{"id":1274,"date":"2023-07-27T01:24:14","date_gmt":"2023-07-27T01:24:14","guid":{"rendered":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-df%e1%84%87%e1%85%a6%e1%84%90%e1%85%a1\/"},"modified":"2023-07-27T01:24:14","modified_gmt":"2023-07-27T01:24:14","slug":"r%e1%84%8b%e1%85%b4-df%e1%84%87%e1%85%a6%e1%84%90%e1%85%a1","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-df%e1%84%87%e1%85%a6%e1%84%90%e1%85%a1\/","title":{"rendered":"R\uc5d0\uc11c dfbetas\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

\ud1b5\uacc4\uc5d0\uc11c \uc6b0\ub9ac\ub294 \ud68c\uadc0 \ubaa8\ub378\uc5d0\uc11c \ub2e4\uc591\ud55c \uad00\ucc30\uc774<\/a> \uc5b4\ub5a4 \uc601\ud5a5\uc744 \ubbf8\uce58\ub294\uc9c0 \uc54c\uace0 \uc2f6\uc5b4\ud558\ub294 \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\uad00\uce21\uac12\uc758 \uc601\ud5a5\uc744 \uacc4\uc0b0\ud558\ub294 \ud55c \uac00\uc9c0 \ubc29\ubc95\uc740 DFBETAS<\/strong> \ub77c\ub294 \uce21\uc815\ud56d\ubaa9\uc744 \uc0ac\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774\ub294 \uac01 \uac1c\ubcc4 \uad00\uce21\uac12\uc744 \uc81c\uac70\ud558\ub294 \uac01 \uacc4\uc218\uc5d0 \ub300\ud55c \ud45c\uc900\ud654\ub41c \ud6a8\uacfc\ub97c \uc54c\ub824\uc90d\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \uce21\uc815\ud56d\ubaa9\uc740 \uc8fc\uc5b4\uc9c4 \ud68c\uadc0 \ubaa8\ub378\uc758 \uac01 \uacc4\uc218 \ucd94\uc815\uce58\uc5d0 \ub300\ud55c \uac01 \uad00\uce21\uac12\uc758 \uc601\ud5a5\uc5d0 \ub300\ud55c \uc544\uc774\ub514\uc5b4\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 R \ubaa8\ub378\uc758 \uac01 \uad00\uce21\uac12\uc5d0 \ub300\ud574 DFBETAS\ub97c \uacc4\uc0b0\ud558\uace0 \uc2dc\uac01\ud654\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \ub2e8\uacc4\ubcc4 \uc608\ub97c \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n

1\ub2e8\uacc4: \ud68c\uadc0 \ubaa8\ub378 \ub9cc\ub4e4\uae30<\/strong><\/span><\/h3>\n

\uba3c\uc800 R\uc5d0 \ub0b4\uc7a5\ub41c mtcars<\/strong> \ub370\uc774\ud130 \uc138\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744<\/a> \ub9cc\ub4ed\ub2c8\ub2e4.<\/span><\/p>\n

 #fit a regression model<\/span>\nmodel <- lm(mpg~disp+hp, data=mtcars)\n\n#view model summary\n<\/span>summary(model)\n\nCoefficients:\n             Estimate Std. Error t value Pr(>|t|)    \n(Intercept) 30.735904 1.331566 23.083 < 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\n<\/strong><\/pre>\n
 2\ub2e8\uacc4: \uac01 \uad00\ucc30\uc5d0 \ub300\ud55c DFBETAS \uacc4\uc0b0<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c\uc73c\ub85c \ub0b4\uc7a5\ub41c dfbetas()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uac01 \uad00\uce21\uac12\uc5d0 \ub300\ud55c DFBETAS \uac12\uc744 \uacc4\uc0b0\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 #calculate DFBETAS for each observation in the model\n<\/span>dfbetas <- as<\/span> . data<\/span> . frame<\/span> (dfbetas(model))\n\n#display DFBETAS for each observation\n<\/span>dfbetas\n\n                      (Intercept) disp hp\nMazda RX4 -0.1174171253 0.030760632 1.748143e-02\nMazda