4e3a\u9ad8\u6760\u6746<\/strong>\u3002<\/span><\/p>\n

\u4efb\u4f55\u7c7b\u578b\u7684\u5206\u6790\u7684\u7b2c\u4e00\u6b65\u90fd\u662f\u4ed4\u7ec6\u7814\u7a76\u5177\u6709\u9ad8\u5f71\u54cd\u529b\u7684\u89c2\u5bdf\u7ed3\u679c\uff0c\u56e0\u4e3a\u5b83\u4eec\u53ef\u80fd\u5bf9\u7ed9\u5b9a\u6a21\u578b\u7684\u7ed3\u679c\u4ea7\u751f\u5f88\u5927\u5f71\u54cd\u3002<\/span><\/p>\n

\u672c\u6559\u7a0b\u5c55\u793a\u4e86\u5982\u4f55\u8ba1\u7b97\u548c\u53ef\u89c6\u5316 R \u6a21\u578b\u4e2d\u6bcf\u4e2a\u89c2\u5bdf\u7684\u6760\u6746\u7387\u7684\u5206\u6b65\u793a\u4f8b\u3002<\/span><\/p>\n

\u7b2c 1 \u6b65\uff1a\u521b\u5efa\u56de\u5f52\u6a21\u578b<\/strong><\/span><\/h3>\n

\u9996\u5148\uff0c\u6211\u4eec\u5c06\u4f7f\u7528 R \u4e2d\u5185\u7f6e\u7684mtcars<\/strong>\u6570\u636e\u96c6\u521b\u5efa\u4e00\u4e2a\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/a>\uff1a<\/span><\/p>\n

