{"id":1272,"date":"2023-07-27T01:24:14","date_gmt":"2023-07-27T01:24:14","guid":{"rendered":"https:\/\/statorials.org\/uk\/dfbetas-%d0%b2-%d1%80\/"},"modified":"2023-07-27T01:24:14","modified_gmt":"2023-07-27T01:24:14","slug":"dfbetas-%d0%b2-%d1%80","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/dfbetas-%d0%b2-%d1%80\/","title":{"rendered":"\u042f\u043a \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 dfbetas \u0443 r"},"content":{"rendered":"

<\/p>\n

\n

\u0423 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0446\u0456 \u043c\u0438 \u0447\u0430\u0441\u0442\u043e \u0445\u043e\u0447\u0435\u043c\u043e \u0437\u043d\u0430\u0442\u0438, \u044f\u043a\u0438\u0439 \u0432\u043f\u043b\u0438\u0432 \u043c\u0430\u044e\u0442\u044c \u0440\u0456\u0437\u043d\u0456 \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f<\/a> \u043d\u0430 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0439\u043d\u0456 \u043c\u043e\u0434\u0435\u043b\u0456.<\/span><\/p>\n

\u041e\u0434\u0438\u043d \u0456\u0437 \u0441\u043f\u043e\u0441\u043e\u0431\u0456\u0432 \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044f \u0432\u043f\u043b\u0438\u0432\u0443 \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u044c \u2013 \u0446\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f \u043c\u0435\u0442\u0440\u0438\u043a\u0438, \u0432\u0456\u0434\u043e\u043c\u043e\u0457 \u044f\u043a DFBETAS<\/strong> , \u044f\u043a\u0430 \u043f\u043e\u0432\u0456\u0434\u043e\u043c\u043b\u044f\u0454 \u043d\u0430\u043c \u043f\u0440\u043e \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u0438\u0437\u043e\u0432\u0430\u043d\u0438\u0439 \u0432\u043f\u043b\u0438\u0432 \u043d\u0430 \u043a\u043e\u0436\u0435\u043d \u043a\u043e\u0435\u0444\u0456\u0446\u0456\u0454\u043d\u0442 \u0432\u0438\u043b\u0443\u0447\u0435\u043d\u043d\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u043e\u043a\u0440\u0435\u043c\u043e\u0433\u043e \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f.<\/span><\/p>\n

\u0426\u044f \u043c\u0435\u0442\u0440\u0438\u043a\u0430 \u0434\u0430\u0454 \u043d\u0430\u043c \u0443\u044f\u0432\u043b\u0435\u043d\u043d\u044f \u043f\u0440\u043e \u0432\u043f\u043b\u0438\u0432 \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f \u043d\u0430 \u043a\u043e\u0436\u043d\u0443 \u043e\u0446\u0456\u043d\u043a\u0443 \u043a\u043e\u0435\u0444\u0456\u0446\u0456\u0454\u043d\u0442\u0430 \u0432 \u0437\u0430\u0434\u0430\u043d\u0456\u0439 \u043c\u043e\u0434\u0435\u043b\u0456 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457.<\/span><\/p>\n

\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u0434\u0435\u043c\u043e\u043d\u0441\u0442\u0440\u0443\u0454 \u043f\u043e\u043a\u0440\u043e\u043a\u043e\u0432\u0438\u0439 \u043f\u0440\u0438\u043a\u043b\u0430\u0434 \u0442\u043e\u0433\u043e, \u044f\u043a \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u0442\u0430 \u0432\u0456\u0437\u0443\u0430\u043b\u0456\u0437\u0443\u0432\u0430\u0442\u0438 DFBETAS \u0434\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f \u0432 \u043c\u043e\u0434\u0435\u043b\u0456 \u0432 R.<\/span><\/p>\n

\u041a\u0440\u043e\u043a 1: \u0421\u0442\u0432\u043e\u0440\u0456\u0442\u044c \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0439\u043d\u0443 \u043c\u043e\u0434\u0435\u043b\u044c<\/strong><\/span><\/h3>\n