RX4 Wag -0.1174171253 0.030760632 1.748143e-02\nDatsun 710 -0.1694989349 0.086630144 -3.332781e-05\nHornet 4 Drive 0.0577309674 0.078971334 -8.705488e-02\nHornet Sportabout -0.0204333878 0.237526523 -1.366155e-01\nValiant -0.1711908285 -0.139135639 1.829038e-01\nDuster 360 -0.0312338677 -0.005356209 3.581378e-02\nMerc 240D -0.0312259577 -0.010409922 2.433256e-02\nMerc 230 -0.0865872595 0.016428917 2.287867e-02\nMerc 280 -0.1560683502 0.078667906 -1.911180e-02\nMerc 280C -0.2254489597 0.113639937 -2.760800e-02\nMerc 450SE 0.0022844093 0.002966155 -2.855985e-02\nMerc 450SL 0.0009062022 0.001176644 -1.132941e-02\nMerc 450SLC 0.0041566755 0.005397169 -5.196706e-02\nCadillac Fleetwood 0.0388832216 -0.134511133 7.277283e-02\nLincoln Continental 0.0483781688 -0.121146607 5.326220e-02\nChrysler Imperial -0.1645266331 0.236634429 -3.917771e-02\nFiat 128 0.5720358325 -0.181104179 -1.265475e-01\nHonda Civic 0.3490872162 -0.053660545 -1.326422e-01\nToyota Corolla 0.7367058819 -0.268512348 -1.342384e-01\nToyota Corona -0.2181110386 0.101336902 5.945352e-03\nDodge Challenger -0.0270169005 -0.123610713 9.441241e-02\nAMC Javelin -0.0406785103 -0.141711468 1.074514e-01\nCamaro Z28 0.0390139262 0.012846225 -5.031588e-02\nPontiac Firebird -0.0549059340 0.574544346 -3.689584e-01\nFiat X1-9 0.0565157245 -0.017751582 -1.262221e-02\nPorsche 914-2 0.0839169111 -0.028670987 -1.240452e-02\nLotus Europa 0.3444562478 -0.402678927 2.135224e-01\nFord Pantera L -0.1598854695 -0.094184733 2.320845e-01\nFerrari Dino -0.0343997122 0.248642444 -2.344154e-01\nMaserati Bora -0.3436265545 -0.511285637 7.319066e-01\nVolvo 142E -0.1784974091 0.132692956 -4.433915e-02\n<\/strong><\/pre>\n
 \uac01 \uad00\uce21\uac12\uc5d0 \ub300\ud574 \ud2b9\uc815 \uad00\uce21\uac12\uc744 \uc81c\uac70\ud560 \ub54c \ubc1c\uc0dd\ud558\ub294 \uc6d0\uc810, disp<\/em> \ubcc0\uc218 \ubc0f hp<\/em> \ubcc0\uc218\uc5d0 \ub300\ud55c \uacc4\uc218 \ucd94\uc815\uce58\uc758 \ucc28\uc774\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc77c\ubc18\uc801\uc73c\ub85c \uc6b0\ub9ac\ub294 \uad00\uce21\uce58\uac00 2\/ \u221an<\/span> \uc758 \uc784\uacc4\uac12\ubcf4\ub2e4 \ud070 DBETAS \uac12\uc744 \uac16\ub294 \uacbd\uc6b0 \uad00\uce21\uce58\uac00 \uc8fc\uc5b4\uc9c4 \uacc4\uc218\uc758 \ucd94\uc815\uc5d0 \uac15\ud55c \uc601\ud5a5\uc744 \ubbf8\uce5c\ub2e4\uace0 \uac04\uc8fc\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c n<\/em> \uc740 \uad00\uce21\uce58 \uc218\uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774 \uc608\uc5d0\uc11c \uc784\uacc4\uac12\uc740 0.3535534<\/strong> \uc785\ub2c8\ub2e4.<\/span><\/p>\n
 #find number of observations<\/span>\nn <- nrow<\/span> (mtcars)\n\n#calculate DFBETAS threshold value<\/span>\nthresh <- 2\/ sqrt<\/span> (n)\n\nthresh\n\n[1] 0.3535534\n<\/strong><\/pre>\n
 3\ub2e8\uacc4: DFBETAS \uc2dc\uac01\ud654<\/strong><\/p>\n
