{"id":3663,"date":"2023-07-16T06:46:06","date_gmt":"2023-07-16T06:46:06","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e4%bb%8e-r-%e4%b8%ad%e7%9a%84-lm-%e4%b8%ad%e6%8f%90%e5%8f%96-rmse\/"},"modified":"2023-07-16T06:46:06","modified_gmt":"2023-07-16T06:46:06","slug":"%e4%bb%8e-r-%e4%b8%ad%e7%9a%84-lm-%e4%b8%ad%e6%8f%90%e5%8f%96-rmse","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e4%bb%8e-r-%e4%b8%ad%e7%9a%84-lm-%e4%b8%ad%e6%8f%90%e5%8f%96-rmse\/","title":{"rendered":"\u5982\u4f55\u4ece r \u4e2d\u7684 lm() \u51fd\u6570\u4e2d\u63d0\u53d6 rmse"},"content":{"rendered":"

<\/p>\n

\n

\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u63d0\u53d6 R \u4e2dlm()<\/a>\u51fd\u6570\u7684\u5747\u65b9\u6839\u8bef\u5dee (RMSE)<\/strong> \uff1a<\/span><\/p>\n

 sqrt(mean(model$residuals^2))\n<\/strong><\/pre>\n
\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u4f7f\u7528\u6b64\u8bed\u6cd5\u3002<\/span><\/p>\n
\u76f8\u5173\uff1a<\/strong>\u5982\u4f55\u89e3\u91ca\u5747\u65b9\u6839\u8bef\u5dee (RMSE)<\/a><\/span><\/p>\n
\u793a\u4f8b\uff1a\u4ece R \u4e2d\u7684 lm() \u4e2d\u63d0\u53d6 RMSE<\/strong><\/span><\/h2>\n
\u5047\u8bbe\u6211\u4eec\u5728 R \u4e2d\u62df\u5408\u4ee5\u4e0b\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n
 #create data frame\n<\/span>df <- data. frame<\/span> (rating=c(67, 75, 79, 85, 90, 96, 97),\n                 points=c(8, 12, 16, 15, 22, 28, 24),\n                 assists=c(4, 6, 6, 5, 3, 8, 7),\n                 rebounds=c(1, 4, 3, 3, 2, 6, 7))\n\n#fit multiple linear regression model\n<\/span>model <- lm(rating ~ points + assists + rebounds, data=df)\n<\/strong><\/pre>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528summary()<\/strong>\u51fd\u6570\u6765\u663e\u793a\u56de\u5f52\u6a21\u578b\u7684\u5b8c\u6574\u6458\u8981\uff1a<\/span><\/p>\n
 #view model summary\n<\/span>summary(model)\n\nCall:\nlm(formula = rating ~ points + assists + rebounds, data = df)\n\nResiduals:\n      1 2 3 4 5 6 7 \n-1.5902 -1.7181 0.2413 4.8597 -1.0201 -0.6082 -0.1644 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)   \n(Intercept) 66.4355 6.6932 9.926 0.00218 **\npoints 1.2152 0.2788 4.359 0.02232 * \nassists -2.5968 1.6263 -1.597 0.20860   \nrebounds 2.8202 1.6118 1.750 0.17847   \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.193 on 3 degrees of freedom\nMultiple R-squared: 0.9589, Adjusted R-squared: 0.9179 \nF-statistic: 23.35 on 3 and 3 DF, p-value: 0.01396\n<\/strong><\/pre>\n
\u8981\u4ec5\u63d0\u53d6\u6a21\u578b\u7684\u5747\u65b9\u6839\u8bef\u5dee (RMSE)\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\uff1a<\/span><\/p>\n
 #extract RMSE of regression model\nsqrt(mean(model$residuals^2))\n\n[1] 2.090564\n<\/span><\/span><\/strong><\/pre>\n
\u8be5\u6a21\u578b\u7684 RMSE \u4e3a2.090564<\/strong> \u3002<\/span><\/p>\n
\u8fd9\u8868\u793a\u6a21\u578b\u7684\u9884\u6d4b\u503c\u4e0e\u6570\u636e\u96c6\u7684\u5b9e\u9645\u503c\u4e4b\u95f4\u7684\u5e73\u5747\u8ddd\u79bb\u3002<\/span><\/p>\n
\u8bf7\u6ce8\u610f\uff0cRMSE \u8d8a\u4f4e\uff0c\u7ed9\u5b9a\u6a21\u578b\u5c31\u8d8a\u80fd\u201c\u62df\u5408\u201d\u6570\u636e\u96c6\u3002<\/span><\/p>\n
\u5728\u6bd4\u8f83\u51e0\u79cd\u4e0d\u540c\u7684\u56de\u5f52\u6a21\u578b\u65f6\uff0c\u5177\u6709\u6700\u4f4e RMSE \u7684\u6a21\u578b\u88ab\u8ba4\u4e3a\u662f\u6700\u201c\u9002\u5408\u201d\u6570\u636e\u96c6\u7684\u6a21\u578b\u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h2>\n
\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u4efb\u52a1\uff1a<\/span><\/p>\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":"
\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u63d0\u53d6 R \u4e2dlm()\u51fd\u6570\u7684\u5747\u65b9\u6839\u8bef\u5dee (RMSE) \uff1a sqrt(mean(model$re […]<\/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-3663","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u4ece R \u4e2d\u7684 lm() \u51fd\u6570\u4e2d\u63d0\u53d6 RMSE - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u793a\u4f8b\u8bf4\u660e\u5982\u4f55\u4ece R \u56de\u5f52\u6a21\u578b\u7684 lm() \u51fd\u6570\u4e2d\u63d0\u53d6 RMSE \u503c\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\/\u4ece-r-\u4e2d\u7684-lm-\u4e2d\u63d0\u53d6-rmse\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5982\u4f55\u4ece R \u4e2d\u7684 lm() \u51fd\u6570\u4e2d\u63d0\u53d6 RMSE - 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