\u0421\u043f\u043e\u0447\u0430\u0442\u043a\u0443 \u043c\u0438 \u0441\u0442\u0432\u043e\u0440\u0438\u043c\u043e \u043c\u043e\u0434\u0435\u043b\u044c \u043c\u043d\u043e\u0436\u0438\u043d\u043d\u043e\u0457 \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457,<\/a> \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0447\u0438 \u043d\u0430\u0431\u0456\u0440 \u0434\u0430\u043d\u0438\u0445 mtcars<\/strong> , \u0432\u0431\u0443\u0434\u043e\u0432\u0430\u043d\u0438\u0439 \u0443 R:<\/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
 \u041a\u0440\u043e\u043a 2: \u041e\u0431\u0447\u0438\u0441\u043b\u0456\u0442\u044c DFBETAS \u0434\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f<\/strong><\/span><\/h3>\n
 \u0414\u0430\u043b\u0456 \u043c\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0454\u043c\u043e \u0432\u0431\u0443\u0434\u043e\u0432\u0430\u043d\u0443 \u0444\u0443\u043d\u043a\u0446\u0456\u044e dfbetas()<\/strong> , \u0449\u043e\u0431 \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f DFBETAS \u0434\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f \u0432 \u043c\u043e\u0434\u0435\u043b\u0456:<\/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
 \u0414\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u043f\u043e\u0431\u0430\u0447\u0438\u0442\u0438 \u0440\u0456\u0437\u043d\u0438\u0446\u044e \u0432 \u043e\u0446\u0456\u043d\u0446\u0456 \u043a\u043e\u0435\u0444\u0456\u0446\u0456\u0454\u043d\u0442\u0430 \u0434\u043b\u044f \u0434\u0436\u0435\u0440\u0435\u043b\u0430, \u0437\u043c\u0456\u043d\u043d\u043e\u0457 disp<\/em> \u0456 \u0437\u043c\u0456\u043d\u043d\u043e\u0457 hp<\/em> , \u044f\u043a\u0430 \u0432\u0438\u043d\u0438\u043a\u0430\u0454, \u043a\u043e\u043b\u0438 \u043c\u0438 \u0432\u0438\u0434\u0430\u043b\u044f\u0454\u043c\u043e \u0446\u0435 \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u0435 \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f.<\/span><\/p>\n
 \u0417\u0430\u0433\u0430\u043b\u043e\u043c \u043c\u0438 \u0432\u0432\u0430\u0436\u0430\u0454\u043c\u043e, \u0449\u043e \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f \u043c\u0430\u0454 \u0441\u0438\u043b\u044c\u043d\u0438\u0439 \u0432\u043f\u043b\u0438\u0432 \u043d\u0430 \u043e\u0446\u0456\u043d\u043a\u0443 \u0437\u0430\u0434\u0430\u043d\u043e\u0433\u043e \u043a\u043e\u0435\u0444\u0456\u0446\u0456\u0454\u043d\u0442\u0430, \u044f\u043a\u0449\u043e \u0432\u043e\u043d\u043e \u043c\u0430\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f DBETAS \u0431\u0456\u043b\u044c\u0448\u0435 \u0437\u0430 \u043f\u043e\u0440\u043e\u0433\u043e\u0432\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f 2\/\u221a n<\/span> , \u0434\u0435 n<\/em> \u2013 \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044c \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u044c.<\/span><\/p>\n
 \u0423 \u0446\u044c\u043e\u043c\u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456 \u043f\u043e\u0440\u043e\u0433\u043e\u0432\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0431\u0443\u0434\u0435 0,3535534<\/strong> :<\/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
 \u041a\u0440\u043e\u043a 3: \u0412\u0456\u0437\u0443\u0430\u043b\u0456\u0437\u0443\u0439\u0442\u0435 DFBETAS<\/strong><\/p>\n
 \u041d\u0430\u0440\u0435\u0448\u0442\u0456, \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0433\u0440\u0430\u0444\u0456\u043a\u0438 \u0434\u043b\u044f \u0432\u0456\u0437\u0443\u0430\u043b\u0456\u0437\u0430\u0446\u0456\u0457 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f DFBETAS \u0434\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f \u0442\u0430 \u0434\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0430 \u0432 \u043c\u043e\u0434\u0435\u043b\u0456:<\/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
\"DFBETAS<\/p>\n
