{"id":3902,"date":"2023-07-14T20:50:46","date_gmt":"2023-07-14T20:50:46","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%89%e1%85%a5%e1%86%ab%e1%84%92%e1%85%a7%e1%86%bc-%e1%84%80%e1%85%a1%e1%84%89%e1%85%a5%e1%86%af-r\/"},"modified":"2023-07-14T20:50:46","modified_gmt":"2023-07-14T20:50:46","slug":"%e1%84%89%e1%85%a5%e1%86%ab%e1%84%92%e1%85%a7%e1%86%bc-%e1%84%80%e1%85%a1%e1%84%89%e1%85%a5%e1%86%af-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%89%e1%85%a5%e1%86%ab%e1%84%92%e1%85%a7%e1%86%bc-%e1%84%80%e1%85%a1%e1%84%89%e1%85%a5%e1%86%af-r\/","title":{"rendered":"R\uc5d0\uc11c linearhypothesis() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

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\ud2b9\uc815 \ud68c\uadc0 \ubaa8\ub378\uc5d0\uc11c \uc120\ud615 \uac00\uc124\uc744 \ud14c\uc2a4\ud2b8\ud558\ub824\uba74 R\uc758 car<\/strong> \ud328\ud0a4\uc9c0\uc5d0 \uc788\ub294 LinearHypothesis()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \ud568\uc218\ub294 \ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n

 linearHypothesis(fit, c(\" var1=0<\/span> \", \" var2=0<\/span> \"))<\/strong><\/pre>\n
 \uc774 \ud2b9\uc815 \uc608\uc5d0\uc11c\ub294 fit<\/strong> \uc774\ub77c\ub294 \ubaa8\ub378\uc758 \ud68c\uadc0 \uacc4\uc218 var1<\/strong> \uacfc var2\uac00<\/strong> \ud568\uaed8 0\uc778\uc9c0 \uc5ec\ubd80\ub97c \ud14c\uc2a4\ud2b8\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc774 \uae30\ub2a5\uc744 \uc2e4\uc81c\ub85c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608: R\uc5d0\uc11c LinearHypothesis() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95<\/strong><\/span><\/h2>\n
 \uacf5\ubd80\ud55c \uc2dc\uac04, \uce58\ub978 \uc5f0\uc2b5 \uc2dc\ud5d8 \ud69f\uc218, \ud559\uae09 \ub0b4 \ud559\uc0dd 10\uba85\uc758 \ucd5c\uc885 \uc2dc\ud5d8 \uc810\uc218\ub97c \ubcf4\uc5ec\uc8fc\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ub370\uc774\ud130 \ud504\ub808\uc784\uc774 R\uc5d0 \uc788\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/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
 \uc774\uc81c R\uc5d0 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc801\uc6a9\ud55c\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc2dc\ud5d8 \uc810\uc218 = \u03b2 0<\/sub> + \u03b2 1<\/sub> (\uc2dc\uac04) + \u03b2 2<\/sub> (\uc2e4\uae30 \uc2dc\ud5d8)<\/span><\/p>\n
 lm()<\/a> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774 \ubaa8\ub378\uc744 \uc870\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/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
 \uc774\uc81c \uc2dc\uac04<\/strong> \uacc4\uc218\uc640 prac_exams\uac00<\/strong> \ubaa8\ub450 0\uc778\uc9c0 \ud14c\uc2a4\ud2b8\ud55c\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774\ub97c \uc704\ud574 LinearHypothesis()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/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
 \uac00\uc124 \uac80\uc815\uc740 \ub2e4\uc74c \uac12\uc744 \ubc18\ud658\ud569\ub2c8\ub2e4.<\/span><\/p>\n