{"id":1545,"date":"2023-07-25T22:47:19","date_gmt":"2023-07-25T22:47:19","guid":{"rendered":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-glm-%e1%84%83%e1%85%a2-lm\/"},"modified":"2023-07-25T22:47:19","modified_gmt":"2023-07-25T22:47:19","slug":"r%e1%84%8b%e1%85%b4-glm-%e1%84%83%e1%85%a2-lm","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-glm-%e1%84%83%e1%85%a2-lm\/","title":{"rendered":"R\uc5d0\uc11c glm\uacfc lm\uc758 \ucc28\uc774\uc810"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">R \ud504\ub85c\uadf8\ub798\ubc0d \uc5b8\uc5b4\ub294 \uc120\ud615 \ubaa8\ub378 \ud53c\ud305\uc744 \uc704\ud574 \ub2e4\uc74c \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. lm &#8211; \uc120\ud615 \ubaa8\ub378\uc744 \ub9de\ucd94\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \ud568\uc218\ub294 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>lm(\uacf5\uc2dd, \ub370\uc774\ud130, \u2026)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\uae08:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\uacf5\uc2dd:<\/strong> \uc120\ud615 \ubaa8\ub378 \uacf5\uc2dd(\uc608: y ~ x1 + x2)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\ub370\uc774\ud130:<\/strong> \ub370\uc774\ud130\uac00 \ud3ec\ud568\ub41c \ub370\uc774\ud130 \ube14\ub85d\uc758 \uc774\ub984<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>2. glm \u2013 \uc77c\ubc18\ud654 \uc120\ud615 \ubaa8\ub378\uc744 \ud53c\ud305\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \ud568\uc218\ub294 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>glm(\uacf5\uc2dd, family=\uac00\uc6b0\uc2a4, \ub370\uc774\ud130, \u2026)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\uae08:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\uacf5\uc2dd:<\/strong> \uc120\ud615 \ubaa8\ub378 \uacf5\uc2dd(\uc608: y ~ x1 + x2)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>family:<\/strong> \ubaa8\ub378\uc744 \uc801\ud569\uc2dc\ud0a4\ub294 \ub370 \uc0ac\uc6a9\ud560 \ud1b5\uacc4\uc801 \ud328\ubc00\ub9ac\uc785\ub2c8\ub2e4. \uae30\ubcf8\uac12\uc740 \uac00\uc6b0\uc2a4\uc774\uc9c0\ub9cc \ub2e4\ub978 \uc635\uc158\uc73c\ub85c\ub294 \uc774\ud56d, \uac10\ub9c8, \ud3ec\uc544\uc1a1 \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\ub370\uc774\ud130:<\/strong> \ub370\uc774\ud130\uac00 \ud3ec\ud568\ub41c \ub370\uc774\ud130 \ube14\ub85d\uc758 \uc774\ub984<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\uc774 \ub450 \ud568\uc218\uc758 \uc720\uc77c\ud55c \ucc28\uc774\uc810\uc740 <strong>glm()<\/strong> \ud568\uc218\uc5d0 \ud3ec\ud568\ub41c <strong>family<\/strong> \uc778\uc218\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\uae30 \uc704\ud574 lm() \ub610\ub294 glm()\uc744 \uc0ac\uc6a9\ud558\uba74 <strong>\uc815\ud655\ud788 \ub3d9\uc77c\ud55c \uacb0\uacfc\uac00 \uc0dd\uc131\ub429\ub2c8\ub2e4<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uadf8\ub7ec\ub098 glm() \ud568\uc218\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \ub354 \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc5d0 \uc801\ud569\ud558\ub3c4\ub85d \uc0ac\uc6a9\ub420 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0(\ud328\ubc00\ub9ac=\uc774\ud56d)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ko\/\u1106\u116e\u11af\u1100\u1169\u1100\u1175-\u1112\u116c\u1100\u1171\/\" target=\"_blank\" rel=\"noopener\">\ud3ec\uc544\uc1a1 \ud68c\uadc0<\/a> (\uac00\uc871=\ubb3c\uace0\uae30)<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 lm() \ubc0f glm() \ud568\uc218\ub97c \uc2e4\uc81c\ub85c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>lm() \ud568\uc218 \uc0ac\uc6a9 \uc608<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub294 lm() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec <strong>\uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744<\/strong> \ud53c\ud305\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit multiple linear regression model\n<\/span>model &lt;- lm(mpg ~ disp + hp, data=mtcars)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nlm(formula = mpg ~ disp + hp, data = mtcars)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-4.7945 -2.3036 -0.8246 1.8582 6.9363 \n\nCoefficients:\n             Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 30.735904 1.331566 23.083 &lt; 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<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>glm() \ud568\uc218 \uc0ac\uc6a9 \uc608<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub294 glm() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc815\ud655\ud788 \ub3d9\uc77c\ud55c <strong>\uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744<\/strong> \ud53c\ud305\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit multiple linear regression model\n<\/span>model &lt;- glm(mpg ~ disp + hp, data=mtcars)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = mpg ~ disp + hp, data = mtcars)\n\nDeviance Residuals: \n    Min 1Q Median 3Q Max  \n-4.7945 -2.3036 -0.8246 1.8582 6.9363  \n\nCoefficients:\n             Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 30.735904 1.331566 23.083 &lt; 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\n(Dispersion parameter for gaussian family taken to be 9.775636)\n\n    Null deviance: 1126.05 on 31 degrees of freedom\nResidual deviance: 283.49 on 29 degrees of freedom\nAIC: 168.62\n\nNumber of Fisher Scoring iterations: 2<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uacc4\uc218 \ucd94\uc815\uce58\uc640 \uacc4\uc218 \ucd94\uc815\uce58\uc758 \ud45c\uc900 \uc624\ucc28\ub294 lm() \ud568\uc218\uc5d0\uc11c \uc0dd\uc131\ub41c \uac83\uacfc \uc815\ud655\ud788 \ub3d9\uc77c\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uacfc \uac19\uc774 family=binomial\uc744 \uc9c0\uc815\ud558\uc5ec glm() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec <strong>\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744<\/strong> \ub9de\ucd9c \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit logistic regression model\n<\/span>model &lt;- glm(am ~ disp + hp, data=mtcars, family=binomial)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = am ~ disp + hp, family = binomial, data = mtcars)\n\nDeviance Residuals: \n    Min 1Q Median 3Q Max  \n-1.9665 -0.3090 -0.0017 0.3934 1.3682  \n\nCoefficients:\n            Estimate Std. Error z value Pr(&gt;|z|)  \n(Intercept) 1.40342 1.36757 1.026 0.3048  \navailable -0.09518 0.04800 -1.983 0.0474 *\nhp 0.12170 0.06777 1.796 0.0725 .\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n    Null deviance: 43,230 on 31 degrees of freedom\nResidual deviance: 16,713 on 29 degrees of freedom\nAIC: 22,713\n\nNumber of Fisher Scoring iterations: 8\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ub610\ud55c \ub2e4\uc74c\uacfc \uac19\uc774 family=poisson\uc744 \uc9c0\uc815\ud558\uc5ec glm() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec <strong>\ud3ec\uc544\uc1a1 \ud68c\uadc0 \ubaa8\ub378<\/strong> \uc5d0 \ub9de\ucd9c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit Poisson regression model\n<\/span>model &lt;- glm(am ~ disp + hp, data=mtcars, family=fish)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = am ~ disp + hp, family = fish, data = mtcars)\n\nDeviance Residuals: \n    Min 1Q Median 3Q Max  \n-1.1266 -0.4629 -0.2453 0.1797 1.5428  \n\nCoefficients:\n             Estimate Std. Error z value Pr(&gt;|z|)   \n(Intercept) 0.214255 0.593463 0.361 0.71808   \navailable -0.018915 0.007072 -2.674 0.00749 **\nhp 0.016522 0.007163 2.307 0.02107 * \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for fish family taken to be 1)\n\n    Null deviance: 23,420 on 31 degrees of freedom\nResidual deviance: 10,526 on 29 degrees of freedom\nAIC: 42,526\n\nNumber of Fisher Scoring iterations: 6\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/ko\/r\u110b\u1174-\u1103\u1161\u11ab\u1109\u116e\u11ab-\u1109\u1165\u11ab\u1112\u1167\u11bc-\u1112\u116c\u1100\u1171\/\" target=\"_blank\" rel=\"noopener\">R\uc5d0\uc11c \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1103\u1161\u110c\u116e\u11bc-\u1109\u1165\u11ab\u1112\u1167\u11bc-\u1112\u116c\u1100\u1171-r\/\" target=\"_blank\" rel=\"noopener\">R\uc5d0\uc11c \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/r-glm-\u110b\u1168\u110e\u1173\u11a8\/\" target=\"_blank\" rel=\"noopener\">R\uc5d0\uc11c glm\uacfc \ud568\uaed8 \uc608\uce21 \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>R \ud504\ub85c\uadf8\ub798\ubc0d \uc5b8\uc5b4\ub294 \uc120\ud615 \ubaa8\ub378 \ud53c\ud305\uc744 \uc704\ud574 \ub2e4\uc74c \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. 1. lm &#8211; \uc120\ud615 \ubaa8\ub378\uc744 \ub9de\ucd94\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc774 \ud568\uc218\ub294 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. lm(\uacf5\uc2dd, \ub370\uc774\ud130, \u2026) \uae08: \uacf5\uc2dd: \uc120\ud615 \ubaa8\ub378 \uacf5\uc2dd(\uc608: y ~ x1 + x2) \ub370\uc774\ud130: \ub370\uc774\ud130\uac00 \ud3ec\ud568\ub41c \ub370\uc774\ud130 \ube14\ub85d\uc758 \uc774\ub984 2. glm \u2013 \uc77c\ubc18\ud654 \uc120\ud615 \ubaa8\ub378\uc744 \ud53c\ud305\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc774 \ud568\uc218\ub294 \ub2e4\uc74c \uad6c\ubb38\uc744 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-1545","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>R\uc5d0\uc11c glm\uacfc lm\uc758 \ucc28\uc774\uc810<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uba87 \uac00\uc9c0 \uc608\ub97c 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