{"id":1585,"date":"2023-07-25T18:28:01","date_gmt":"2023-07-25T18:28:01","guid":{"rendered":"https:\/\/statorials.org\/ko\/r-%e1%84%83%e1%85%a1%e1%86%ab%e1%84%8b%e1%85%b5%e1%86%af-%e1%84%80%e1%85%a1%e1%86%b9%e1%84%8b%e1%85%b3%e1%86%af-%e1%84%8b%e1%85%a8%e1%84%8e%e1%85%b3%e1%86%a8\/"},"modified":"2023-07-25T18:28:01","modified_gmt":"2023-07-25T18:28:01","slug":"r-%e1%84%83%e1%85%a1%e1%86%ab%e1%84%8b%e1%85%b5%e1%86%af-%e1%84%80%e1%85%a1%e1%86%b9%e1%84%8b%e1%85%b3%e1%86%af-%e1%84%8b%e1%85%a8%e1%84%8e%e1%85%b3%e1%86%a8","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/r-%e1%84%83%e1%85%a1%e1%86%ab%e1%84%8b%e1%85%b5%e1%86%af-%e1%84%80%e1%85%a1%e1%86%b9%e1%84%8b%e1%85%b3%e1%86%af-%e1%84%8b%e1%85%a8%e1%84%8e%e1%85%b3%e1%86%a8\/","title":{"rendered":"R\uc5d0\uc11c \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e8\uc77c \uac12\uc744 \uc608\uce21\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

R\uc5d0 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\ub824\uba74 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\ub294 lm()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 model <- lm(y ~ x1 + x2, data=df)\n<\/strong><\/pre>\n
 \uadf8\ub7f0 \ub2e4\uc74c \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e8\uc77c \uac12\uc744 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 predict(model, newdata = new)<\/strong><\/pre>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 R\uc758 \uc801\ud569 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e8\uc77c \uac12\uc744 \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\uc2dc 1: \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc608\uce21<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 R\uc5d0\uc11c \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud53c\ud305\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #create data\n<\/span>df <- data. frame<\/span> (x=c(3, 4, 4, 5, 5, 6, 7, 8, 11, 12),\n                 y=c(22, 24, 24, 25, 25, 27, 29, 31, 32, 36))\n\n#fit simple linear regression model\n<\/span>model <- lm(y ~ x, data=df)<\/strong><\/pre>\n
 \uadf8\ub9ac\uace0 \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0c8\ub85c\uc6b4 \uad00\ucc30\uc5d0 \ub300\ud55c \uc751\ub2f5 \uac12\uc744 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #define new observation\n<\/span>new <- data. frame<\/span> (x=c(5))\n\n#use the fitted model to predict the value for the new observation\n<\/span>predict(model, newdata = new)\n\n       1 \n25.36364 \n<\/strong><\/pre>\n
 \ubaa8\ub378\uc740 \uc774 \uc0c8\ub85c\uc6b4 \uad00\uce21\uac12\uc774 25.36364<\/strong> \uc758 \ubc18\uc751 \uac12\uc744 \uac00\uc9c8 \uac83\uc774\ub77c\uace0 \uc608\uce21\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\uc2dc 2: \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc608\uce21<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 R\uc5d0\uc11c \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #create data\n<\/span>df <- data. frame<\/span> (x1=c(3, 4, 4, 5, 5, 6, 7, 8, 11, 12),\n                 x2=c(6, 6, 7, 7, 8, 9, 11, 13, 14, 14),\n                 y=c(22, 24, 24, 25, 25, 27, 29, 31, 32, 36))\n\n#fit multiple linear regression model\n<\/span>model <- lm(y ~ x1 + x2, data=df)<\/strong><\/pre>\n
 \uadf8\ub9ac\uace0 \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0c8\ub85c\uc6b4 \uad00\ucc30\uc5d0 \ub300\ud55c \uc751\ub2f5 \uac12\uc744 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #define new observation\n<\/span>new <- data.frame(x1=c(5),\n                  x2=c(10))\n\n#use the fitted model to predict the value for the new observation\n<\/span>predict(model, newdata = new)\n\n       1 \n26.17073 \n<\/strong><\/pre>\n