 \u041d\u0430 \u043a\u043e\u0436\u043d\u043e\u043c\u0443 \u0433\u0440\u0430\u0444\u0456\u043a\u0443 \u0432\u0456\u0441\u044c x \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0454 \u0456\u043d\u0434\u0435\u043a\u0441 \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f \u0432 \u043d\u0430\u0431\u043e\u0440\u0456 \u0434\u0430\u043d\u0438\u0445, \u0430 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f y \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0454 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u043d\u0438\u0439 DFBETAS \u0434\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f \u0442\u0430 \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0430.<\/span><\/p>\n
 \u041d\u0430 \u043f\u0435\u0440\u0448\u043e\u043c\u0443 \u0433\u0440\u0430\u0444\u0456\u043a\u0443 \u043c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e, \u0449\u043e \u0442\u0440\u0438 \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f \u043f\u0435\u0440\u0435\u0432\u0438\u0449\u0443\u044e\u0442\u044c \u0430\u0431\u0441\u043e\u043b\u044e\u0442\u043d\u0435 \u043f\u043e\u0440\u043e\u0433\u043e\u0432\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f 0,3535534<\/strong> , \u0430 \u043d\u0430 \u0434\u0440\u0443\u0433\u043e\u043c\u0443 \u0433\u0440\u0430\u0444\u0456\u043a\u0443 \u043c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e, \u0449\u043e \u0434\u0432\u0430 \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f \u043f\u0435\u0440\u0435\u0432\u0438\u0449\u0443\u044e\u0442\u044c \u0430\u0431\u0441\u043e\u043b\u044e\u0442\u043d\u0435 \u043f\u043e\u0440\u043e\u0433\u043e\u0432\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f.<\/span><\/p>\n
 \u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u0440\u0456\u0448\u0438\u0442\u0438 \u0443\u0432\u0430\u0436\u043d\u0456\u0448\u0435 \u0432\u0438\u0432\u0447\u0438\u0442\u0438 \u0446\u0456 \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f, \u0449\u043e\u0431 \u0432\u0438\u0437\u043d\u0430\u0447\u0438\u0442\u0438, \u0447\u0438 \u043c\u0430\u044e\u0442\u044c \u0432\u043e\u043d\u0438 \u043d\u0430\u0434\u043c\u0456\u0440\u043d\u0438\u0439 \u0432\u043f\u043b\u0438\u0432 \u043d\u0430 \u043e\u0446\u0456\u043d\u043a\u0443 \u043a\u043e\u0435\u0444\u0456\u0446\u0456\u0454\u043d\u0442\u0456\u0432 \u043c\u043e\u0434\u0435\u043b\u0456.<\/span><\/p>\n
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Statology<\/title>\n<meta name=\"description\" content=\"\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 DFBETAS \u0443 R, \u043d\u0430 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/uk\/dfbetas-\u0432-\u0440\/\" \/>\n<meta property=\"og:locale\" content=\"uk_UA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u042f\u043a \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 DFBETAS \u0443 R - Statology\" \/>\n<meta property=\"og:description\" content=\"\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 DFBETAS \u0443 R, \u043d\u0430 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/uk\/dfbetas-\u0432-\u0440\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-27T01:24:14+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/dfbetas1.png\" \/>\n<meta name=\"author\" content=\"\u0420\u0435\u0434\u0430\u043a\u0446\u0456\u044f\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u041d\u0430\u043f\u0438\u0441\u0430\u043d\u043e\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u0420\u0435\u0434\u0430\u043a\u0446\u0456\u044f\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u041f\u0440\u0438\u0431\u043b. \u0447\u0430\u0441 \u0447\u0438\u0442\u0430\u043d\u043d\u044f\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 \u0445\u0432\u0438\u043b\u0438\u043d\u0438\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/uk\/dfbetas-%d0%b2-%d1%80\/\",\"url\":\"https:\/\/statorials.org\/uk\/dfbetas-%d0%b2-%d1%80\/\",\"name\":\"\u042f\u043a \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 DFBETAS \u0443 R - 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