{"id":1246,"date":"2023-07-27T03:48:40","date_gmt":"2023-07-27T03:48:40","guid":{"rendered":"https:\/\/statorials.org\/my\/%e1%80%a1%e1%80%80%e1%80%bc%e1%80%bd%e1%80%84%e1%80%ba%e1%80%b8%e1%80%a1%e1%80%80%e1%80%bb%e1%80%94%e1%80%ba%e1%80%99%e1%80%bb%e1%80%ac%e1%80%b8%e1%80%80%e1%80%ad%e1%80%af-r-%e1%80%90%e1%80%bd\/"},"modified":"2023-07-27T03:48:40","modified_gmt":"2023-07-27T03:48:40","slug":"%e1%80%a1%e1%80%80%e1%80%bc%e1%80%bd%e1%80%84%e1%80%ba%e1%80%b8%e1%80%a1%e1%80%80%e1%80%bb%e1%80%94%e1%80%ba%e1%80%99%e1%80%bb%e1%80%ac%e1%80%b8%e1%80%80%e1%80%ad%e1%80%af-r-%e1%80%90%e1%80%bd","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/%e1%80%a1%e1%80%80%e1%80%bc%e1%80%bd%e1%80%84%e1%80%ba%e1%80%b8%e1%80%a1%e1%80%80%e1%80%bb%e1%80%94%e1%80%ba%e1%80%99%e1%80%bb%e1%80%ac%e1%80%b8%e1%80%80%e1%80%ad%e1%80%af-r-%e1%80%90%e1%80%bd\/","title":{"rendered":"R \u1010\u103d\u1004\u103a \u1000\u103b\u1031\u102c\u1004\u103a\u1038\u101e\u102c\u1038\u1016\u103c\u1005\u103a\u1000\u103b\u1014\u103a\u1014\u1031\u1019\u103e\u102f\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1014\u100a\u103a\u1038"},"content":{"rendered":"

<\/p>\n

\n

\u1000\u103b\u1031\u102c\u1004\u103a\u1038\u101e\u102c\u1038\u1021\u1000\u103c\u103d\u1004\u103a\u1038\u1021\u1000\u103b\u1014\u103a<\/strong> \u101e\u100a\u103a \u104e\u1004\u103a\u1038\u104f\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1005\u1036\u101e\u103d\u1031\u1016\u100a\u103a\u1019\u103e\u102f\u1016\u103c\u1004\u1037\u103a \u1015\u102d\u102f\u1004\u103a\u1038\u1001\u103c\u102c\u1038\u1011\u102c\u1038\u101e\u1031\u102c \u1021\u1000\u103c\u103d\u1004\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n

\u101c\u1000\u103a\u1010\u103d\u1031\u1037\u1010\u103d\u1004\u103a\u104a \u1000\u103b\u1031\u102c\u1004\u103a\u1038\u101e\u102c\u1038\u1021\u1000\u103c\u103d\u1004\u103a\u1038\u1021\u1000\u103b\u1014\u103a\u101e\u100a\u103a 3 \u104f \u1015\u1000\u1010\u102d\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1011\u1000\u103a \u1015\u102d\u102f\u1000\u103c\u102e\u1038\u101e\u1031\u102c \u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1010\u1005\u103a\u1001\u102f\u1010\u103d\u1004\u103a \u1005\u1031\u102c\u1004\u1037\u103a\u1000\u103c\u100a\u1037\u103a\u1019\u103e\u102f<\/a> \u1010\u102d\u102f\u1004\u103a\u1038\u101e\u100a\u103a \u101e\u102c\u101c\u103d\u1014\u103a\u101e\u100a\u103a\u101f\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037 \u101a\u1031\u1018\u1030\u101a\u103b\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a \u1006\u102d\u102f\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n