 \ub9c8\uc9c0\ub9c9\uc73c\ub85c \ubaa8\ub378\uc758 \uac01 \uad00\uce21\uac12\uacfc \uac01 \uc608\uce21 \ubcc0\uc218\uc5d0 \ub300\ud55c DFBETAS \uac12\uc744 \uc2dc\uac01\ud654\ud558\ub294 \ud50c\ub86f\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 #specify 2 rows and 1 column in plotting region<\/span>\nby(mfrow=c(2,1))\n\n#plot DFBETAS for disp<\/em> with threshold lines<\/span>\nplot(dfbetas$disp, type=' h<\/span> ')\nabline(h = thresh, lty = 2)\nabline(h = -thresh, lty = 2)\n\n#plot DFBETAS for hp<\/em> with threshold lines<\/span> \nplot(dfbetas$hp, type=' h<\/span> ')\nabline(h = thresh, lty = 2)\nabline(h = -thresh, lty = 2)\n<\/strong><\/pre>\n
\"R\uc758<\/p>\n
 \uac01 \ud50c\ub86f\uc5d0\uc11c x\ucd95\uc740 \ub370\uc774\ud130 \uc138\ud2b8\uc758 \uac01 \uad00\uce21\uce58\uc5d0 \ub300\ud55c \uc778\ub371\uc2a4\ub97c \ud45c\uc2dc\ud558\uace0 y-\uac12\uc740 \uac01 \uad00\uce21\uce58 \ubc0f \uac01 \uc608\uce21 \ubcc0\uc218\uc5d0 \ud574\ub2f9\ud558\ub294 DFBETAS\ub97c \ud45c\uc2dc\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uccab \ubc88\uc9f8 \ud50c\ub86f\uc5d0\uc11c\ub294 3\uac1c\uc758 \uad00\uce21\uce58\uac00 \uc808\ub300 \uc784\uacc4\uac12 0.3535534<\/strong> \ub97c \ucd08\uacfc\ud558\ub294 \uac83\uc744 \ubcfc \uc218 \uc788\uc73c\uba70, \ub450 \ubc88\uc9f8 \ud50c\ub86f\uc5d0\uc11c\ub294 2\uac1c\uc758 \uad00\uce21\uce58\uac00 \uc808\ub300 \uc784\uacc4\uac12\uc744 \ucd08\uacfc\ud558\ub294 \uac83\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc6b0\ub9ac\ub294 \uc774\ub7ec\ud55c \uad00\ucc30\uc744 \ub354 \uc790\uc138\ud788 \uc5f0\uad6c\ud558\uc5ec \ubaa8\ub378 \uacc4\uc218 \ucd94\uc815\uc5d0 \uacfc\ub3c4\ud55c \uc601\ud5a5\uc744 \ubbf8\uce58\ub294\uc9c0 \uc5ec\ubd80\ub97c \uacb0\uc815\ud560 \uc218 \uc788\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 \ub808\ubc84\ub9ac\uc9c0 \ud1b5\uacc4\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a>
 R\uc5d0\uc11c DFFITS\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\ud1b5\uacc4\uc5d0\uc11c \uc6b0\ub9ac\ub294 \ud68c\uadc0 \ubaa8\ub378\uc5d0\uc11c \ub2e4\uc591\ud55c \uad00\ucc30\uc774 \uc5b4\ub5a4 \uc601\ud5a5\uc744 \ubbf8\uce58\ub294\uc9c0 \uc54c\uace0 \uc2f6\uc5b4\ud558\ub294 \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4. \uad00\uce21\uac12\uc758 \uc601\ud5a5\uc744 \uacc4\uc0b0\ud558\ub294 \ud55c \uac00\uc9c0 \ubc29\ubc95\uc740 DFBETAS \ub77c\ub294 \uce21\uc815\ud56d\ubaa9\uc744 \uc0ac\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774\ub294 \uac01 \uac1c\ubcc4 \uad00\uce21\uac12\uc744 \uc81c\uac70\ud558\ub294 \uac01 \uacc4\uc218\uc5d0 \ub300\ud55c \ud45c\uc900\ud654\ub41c \ud6a8\uacfc\ub97c \uc54c\ub824\uc90d\ub2c8\ub2e4. \uc774 \uce21\uc815\ud56d\ubaa9\uc740 \uc8fc\uc5b4\uc9c4 \ud68c\uadc0 \ubaa8\ub378\uc758 \uac01 \uacc4\uc218 \ucd94\uc815\uce58\uc5d0 \ub300\ud55c \uac01 \uad00\uce21\uac12\uc758 \uc601\ud5a5\uc5d0 \ub300\ud55c \uc544\uc774\ub514\uc5b4\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 R \ubaa8\ub378\uc758 \uac01 […]<\/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-1274","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nR\uc5d0\uc11c DFBETAS\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95 - 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