{"id":3996,"date":"2023-07-14T06:42:43","date_gmt":"2023-07-14T06:42:43","guid":{"rendered":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-%e1%84%89%e1%85%b3%e1%84%91%e1%85%b3%e1%86%af%e1%84%85%e1%85%a1%e1%84%8b%e1%85%b5%e1%86%ab-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1\/"},"modified":"2023-07-14T06:42:43","modified_gmt":"2023-07-14T06:42:43","slug":"r%e1%84%8b%e1%85%b4-%e1%84%89%e1%85%b3%e1%84%91%e1%85%b3%e1%86%af%e1%84%85%e1%85%a1%e1%84%8b%e1%85%b5%e1%86%ab-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-%e1%84%89%e1%85%b3%e1%84%91%e1%85%b3%e1%86%af%e1%84%85%e1%85%a1%e1%84%8b%e1%85%b5%e1%86%ab-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1\/","title":{"rendered":"R\uc5d0\uc11c \uc2a4\ud50c\ub77c\uc778 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95(\uc608\uc81c \ud3ec\ud568)"},"content":{"rendered":"

<\/p>\n

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\uc2a4\ud50c\ub77c\uc778 \ud68c\uadc0\ub294<\/strong> \ub370\uc774\ud130\uc758 \ud328\ud134\uc774 \uac11\uc790\uae30 \ubcc0\uacbd\ub418\ub294 \uc810 \ub610\ub294 “\ub9e4\ub4ed”\uc774 \uc788\uace0 \uc120\ud615 \ud68c\uadc0<\/a> \ubc0f \ub2e4\ud56d\uc2dd \ud68c\uadc0\uac00<\/a> \ub370\uc774\ud130\uc5d0 \uc801\ud569\ud560 \ub9cc\ud07c \uc720\uc5f0\ud558\uc9c0 \uc54a\uc744 \ub54c \uc0ac\uc6a9\ub418\ub294 \ud68c\uadc0 \uc720\ud615\uc785\ub2c8\ub2e4.<\/span><\/p>\n

\ub2e4\uc74c \ub2e8\uacc4\ubcc4 \uc608\uc81c\uc5d0\uc11c\ub294 R\uc5d0\uc11c \uc2a4\ud50c\ub77c\uc778 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n

1\ub2e8\uacc4: \ub370\uc774\ud130 \uc0dd\uc131<\/strong><\/span><\/h2>\n

\uba3c\uc800 \ub450 \uac1c\uc758 \ubcc0\uc218\uac00 \uc788\ub294 R\uc5d0\uc11c \ub370\uc774\ud130 \uc138\ud2b8\ub97c \ub9cc\ub4e4\uace0 \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \uc2dc\uac01\ud654\ud558\uae30 \uc704\ud55c \uc0b0\uc810\ub3c4\ub97c \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n