 \ubaa8\ub378\uc740 \uc774 \uc0c8\ub85c\uc6b4 \uad00\uce21\uac12\uc774 26.17073<\/strong> \uc758 \uc751\ub2f5 \uac12\uc744 \uac00\uc9c8 \uac83\uc774\ub77c\uace0 \uc608\uce21\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc0c8 \uac12\uc744 \uc608\uce21\ud560 \ub54c \ubc1c\uc0dd\ud560 \uc218 \uc788\ub294 \uc624\ub958<\/strong><\/span><\/h3>\n
 \uc0c8 \uac12\uc744 \uc608\uce21\ud558\ub824\uace0 \ud560 \ub54c \ubc1c\uc0dd\ud560 \uc218 \uc788\ub294 \uac00\uc7a5 \uc77c\ubc18\uc801\uc778 \uc624\ub958 \ub294 \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\ub294 \ub370 \uc0ac\uc6a9\ud55c \ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \uc608\uce21\ud558\ub824\ub294 \uc0c8 \uad00\ucc30\uacfc \ub3d9\uc77c\ud55c \uc5f4 \uc774\ub984\uc774 \uc5c6\ub294 \uacbd\uc6b0<\/strong> \uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\ub97c \ub4e4\uc5b4 R\uc5d0 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc801\ud569\ud558\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 #createdata\n<\/span>df <- data. frame<\/span> (x1=c(3, 4, 4, 5, 5, 6, 7, 8, 11, 12),\n                 x2=c(6, 6, 7, 7, 8, 9, 11, 13, 14, 14),\n                 y=c(22, 24, 24, 25, 25, 27, 29, 31, 32, 36))\n\n#fit multiple linear regression model\n<\/span>model <- lm(y ~ x1 + x2, data=df)<\/strong><\/pre>\n
 \ub2e4\uc74c\uc73c\ub85c, \uc774 \uc0c8\ub85c\uc6b4 \uad00\ucc30\uc5d0 \ub300\ud55c \ubc18\uc751 \uac12\uc744 \uc608\uce21\ud558\uae30 \uc704\ud574 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud55c\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #define new observation\n<\/span>new <- data.frame(x_1=c(5),\n                  x_2=c(10))\n\n#use the fitted model to predict the value for the new observation\n<\/span>predict(model, newdata = new)\n\nError in eval(predvars, data, env): object 'x1' not found\n<\/span><\/strong><\/pre>\n
 \uc0c8 \uad00\uce21\uce58(x_1, x_2)\uc758 \uc5f4 \uc774\ub984\uc774 \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\ub294 \ub370 \uc0ac\uc6a9\ud55c \uc6d0\ub798 \ub370\uc774\ud130 \ud504\ub808\uc784(x1, x2)\uc758 \uc5f4 \uc774\ub984\uacfc \uc77c\uce58\ud558\uc9c0 \uc54a\uae30 \ub54c\ubb38\uc5d0 \uc624\ub958\uac00 \ubc1c\uc0dd\ud588\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n
 R\uc5d0\uc11c \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a>
 R\uc5d0\uc11c \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a>
 R\uc5d0\uc11c \uc794\ucc28 \ud50c\ub86f\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
R\uc5d0 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\ub824\uba74 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\ub294 lm() \ud568\uc218\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. model <- lm(y ~ x1 + x2, data=df) \uadf8\ub7f0 \ub2e4\uc74c \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e8\uc77c \uac12\uc744 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. predict(model, newdata = new) \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 R\uc758 \uc801\ud569 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e8\uc77c \uac12\uc744 \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \uc608\uc2dc 1: \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 […]<\/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-1585","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nR\uc5d0\uc11c \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e8\uc77c \uac12\uc744 \uc608\uce21\ud558\ub294 \ubc29\ubc95<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 R\uc758 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e8\uc77c \uac12\uc744 \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc744 \uc608\uc81c\uc640 \ud568\uaed8 \uc124\uba85\ud569\ub2c8\ub2e4.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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