\u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u1037\u103a MASS \u1015\u1000\u103a\u1000\u1031\u1037\u1001\u103a\u103b\u1019\u103e studres()<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d R \u101b\u103e\u102d \u1019\u100a\u103a\u101e\u100a\u1037\u103a \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1015\u102f\u1036\u1005\u1036\u104f \u1000\u103b\u1031\u102c\u1004\u103a\u1038\u101e\u102c\u1038\u1016\u103c\u1005\u103a\u1000\u103b\u1014\u103a\u1014\u1031\u101e\u100a\u1037\u103a \u1021\u1000\u103c\u103d\u1004\u103a\u1038\u1021\u1000\u103b\u1014\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u101c\u103b\u1004\u103a\u1019\u103c\u1014\u103a\u1005\u103d\u102c \u101b\u101b\u103e\u102d\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n

studres (\u1019\u1031\u102c\u103a\u1012\u101a\u103a)<\/strong><\/span><\/p>\n

\u1018\u101a\u103a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000<\/em> \u1019\u1006\u102d\u102f linear model \u1000\u102d\u102f \u1000\u102d\u102f\u101a\u103a\u1005\u102c\u1038\u1015\u103c\u102f\u1010\u101a\u103a\u104b<\/span><\/p>\n

\u1025\u1015\u1019\u102c- R \u1010\u103d\u1004\u103a \u1000\u103b\u1031\u102c\u1004\u103a\u1038\u101e\u102c\u1038\u1016\u103c\u1005\u103a\u1000\u103b\u1014\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1001\u103c\u1004\u103a\u1038\u104b<\/strong><\/span><\/h3>\n

built-in mtcars<\/em> dataset \u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d R \u1010\u103d\u1004\u103a \u101b\u102d\u102f\u1038\u101b\u103e\u1004\u103a\u1038\u101e\u1031\u102c linear regression<\/a> model \u1000\u102d\u102f \u1010\u100a\u103a\u1006\u1031\u102c\u1000\u103a\u1019\u100a\u103a\u1006\u102d\u102f\u1015\u102b\u1005\u102d\u102f\u1037\u104b<\/span><\/p>\n