 #load the dataset<\/span>\ndata(mtcars)\n\n#fit a regression model<\/span>\nmodel <- lm(mpg~disp+hp, data=mtcars)\n\n#view model summary\n<\/span>summary(model)\n\nCoefficients:\n             Estimate Std. Error t value Pr(>|t|)    \n(Intercept) 30.735904 1.331566 23.083 < 2nd-16 ***\navailable -0.030346 0.007405 -4.098 0.000306 ***\nhp -0.024840 0.013385 -1.856 0.073679 .  \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.127 on 29 degrees of freedom\nMultiple R-squared: 0.7482, Adjusted R-squared: 0.7309 \nF-statistic: 43.09 on 2 and 29 DF, p-value: 2.062e-09\n<\/strong><\/pre>\n
\u7b2c 2 \u6b65\uff1a\u8ba1\u7b97\u6bcf\u4e2a\u89c2\u5bdf\u7684\u6760\u6746\u7387<\/strong><\/span><\/h3>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528hatvalues()<\/strong>\u51fd\u6570\u6765\u8ba1\u7b97\u6a21\u578b\u4e2d\u6bcf\u4e2a\u89c2\u5bdf\u7684\u6760\u6746\u7387\uff1a<\/span><\/p>\n
 #calculate leverage for each observation in the model\n<\/span>hats <- as<\/span> . data<\/span> . frame<\/span> (hatvalues(model))\n\n#display leverage stats for each observation\n<\/span>hats\n\n                    hatvalues(model)\nMazda RX4 0.04235795\nMazda RX4 Wag 0.04235795\nDatsun 710 0.06287776\nHornet 4 Drive 0.07614472\nHornet Sportabout 0.08097817\nValiant 0.05945972\nDuster 360 0.09828955\nMerc 240D 0.08816960\nMerc 230 0.05102253\nMerc 280 0.03990060\nMerc 280C 0.03990060\nMerc 450SE 0.03890159\nMerc 450SL 0.03890159\nMerc 450SLC 0.03890159\nCadillac Fleetwood 0.19443875\nLincoln Continental 0.16042361\nChrysler Imperial 0.12447530\nFiat 128 0.08346304\nHonda Civic 0.09493784\nToyota Corolla 0.08732818\nToyota Corona 0.05697867\nDodge Challenger 0.06954069\nAMC Javelin 0.05767659\nCamaro Z28 0.10011654\nPontiac Firebird 0.12979822\nFiat X1-9 0.08334018\nPorsche 914-2 0.05785170\nLotus Europa 0.08193899\nFord Pantera L 0.13831817\nFerrari Dino 0.12608583\nMaserati Bora 0.49663919\nVolvo 142E 0.05848459\n<\/strong><\/pre>\n
\u901a\u5e38\uff0c\u6211\u4eec\u4f1a\u4ed4\u7ec6\u7814\u7a76\u6760\u6746\u503c\u5927\u4e8e 2 \u7684\u89c2\u6d4b\u503c\u3002<\/span><\/p>\n
\u6267\u884c\u6b64\u64cd\u4f5c\u7684\u4e00\u4e2a\u7b80\u5355\u65b9\u6cd5\u662f\u6839\u636e\u89c2\u6d4b\u503c\u7684\u6760\u6746\u503c\u6309\u964d\u5e8f\u5bf9\u89c2\u6d4b\u503c\u8fdb\u884c\u6392\u5e8f\uff1a<\/span><\/p>\n
 #sort observations by leverage, descending<\/span>\nhats[ order<\/span> (-hats[' hatvalues(model)<\/span> ']), ]\n\n [1] 0.49663919 0.19443875 0.16042361 0.13831817 0.12979822 0.12608583\n [7] 0.12447530 0.10011654 0.09828955 0.09493784 0.08816960 0.08732818\n[13] 0.08346304 0.08334018 0.08193899 0.08097817 0.07614472 0.06954069\n[19] 0.06287776 0.05945972 0.05848459 0.05785170 0.05767659 0.05697867\n[25] 0.05102253 0.04235795 0.04235795 0.03990060 0.03990060 0.03890159\n[31] 0.03890159 0.03890159\n<\/strong><\/pre>\n
\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u6700\u9ad8\u6760\u6746\u503c\u4e3a0.4966<\/strong> \u3002\u7531\u4e8e\u8fd9\u4e2a\u6570\u5b57\u4e0d\u5927\u4e8e 2\uff0c\u6211\u4eec\u77e5\u9053\u6570\u636e\u96c6\u4e2d\u7684\u6240\u6709\u89c2\u6d4b\u503c\u90fd\u6ca1\u6709\u9ad8\u6760\u6746\u7387\u3002<\/span><\/p>\n
\u7b2c 3 \u6b65\uff1a\u53ef\u89c6\u5316\u6bcf\u4e2a\u89c2\u5bdf\u7ed3\u679c\u7684\u6760\u6746\u4f5c\u7528<\/strong><\/span><\/h3>\n
\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u5feb\u901f\u56fe\u8868\u6765\u53ef\u89c6\u5316\u6bcf\u4e2a\u89c2\u5bdf\u7684\u6760\u6746\u7387\uff1a<\/span> <\/p>\n
 #plot leverage values for each observation<\/span>\nplot(hatvalues(model), type = ' h<\/span> ')\n<\/strong><\/pre>\n
\"R<\/p>\n
 x \u8f74\u663e\u793a\u6570\u636e\u96c6\u4e2d\u6bcf\u4e2a\u89c2\u6d4b\u503c\u7684\u7d22\u5f15\uff0cy \u503c\u663e\u793a\u6bcf\u4e2a\u89c2\u6d4b\u503c\u76f8\u5e94\u7684\u6760\u6746\u7edf\u8ba1\u6570\u636e\u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n
\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u7b80\u5355\u7ebf\u6027\u56de\u5f52<\/a>
\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u591a\u5143\u7ebf\u6027\u56de\u5f52<\/a>
\u5982\u4f55\u5728 R \u4e2d\u521b\u5efa\u6b8b\u5dee\u56fe<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u5728\u7edf\u8ba1\u5b66\u4e2d\uff0c\u5982\u679c\u67d0\u4e2a\u89c2\u6d4b\u503c\u7684\u54cd\u5e94\u53d8\u91cf\u503c\u8fdc\u5927\u4e8e\u6570\u636e\u96c6\u4e2d\u7684\u5176\u4ed6\u89c2\u6d4b\u503c\uff0c\u5219\u8be5\u89c2\u6d4b\u503c\u88ab\u89c6\u4e3a\u5f02\u5e38\u503c\u3002 \u540c\u6837\uff0c\u5982\u679c\u67d0\u4e2a\u89c2\u6d4b […]<\/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-1275","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u5728 R \u4e2d\u8ba1\u7b97\u6760\u6746\u7edf\u8ba1\u6570\u636e<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5728 R \u4e2d\u8ba1\u7b97\u6760\u6746\u7edf\u8ba1\u6570\u636e\uff0c\u5305\u62ec\u5206\u6b65\u793a\u4f8b\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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