 #create data frame\n<\/span>df <- data. frame<\/span> (x=1:20,\n                 y=c(2, 4, 7, 9, 13, 15, 19, 16, 13, 10,\n                     11, 14, 15, 15, 16, 15, 17, 19, 18, 20))\n\n#view head of data frame\n<\/span>head(df)\n\n  xy\n1 1 2\n2 2 4\n3 3 7\n4 4 9\n5 5 13\n6 6 15\n\n#create scatterplot\n<\/span>plot(df$x, df$y, cex= 1.5<\/span> , pch= 19<\/span> )\n<\/strong><\/pre>\n
\"\"<\/p>\n
 \ubd84\uba85\ud788 x\uc640 y \uc0ac\uc774\uc758 \uad00\uacc4\ub294 \ube44\uc120\ud615\uc774\uba70 x=7\uacfc x=10\uc5d0\uc11c \ub370\uc774\ud130\uc758 \ud328\ud134\uc774 \uac11\uc790\uae30 \ubcc0\uacbd\ub418\ub294 \ub450 \uac1c\uc758 \uc810 \ub610\ub294 “\ub178\ub4dc”\uac00 \uc788\ub294 \uac83\uc73c\ub85c \ubcf4\uc785\ub2c8\ub2e4.<\/span><\/p>\n
 2\ub2e8\uacc4: \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378 \ud53c\ud305<\/strong><\/span><\/h2>\n
 \uadf8\ub7f0 \ub2e4\uc74c lm() \ud568\uc218\ub97c<\/a> \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc774 \ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \ub9de\ucd94\uace0 \uc0b0\uc810\ub3c4\uc758 \ud68c\uadc0\uc120\uc5d0 \ub9de\ucdb0 \ud50c\ub86f\ud569\ub2c8\ub2e4.<\/span> <\/p>\n
 #fit simple linear regression model\n<\/span>linear_fit <- lm(df$y ~ df$x)\n\n#view model summary\n<\/span>summary(linear_fit)\n\nCall:\nlm(formula = df$y ~ df$x)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-5.2143 -1.6327 -0.3534 0.6117 7.8789 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)    \n(Intercept) 6.5632 1.4643 4.482 0.000288 ***\ndf$x 0.6511 0.1222 5.327 4.6e-05 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.152 on 18 degrees of freedom\nMultiple R-squared: 0.6118, Adjusted R-squared: 0.5903 \nF-statistic: 28.37 on 1 and 18 DF, p-value: 4.603e-05\n\n#create scatterplot\n<\/span>plot(df$x, df$y, cex= 1.5<\/span> , pch= 19<\/span> )\n\n#add regression line to scatterplot\n<\/span>abline(linear_fit)<\/strong> <\/pre>\n
\"\"<\/p>\n
 \uc0b0\uc810\ub3c4\ub97c \ubcf4\uba74 \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0\uc120\uc774 \ub370\uc774\ud130\uc5d0 \uc798 \ub9de\uc9c0 \uc54a\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774\ub97c \uc2a4\ud50c\ub77c\uc778 \ubaa8\ub378\uc758 \uc870\uc815\ub41c R \uc81c\uacf1 \uac12\uacfc \ube44\uad50\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 3\ub2e8\uacc4: \uc2a4\ud50c\ub77c\uc778 \ud68c\uadc0 \ubaa8\ub378 \ub9de\ucd94\uae30<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c\uc73c\ub85c, splines<\/strong> \ud328\ud0a4\uc9c0\uc758 bs()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub450 \uac1c\uc758 \ub178\ub4dc\uac00 \uc788\ub294 \uc2a4\ud50c\ub77c\uc778 \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd98 \ub2e4\uc74c \uc801\ud569\ud55c \ubaa8\ub378\uc744 \uc0b0\uc810\ub3c4\uc5d0 \uadf8\ub824 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 library<\/span> (splines)\n\n#fit spline regression model\n<\/span>spline_fit <- lm(df$y ~ bs(df$x, knots=c( 7<\/span> , 10<\/span> )))\n\n#view summary of spline regression model\n<\/span>summary(spline_fit)\n\nCall:\nlm(formula = df$y ~ bs(df$x, knots = c(7, 10)))\n\nResiduals:\n     Min 1Q Median 3Q Max \n-2.84883 -0.94928 0.08675 0.78069 2.61073 \n\nCoefficients:\n                            Estimate Std. Error t value Pr(>|t|)    \n(Intercept) 2.073 1.451 1.429 0.175    \nbs(df$x, knots = c(7, 10))1 2.173 3.247 0.669 0.514    \nbs(df$x, knots = c(7, 10))2 19.737 2.205 8.949 3.63e-07 ***\nbs(df$x, knots = c(7, 10))3 3.256 2.861 1.138 0.274    \nbs(df$x, knots = c(7, 10))4 