 #build simple linear regression model<\/span>\nmodel <- lm(mpg ~ disp, data=mtcars)\n<\/strong><\/pre>\n
 \u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u101b\u103e\u102d \u1005\u1030\u1038\u1005\u1019\u103a\u1038\u1019\u103e\u102f\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1021\u1010\u103d\u1000\u103a \u1000\u103b\u1031\u102c\u1004\u103a\u1038\u101e\u102c\u1038\u1016\u103c\u1005\u103a\u1000\u103b\u1014\u103a\u1014\u1031\u101e\u1031\u1038\u101e\u1031\u102c \u1021\u1000\u103c\u103d\u1004\u103a\u1038\u1021\u1000\u103b\u1014\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u101b\u1014\u103a MASS \u1021\u1011\u102f\u1015\u103a\u1019\u103e studres()<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n
 library<\/span> (MASS)<\/span>\n\n#calculate studentized residuals<\/span>\nstud_resids <- studres(model)\n\n#view first three studentized residuals<\/span>\nhead(stud_resids, 3)\n\n    Mazda RX4 Mazda RX4 Wag Datsun 710 \n   -0.6236250 -0.6236250 -0.7405315 \n<\/strong><\/pre>\n
 \u101e\u1000\u103a\u1006\u102d\u102f\u1004\u103a\u101b\u102c \u101c\u1031\u1037\u101c\u102c\u1011\u102c\u1038\u101e\u1031\u102c \u1021\u1000\u103c\u103d\u1004\u103a\u1038\u1021\u1000\u103b\u1014\u103a\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u1015\u1010\u103a\u101e\u1000\u103a\u101e\u1031\u102c \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1019\u103b\u102c\u1038\u104f \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u1019\u103c\u1014\u103a\u1000\u103d\u1000\u103a\u1000\u103d\u1000\u103a \u1016\u1014\u103a\u1010\u102e\u1038\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span> <\/p>\n
 #plot predictor variable vs. studentized residuals\n<\/span>plot(mtcars$disp, stud_resids, ylab=' Studentized Residuals<\/span> ', xlab=' Displacement<\/span> ') \n\n#add horizontal line at 0\n<\/span>abline(0, 0)<\/strong> <\/pre>\n
\"R<\/p>\n
 \u1002\u101b\u1015\u103a\u1019\u103e\u101c\u1031\u1037\u101c\u102c\u1010\u103d\u1031\u1037\u101b\u103e\u102d\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a 3 \u1011\u1000\u103a\u1000\u103c\u102e\u1038\u101e\u1031\u102c \u1015\u1000\u1010\u102d\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u101b\u103e\u102d\u101e\u1031\u102c \u1000\u103b\u1031\u102c\u1004\u103a\u1038\u101e\u102c\u1038\u1021\u1000\u103c\u103d\u1004\u103a\u1038\u1021\u1000\u103b\u1014\u103a\u1019\u101b\u103e\u102d\u101e\u100a\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1031\u1037\u1019\u103c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104a \u1011\u102d\u102f\u1037\u1000\u103c\u1031\u102c\u1004\u1037\u103a \u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1010\u103d\u1004\u103a \u101b\u103e\u1004\u103a\u1038\u101b\u103e\u1004\u103a\u1038\u101c\u1004\u103a\u1038\u101c\u1004\u103a\u1038\u1021\u1005\u103d\u1014\u103a\u1038\u1021\u1011\u1004\u103a\u1038\u1019\u103b\u102c\u1038\u1019\u101b\u103e\u102d\u1015\u102b\u104b<\/span><\/p>\n
 \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1006\u1014\u1039\u1012\u101b\u103e\u102d\u1015\u102b\u1000 \u1019\u1030\u101c\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1010\u103d\u1004\u103a \u101c\u1031\u1037\u101c\u102c\u1019\u103e\u102f\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u104f \u1000\u103b\u1031\u102c\u1004\u103a\u1038\u101e\u102c\u1038\u1016\u103c\u1005\u103a\u1000\u103b\u1014\u103a\u1014\u1031\u101e\u1031\u1038\u101e\u1031\u102c \u1021\u1000\u103c\u103d\u1004\u103a\u1038\u1021\u1000\u103b\u1014\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f\u101c\u100a\u103a\u1038 \u1015\u103c\u1014\u103a\u1011\u100a\u1037\u103a\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n
 #add studentized residuals to orignal dataset\n<\/span>final_data <- cbind<\/span> (mtcars[c(' mpg<\/span> ', ' disp<\/span> ')], stud_resids)\n\n#view final dataset\n<\/span>head(final_data)\n\n                   mpg disp stud_resids\nMazda RX4 21.0 160 -0.6236250\nMazda RX4 Wag 21.0 160 -0.6236250\nDatsun 710 22.8 108 -0.7405315\nHornet 4 Drive 21.4 258 0.7556078\nHornet Sportabout 18.7 360 1.2658336\nValiant 18.1 225 -0.6896297<\/strong><\/pre>\n
