{"id":2137,"date":"2023-07-23T12:53:51","date_gmt":"2023-07-23T12:53:51","guid":{"rendered":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b3%e1%86%ab-%e1%84%90%e1%85%b3%e1%86%a8%e1%84%8b%e1%85%b5%e1%84%8c%e1%85%a5%e1%86%b7%e1%84%8b%e1%85%b3%e1%84%85%e1%85%a9-%e1%84%8b%e1%85%b5%e1%86%ab%e1%84%92%e1%85%a2-%e1%84%8c\/"},"modified":"2023-07-23T12:53:51","modified_gmt":"2023-07-23T12:53:51","slug":"r%e1%84%8b%e1%85%b3%e1%86%ab-%e1%84%90%e1%85%b3%e1%86%a8%e1%84%8b%e1%85%b5%e1%84%8c%e1%85%a5%e1%86%b7%e1%84%8b%e1%85%b3%e1%84%85%e1%85%a9-%e1%84%8b%e1%85%b5%e1%86%ab%e1%84%92%e1%85%a2-%e1%84%8c","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b3%e1%86%ab-%e1%84%90%e1%85%b3%e1%86%a8%e1%84%8b%e1%85%b5%e1%84%8c%e1%85%a5%e1%86%b7%e1%84%8b%e1%85%b3%e1%84%85%e1%85%a9-%e1%84%8b%e1%85%b5%e1%86%ab%e1%84%92%e1%85%a2-%e1%84%8c\/","title":{"rendered":"R\uc5d0\uc11c \uc218\uc815\ud558\ub294 \ubc29\ubc95: \ud2b9\uc774\uc810\uc73c\ub85c \uc778\ud574 \uc815\uc758\ub418\uc9c0 \uc54a\uc74c"},"content":{"rendered":"

<\/p>\n

\n

R\uc5d0\uc11c \ubc1c\uc0dd\ud560 \uc218 \uc788\ub294 \uc624\ub958 \uba54\uc2dc\uc9c0\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 Coefficients: (1 not defined because of singularities) \n<\/strong><\/pre>\n
 \uc774 \uc624\ub958 \uba54\uc2dc\uc9c0\ub294 R\uc5d0\uc11c glm()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud53c\ud305\ud558\uace0 \ub450 \uac1c \uc774\uc0c1\uc758 \uc608\uce21 \ubcc0\uc218\uac00 \uc11c\ub85c \uc815\ud655\ud55c \uc120\ud615 \uad00\uacc4( \uc644\uc804 \ub2e4\uc911 \uacf5\uc120\uc131<\/a> \uc774\ub77c\uace0 \ud568)\ub97c \uac00\uc9c8 \ub54c \ubc1c\uc0dd\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774 \uc624\ub958\ub97c \ud574\uacb0\ud558\ub824\uba74 cor()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\uc138\ud2b8\uc5d0\uc11c \uc11c\ub85c \uc644\ubcbd\ud55c \uc0c1\uad00\uad00\uacc4\uac00 \uc788\ub294 \ubcc0\uc218\ub97c \uc2dd\ubcc4\ud558\uace0 \ud68c\uadc0 \ubaa8\ub378\uc5d0\uc11c \uc774\ub7ec\ud55c \ubcc0\uc218 \uc911 \ud558\ub098\ub97c \uc81c\uac70\ud558\uba74 \ub429\ub2c8\ub2e4.<\/span><\/span><\/p>\n
 \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc2e4\uc81c\ub85c \uc774 \uc624\ub958 \uba54\uc2dc\uc9c0\ub97c \ucc98\ub9ac\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc624\ub958\ub97c \uc7ac\ud604\ud558\ub294 \ubc29\ubc95<\/strong><\/h3>\n
 R\uc758 \ub2e4\uc74c \ub370\uc774\ud130 \ud504\ub808\uc784\uc5d0 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744<\/a> \uc801\uc6a9\ud55c\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 #define data\n<\/span>df <- data. frame<\/span> (y = c(0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1),\n                 x1 = c(3, 3, 4, 4, 3, 2, 5, 8, 9, 9, 9, 8, 9, 9, 9),\n                 x2 = c(6, 6, 8, 8, 6, 4, 10, 16, 18, 18, 18, 16, 18, 18, 18),\n                 x3 = c(4, 7, 7, 3, 8, 9, 9, 8, 7, 8, 9, 4, 9, 10, 13))\n\n#fit logistic regression model\n<\/span>model <- glm(y~x1+x2+x3, data=df, family=binomial)\n\n#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = y ~ x1 + x2 + x3, family = binomial, data = df)\n\nDeviance Residuals: \n       Min 1Q Median 3Q Max  \n-1.372e-05 -2.110e-08 2.110e-08 2.110e-08 1.575e-05  \n\nCoefficients: (1 not defined because of singularities)\n              Estimate Std. Error z value Pr(>|z|)\n(Intercept) -75.496 176487.031 0.000 1\nx1 14.546 24314.459 0.001 1\nx2 NA NA NA NA\nx3 -2.258 20119.863 0.000 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n    Null deviance: 2.0728e+01 on 14 degrees of freedom\nResidual deviance: 5.1523e-10 on 12 degrees of freedom\nAIC: 6\n\nNumber of Fisher Scoring iterations: 24\n<\/strong><\/span><\/pre>\n
 \uacc4\uc218 \ucd9c\ub825 \uc9c1\uc804\uc5d0 \ub2e4\uc74c \uba54\uc2dc\uc9c0\uac00 \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 Coefficients: (1 not defined because of singularities)\n<\/strong><\/span><\/pre>\n
 \uc774\ub294 \ubaa8\ub378\uc5d0 \uc788\ub294 \ub450 \uac1c \uc774\uc0c1\uc758 \uc608\uce21 \ubcc0\uc218\uac00 \uc644\ubcbd\ud55c \uc120\ud615 \uad00\uacc4\ub97c \uac16\uace0 \uc788\uc73c\ubbc0\ub85c \ubaa8\ub378\uc758 \ubaa8\ub4e0 \ud68c\uadc0 \uacc4\uc218\ub97c \ucd94\uc815\ud560 \uc218\ub294 \uc5c6\uc74c\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\ub97c \ub4e4\uc5b4, \uc608\uce21 \ubcc0\uc218 x 2<\/sub><\/strong> \uc5d0 \ub300\ud574\uc11c\ub294 \uacc4\uc218 \ucd94\uc815\uc744 \ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc624\ub958\ub97c \ucc98\ub9ac\ud558\ub294 \ubc29\ubc95<\/strong><\/span><\/h3>\n
 \uc774 \uc624\ub958\ub97c \uc77c\uc73c\ud0a4\ub294 \uc608\uce21 \ubcc0\uc218\ub97c \uc2dd\ubcc4\ud558\ub824\uba74 cor()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0c1\uad00 \ud589\ub82c\uc744<\/a> \uc0dd\uc131\ud558\uace0 \uc11c\ub85c \uc815\ud655\ud788 1<\/strong> \uc758 \uc0c1\uad00 \uad00\uacc4\ub97c \uac16\ub294 \ubcc0\uc218\ub97c \uac80\uc0ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #create correlation matrix<\/span>\ncor(df)\n\n           y x1 x2 x3\ny 1.0000000 0.9675325 0.9675325 0.3610320\nx1 0.9675325 1.0000000 1.0000000 0.3872889\nx2 0.9675325 1.0000000 1.0000000 0.3872889\nx3 0.3610320 0.3872889 0.3872889 1.0000000\n<\/strong><\/span><\/pre>\n
 \uc0c1\uad00 \ud589\ub82c\uc744 \ud1b5\ud574 \ubcc0\uc218 x 1<\/sub><\/strong> \uacfc x 2<\/sub><\/strong> \uac00 \uc644\ubcbd\ud558\uac8c \uc0c1\uad00\ub418\uc5b4 \uc788\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774 \uc624\ub958\ub97c \ud574\uacb0\ud558\ub824\uba74 \ud68c\uadc0 \ubaa8\ub378\uc5d0\uc11c \uc2e4\uc81c\ub85c \uace0\uc720\ud558\uac70\ub098 \ub3c5\ub9bd\uc801\uc778 \uc815\ubcf4\ub97c \uc81c\uacf5\ud558\uc9c0 \uc54a\ub294 \ub450 \ubcc0\uc218 \uc911 \ud558\ub098\ub97c \ubaa8\ub378\uc5d0\uc11c \uc81c\uac70\ud558\uba74 \ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\ub97c \ub4e4\uc5b4 x 2\ub97c<\/sub><\/strong> \uc81c\uac70\ud558\uace0 \ub2e4\uc74c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc801\ud569\ud558\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 #fit logistic regression model\n<\/span>model <- glm(y~x1+x3, data=df, family=binomial)\n\n#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = y ~ x1 + x3, family = binomial, data = df)\n\nDeviance Residuals: \n       Min 1Q Median 3Q Max  \n-1.372e-05 -2.110e-08 2.110e-08 2.110e-08 1.575e-05  \n\nCoefficients:\n              Estimate Std. Error z value Pr(>|z|)\n(Intercept) -75.496 176487.031 0.000 1\nx1 14.546 24314.459 0.001 1\nx3 -2.258 20119.863 0.000 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n    Null deviance: 2.0728e+01 on 14 degrees of freedom\nResidual deviance: 5.1523e-10 on 12 degrees of freedom\nAIC: 6\n\nNumber of Fisher Scoring iterations: 24<\/strong><\/span><\/pre>\n
 \uc774\ubc88\uc5d0\ub294 “\ud2b9\uc774\uc810\uc73c\ub85c \uc778\ud574 \uc815\uc758\ub418\uc9c0 \uc54a\uc74c” \uc624\ub958 \uba54\uc2dc\uc9c0\uac00 \uc218\uc2e0\ub418\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucc38\uace0<\/strong> : x 1<\/sub> \ub610\ub294 x 2 \ub97c<\/sub> \uc81c\uac70\ud558\ub294\uc9c0 \uc5ec\ubd80\ub294 \uc911\uc694\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \ucd5c\uc885 \ubaa8\ub378\uc5d0\ub294 \uc720\uc9c0\ud558\uae30\ub85c \uacb0\uc815\ud55c \ubcc0\uc218\uc5d0 \ub300\ud55c \ub3d9\uc77c\ud55c \uacc4\uc218 \ucd94\uc815\uce58\uac00 \ud3ec\ud568\ub418\uba70 \ubaa8\ub378\uc758 \uc804\ubc18\uc801\uc778 \uc801\ud569\ub3c4\ub294 \ub3d9\uc77c\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 R\uc5d0\uc11c \ub2e4\ub978 \uc624\ub958\ub97c \ucc98\ub9ac\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 R \uc218\uc815 \ubc29\ubc95: ExtractVars\uc758 \uc798\ubabb\ub41c \ud15c\ud50c\ub9bf \uc218\uc2dd
 R\uc5d0\uc11c \uc218\uc815\ud558\ub294 \ubc29\ubc95: \uc778\uc218\uac00 \uc22b\uc790\ub3c4 \uc544\ub2c8\uace0 \ub17c\ub9ac\ub3c4 \uc544\ub2d9\ub2c8\ub2e4. return na<\/a>
 \ud574\uacb0 \ubc29\ubc95: randomForest.default(m, y, \u2026): \uc678\ubd80 \ud568\uc218 \ud638\ucd9c\uc758 Na\/NaN\/Inf<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
R\uc5d0\uc11c \ubc1c\uc0dd\ud560 \uc218 \uc788\ub294 \uc624\ub958 \uba54\uc2dc\uc9c0\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. Coefficients: (1 not defined because of singularities) \uc774 \uc624\ub958 \uba54\uc2dc\uc9c0\ub294 R\uc5d0\uc11c glm() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud53c\ud305\ud558\uace0 \ub450 \uac1c \uc774\uc0c1\uc758 \uc608\uce21 \ubcc0\uc218\uac00 \uc11c\ub85c \uc815\ud655\ud55c \uc120\ud615 \uad00\uacc4( \uc644\uc804 \ub2e4\uc911 \uacf5\uc120\uc131 \uc774\ub77c\uace0 \ud568)\ub97c \uac00\uc9c8 \ub54c \ubc1c\uc0dd\ud569\ub2c8\ub2e4. \uc774 \uc624\ub958\ub97c \ud574\uacb0\ud558\ub824\uba74 cor() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\uc138\ud2b8\uc5d0\uc11c \uc11c\ub85c \uc644\ubcbd\ud55c \uc0c1\uad00\uad00\uacc4\uac00 \uc788\ub294 \ubcc0\uc218\ub97c \uc2dd\ubcc4\ud558\uace0 \ud68c\uadc0 […]<\/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-2137","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nR\uc5d0\uc11c \uc218\uc815\ud558\ub294 \ubc29\ubc95: \ud2b9\uc774\uc810\uc73c\ub85c \uc778\ud574 \uc815\uc758\ub418\uc9c0 \uc54a\uc74c - 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