19.157 2.690 7.121 5.16e-06 ***\nbs(df$x, knots = c(7, 10))5 16.771 1.999 8.391 7.83e-07 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 1.568 on 14 degrees of freedom\nMultiple R-squared: 0.9253, Adjusted R-squared: 0.8987 \nF-statistic: 34.7 on 5 and 14 DF, p-value: 2.081e-07\n\n#calculate predictions using spline regression model\n<\/span>x_lim <- range(df$x)\nx_grid <- seq(x_lim[ 1<\/span> ], x_lim[ 2<\/span> ])\npreds <- predict(spline_fit, newdata=list(x=x_grid))\n\n#create scatter plot with spline regression predictions\n<\/span>plot(df$x, df$y, cex= 1.5<\/span> , pch= 19<\/span> )\nlines(x_grid, preds)<\/strong> <\/pre>\n
\"\"<\/p>\n
 \uc0b0\uc810\ub3c4\uc5d0\uc11c \uc2a4\ud50c\ub77c\uc778 \ud68c\uadc0 \ubaa8\ub378\uc774 \ub370\uc774\ud130\ub97c \ub9e4\uc6b0 \uc798 \uc801\ud569\ud560 \uc218 \uc788\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ubaa8\ub378 \uacb0\uacfc\uc5d0\uc11c \uc870\uc815\ub41c R-\uc81c\uacf1 \uac12\uc774 0.8987<\/strong> \uc784\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774 \ubaa8\ub378\uc758 \uc870\uc815\ub41c R-\uc81c\uacf1 \uac12\uc740 \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\ubcf4\ub2e4 \ud6e8\uc52c \ub192\uc73c\uba70 \uc774\ub294 \uc2a4\ud50c\ub77c\uc778 \ud68c\uadc0 \ubaa8\ub378\uc774 \ub370\uc774\ud130\uc5d0 \ub354 \uc798 \uc801\ud569\ud560 \uc218 \uc788\uc74c\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774 \uc608\uc5d0\uc11c\ub294 \ub178\ub4dc\uac00 x=7 \ubc0f x=10\uc5d0 \uc704\uce58\ud558\ub3c4\ub85d \uc120\ud0dd\ud588\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc2e4\uc81c\ub85c\ub294 \ub370\uc774\ud130\uc758 \ud328\ud134\uc774 \ubcc0\uacbd\ub418\ub294 \uac83\uc73c\ub85c \ub098\ud0c0\ub098\ub294 \uc704\uce58\uc640 \ub3c4\uba54\uc778 \uc804\ubb38 \uc9c0\uc2dd\uc744 \uae30\ubc18\uc73c\ub85c \ub178\ub4dc \uc704\uce58\ub97c \uc9c1\uc811 \uc120\ud0dd\ud574\uc57c \ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 R\uc5d0\uc11c \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 R\uc5d0\uc11c \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a>
 R\uc5d0\uc11c \uc9c0\uc218 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a>
 R\uc5d0\uc11c \uac00\uc911 \ucd5c\uc18c \uc81c\uacf1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\uc2a4\ud50c\ub77c\uc778 \ud68c\uadc0\ub294 \ub370\uc774\ud130\uc758 \ud328\ud134\uc774 \uac11\uc790\uae30 \ubcc0\uacbd\ub418\ub294 \uc810 \ub610\ub294 “\ub9e4\ub4ed”\uc774 \uc788\uace0 \uc120\ud615 \ud68c\uadc0 \ubc0f \ub2e4\ud56d\uc2dd \ud68c\uadc0\uac00 \ub370\uc774\ud130\uc5d0 \uc801\ud569\ud560 \ub9cc\ud07c \uc720\uc5f0\ud558\uc9c0 \uc54a\uc744 \ub54c \uc0ac\uc6a9\ub418\ub294 \ud68c\uadc0 \uc720\ud615\uc785\ub2c8\ub2e4. \ub2e4\uc74c \ub2e8\uacc4\ubcc4 \uc608\uc81c\uc5d0\uc11c\ub294 R\uc5d0\uc11c \uc2a4\ud50c\ub77c\uc778 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. 1\ub2e8\uacc4: \ub370\uc774\ud130 \uc0dd\uc131 \uba3c\uc800 \ub450 \uac1c\uc758 \ubcc0\uc218\uac00 \uc788\ub294 R\uc5d0\uc11c \ub370\uc774\ud130 \uc138\ud2b8\ub97c \ub9cc\ub4e4\uace0 \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \uc2dc\uac01\ud654\ud558\uae30 \uc704\ud55c \uc0b0\uc810\ub3c4\ub97c \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. #create data […]<\/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-3996","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nR\uc5d0\uc11c \uc2a4\ud50c\ub77c\uc778 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95(\uc608\uc81c \ud3ec\ud568) - 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