 \u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a \u101b\u103e\u102f\u1019\u103c\u1004\u103a\u101e\u102f\u1036\u1038\u101e\u1015\u103a\u1001\u103b\u1000\u103a\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1000\u102d\u102f \u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u1021\u1014\u102e\u1038\u1005\u1015\u103a\u1006\u102f\u1036\u1038\u101e\u102d\u101b\u103e\u102d\u1014\u102d\u102f\u1004\u103a\u101b\u1014\u103a \u104e\u1004\u103a\u1038\u104f\u1000\u103b\u1031\u102c\u1004\u103a\u1038\u101e\u102c\u1038\u1021\u1000\u103c\u103d\u1004\u103a\u1038\u1021\u1000\u103b\u1014\u103a\u1019\u103b\u102c\u1038\u1021\u1015\u1031\u102b\u103a \u1021\u1001\u103c\u1031\u1001\u1036\u104d \u1005\u1030\u1038\u1005\u1019\u103a\u1038\u1019\u103e\u102f\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1000\u102d\u102f \u1005\u102e\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span><\/p>\n
 #sort studentized residuals descending\n<\/span>final_data[ order<\/span> (-stud_resids),]\n\n                     mpg disp stud_resids\nToyota Corolla 33.9 71.1 2.52397102\nPontiac Firebird 19.2 400.0 2.06825391\nFiat 128 32.4 78.7 2.03684699\nLotus Europa 30.4 95.1 1.53905536\nHonda Civic 30.4 75.7 1.27099586\nHornet Sportabout 18.7 360.0 1.26583364\nChrysler Imperial 14.7 440.0 1.06486066\nHornet 4 Drive 21.4 258.0 0.75560776\nPorsche 914-2 26.0 120.3 0.42424678\nFiat X1-9 27.3 79.0 0.30183728\nMerc 240D 24.4 146.7 0.26235893\nFord Pantera L 15.8 351.0 0.20825609\nCadillac Fleetwood 10.4 472.0 0.08338531\nLincoln Continental 10.4 460.0 -0.07863385\nDuster 360 14.3 360.0 -0.14476167\nMerc 450SL 17.3 275.8 -0.28759769\nDodge Challenger 15.5 318.0 -0.30826585\nMerc 230 22.8 140.8 -0.30945955\nMerc 450SE 16.4 275.8 -0.56742476\nAMC Javelin 15.2 304.0 -0.58138205\nCamaro Z28 13.3 350.0 -0.58848471\nMazda RX4 Wag 21.0 160.0 -0.62362497\nMazda RX4 21.0 160.0 -0.62362497\nMaserati Bora 15.0 301.0 -0.68315010\nValiant 18.1 225.0 -0.68962974\nDatsun 710 22.8 108.0 -0.74053152\nMerc 450SLC 15.2 275.8 -0.94814699\nToyota Corona 21.5 120.1 -0.99751166\nVolvo 142E 21.4 121.0 -1.01790487\nMerc 280 19.2 167.6 -1.09979261\nFerrari Dino 19.7 145.0 -1.24732999\nMerc 280C 17.8 167.6 -1.57258064\n<\/strong><\/pre>\n
 \u1011\u1015\u103a\u101c\u1031\u102c\u1004\u103a\u1038\u1021\u101b\u1004\u103a\u1038\u1021\u1019\u103c\u1005\u103a\u1019\u103b\u102c\u1038<\/strong><\/span><\/h3>\n
 R \u1010\u103d\u1004\u103a \u101b\u102d\u102f\u1038\u101b\u103e\u1004\u103a\u1038\u101e\u1031\u102c linear regression \u101c\u102f\u1015\u103a\u1014\u100a\u103a\u1038<\/a>
 R \u1010\u103d\u1004\u103a linear regression \u1021\u1019\u103b\u102c\u1038\u1021\u1015\u103c\u102c\u1038\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1014\u100a\u103a\u1038<\/a>
 R \u1010\u103d\u1004\u103a\u1000\u103b\u1014\u103a\u101b\u103e\u102d\u101e\u1031\u102c\u1000\u103d\u1000\u103a\u1000\u103d\u1000\u103a\u1016\u1014\u103a\u1010\u102e\u1038\u1014\u100a\u103a\u1038<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u1000\u103b\u1031\u102c\u1004\u103a\u1038\u101e\u102c\u1038\u1021\u1000\u103c\u103d\u1004\u103a\u1038\u1021\u1000\u103b\u1014\u103a \u101e\u100a\u103a \u104e\u1004\u103a\u1038\u104f\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1005\u1036\u101e\u103d\u1031\u1016\u100a\u103a\u1019\u103e\u102f\u1016\u103c\u1004\u1037\u103a \u1015\u102d\u102f\u1004\u103a\u1038\u1001\u103c\u102c\u1038\u1011\u102c\u1038\u101e\u1031\u102c \u1021\u1000\u103c\u103d\u1004\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b \u101c\u1000\u103a\u1010\u103d\u1031\u1037\u1010\u103d\u1004\u103a\u104a \u1000\u103b\u1031\u102c\u1004\u103a\u1038\u101e\u102c\u1038\u1021\u1000\u103c\u103d\u1004\u103a\u1038\u1021\u1000\u103b\u1014\u103a\u101e\u100a\u103a 3 \u104f \u1015\u1000\u1010\u102d\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1011\u1000\u103a \u1015\u102d\u102f\u1000\u103c\u102e\u1038\u101e\u1031\u102c 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