{"id":3900,"date":"2023-07-14T20:50:46","date_gmt":"2023-07-14T20:50:46","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e7%b7%9a%e5%bd%a2%e4%bb%ae%e8%aa%acr\/"},"modified":"2023-07-14T20:50:46","modified_gmt":"2023-07-14T20:50:46","slug":"%e7%b7%9a%e5%bd%a2%e4%bb%ae%e8%aa%acr","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e7%b7%9a%e5%bd%a2%e4%bb%ae%e8%aa%acr\/","title":{"rendered":"R \u3067 linearhypothesis() \u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

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R \u306ecar<\/strong>\u30d1\u30c3\u30b1\u30fc\u30b8\u306eLinearHypothesis()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u7279\u5b9a\u306e\u56de\u5e30\u30e2\u30c7\u30eb\u3067\u7dda\u5f62\u4eee\u8aac\u3092\u30c6\u30b9\u30c8\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u95a2\u6570\u306f\u6b21\u306e\u57fa\u672c\u69cb\u6587\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n

 linearHypothesis(fit, c(\" var1=0<\/span> \", \" var2=0<\/span> \"))<\/strong><\/pre>\n
\u3053\u306e\u7279\u5b9a\u306e\u4f8b\u3067\u306f\u3001 fit<\/strong>\u3068\u547c\u3070\u308c\u308b\u30e2\u30c7\u30eb\u306e\u56de\u5e30\u4fc2\u6570var1<\/strong>\u3068var2<\/strong>\u304c\u5408\u308f\u305b\u3066 0 \u306b\u7b49\u3057\u3044\u304b\u3069\u3046\u304b\u3092\u30c6\u30b9\u30c8\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u95a2\u6570\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
\u4f8b: R \u3067 LinearHypothesis() \u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5<\/strong><\/span><\/h2>\n
R \u306b\u3001\u30af\u30e9\u30b9\u306e 10 \u4eba\u306e\u751f\u5f92\u306e\u5b66\u7fd2\u6642\u9593\u6570\u3001\u53d7\u9a13\u3057\u305f\u6a21\u64ec\u8a66\u9a13\u306e\u6570\u3001\u304a\u3088\u3073\u6700\u7d42\u8a66\u9a13\u306e\u5f97\u70b9\u3092\u793a\u3059\u6b21\u306e\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/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
\u3053\u3053\u3067\u3001\u6b21\u306e\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092 R \u306b\u5f53\u3066\u306f\u3081\u305f\u3044\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u8a66\u9a13\u306e\u30b9\u30b3\u30a2 = \u03b2 0<\/sub> + \u03b2 1<\/sub> (\u6642\u9593\u6570) + \u03b2 2<\/sub> (\u5b9f\u6280\u8a66\u9a13)<\/span><\/p>\n
 lm()<\/a>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u3053\u306e\u30e2\u30c7\u30eb\u3092\u9069\u5fdc\u3055\u305b\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/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
\u3053\u3053\u3067\u3001\u6642\u9593<\/strong>\u4fc2\u6570\u3068prac_exams \u304c<\/strong>\u4e21\u65b9\u3068\u3082 0 \u3067\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u30c6\u30b9\u30c8\u3057\u305f\u3044\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u308c\u3092\u884c\u3046\u306b\u306f\u3001 LinearHypothesis()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002<\/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
\u4eee\u8aac\u691c\u5b9a\u306f\u6b21\u306e\u5024\u3092\u8fd4\u3057\u307e\u3059\u3002<\/span><\/p>\n