{"id":3905,"date":"2023-07-14T20:50:46","date_gmt":"2023-07-14T20:50:46","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e7%ba%bf%e6%80%a7%e5%81%87%e8%ae%be-r\/"},"modified":"2023-07-14T20:50:46","modified_gmt":"2023-07-14T20:50:46","slug":"%e7%ba%bf%e6%80%a7%e5%81%87%e8%ae%be-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e7%ba%bf%e6%80%a7%e5%81%87%e8%ae%be-r\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u4f7f\u7528 linearhypothesis() \u51fd\u6570"},"content":{"rendered":"

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\u60a8\u53ef\u4ee5\u4f7f\u7528 R \u4e2d\u7684car<\/strong>\u5305\u4e2d\u7684LinearHypothesis()<\/strong>\u51fd\u6570\u6765\u6d4b\u8bd5\u7279\u5b9a\u56de\u5f52\u6a21\u578b\u4e2d\u7684\u7ebf\u6027\u5047\u8bbe\u3002<\/span><\/p>\n

\u8be5\u51fd\u6570\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\uff1a<\/span><\/p>\n

 linearHypothesis(fit, c(\" var1=0<\/span> \", \" var2=0<\/span> \"))<\/strong><\/pre>\n
\u6b64\u7279\u5b9a\u793a\u4f8b\u6d4b\u8bd5\u540d\u4e3afit<\/strong>\u7684\u6a21\u578b\u4e2d\u7684\u56de\u5f52\u7cfb\u6570var1<\/strong>\u548cvar2<\/strong>\u662f\u5426\u5171\u540c\u7b49\u4e8e 0\u3002<\/span><\/p>\n
\u4e0b\u9762\u7684\u4f8b\u5b50\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u9645\u4e2d\u4f7f\u7528\u8fd9\u4e2a\u529f\u80fd\u3002<\/span><\/p>\n
\u793a\u4f8b\uff1a\u5982\u4f55\u5728 R \u4e2d\u4f7f\u7528 LinearHypothesis() \u51fd\u6570<\/strong><\/span><\/h2>\n
\u5047\u8bbe\u6211\u4eec\u5728 R \u4e2d\u6709\u4ee5\u4e0b\u6570\u636e\u6846\uff0c\u663e\u793a\u4e86\u73ed\u7ea7 10 \u540d\u5b66\u751f\u7684\u5b66\u4e60\u5c0f\u65f6\u6570\u3001\u53c2\u52a0\u7684\u6a21\u62df\u8003\u8bd5\u6b21\u6570\u4ee5\u53ca\u671f\u672b\u8003\u8bd5\u6210\u7ee9\uff1a<\/span><\/p>\n
 #create data frame\n<\/span>df <- data.frame(score=c(77, 79, 84, 85, 88, 99, 95, 90, 92, 94),\n                 hours=c(1, 1, 2, 3, 2, 4, 4, 2, 3, 3),\n                 prac_exams=c(2, 4, 4, 2, 4, 5, 4, 3, 2, 1))\n\n#view data frame\n<\/span>df\n\n   score hours prac_exams\n1 77 1 2\n2 79 1 4\n3 84 2 4\n4 85 3 2\n5 88 2 4\n6 99 4 5\n7 95 4 4\n8 90 2 3\n9 92 3 2\n10 94 3 1\n<\/strong><\/pre>\n
\u73b0\u5728\u5047\u8bbe\u6211\u4eec\u60f3\u8981\u5728 R \u4e2d\u62df\u5408\u4ee5\u4e0b\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n
\u8003\u8bd5\u6210\u7ee9 = \u03b2 0<\/sub> + \u03b2 1<\/sub> \uff08\u5c0f\u65f6\uff09+ \u03b2 2<\/sub> \uff08\u5b9e\u8df5\u8003\u8bd5\uff09<\/span><\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528lm()<\/a>\u51fd\u6570\u6765\u9002\u5e94\u8fd9\u4e2a\u6a21\u578b\uff1a<\/span><\/p>\n
 #fit multiple linear regression model\n<\/span>fit <- lm(score ~ hours + prac_exams, data=df)\n\n#view summary of model\n<\/span>summary(fit)\n\nCall:\nlm(formula = score ~ hours + prac_exams, data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-5.8366 -2.0875 0.1381 2.0652 4.6381 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)    \n(Intercept) 72.7393 3.9455 18.436 3.42e-07 ***\nhours 5.8093 1.1161 5.205 0.00125 ** \nprac_exams 0.3346 0.9369 0.357 0.73150    \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.59 on 7 degrees of freedom\nMultiple R-squared: 0.8004, Adjusted R-squared: 0.7434 \nF-statistic: 14.03 on 2 and 7 DF, p-value: 0.003553\n<\/strong><\/pre>\n
\u73b0\u5728\u5047\u8bbe\u6211\u4eec\u8981\u6d4b\u8bd5\u5c0f\u65f6<\/strong>\u7cfb\u6570\u548cprac_exams<\/strong>\u662f\u5426\u90fd\u4e3a\u96f6\u3002<\/span><\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528LinearHypothesis()<\/strong>\u51fd\u6570\u6765\u505a\u5230\u8fd9\u4e00\u70b9\uff1a<\/span><\/p>\n
 library<\/span> (car)\n\n#perform hypothesis test for hours=0 and prac_exams=0\n<\/span>linearHypothesis(fit, c(\" hours=0<\/span> \", \" prac_exams=0<\/span> \"))\n\nLinear hypothesis testing\n\nHypothesis:\nhours = 0\nprac_exams = 0\n\nModel 1: restricted model\nModel 2: score ~ hours + prac_exams\n\n  Res.Df RSS Df Sum of Sq F Pr(>F)   \n1 9 452.10                                \n2 7 90.24 2 361.86 14.035 0.003553 **\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n<\/strong><\/pre>\n
\u5047\u8bbe\u68c0\u9a8c\u8fd4\u56de\u4ee5\u4e0b\u503c\uff1a<\/span><\/p>\n