{"id":1274,"date":"2023-07-27T01:24:14","date_gmt":"2023-07-27T01:24:14","guid":{"rendered":"https:\/\/statorials.org\/ar\/dfbetas-%d9%81%d9%8a-%d8%b5\/"},"modified":"2023-07-27T01:24:14","modified_gmt":"2023-07-27T01:24:14","slug":"dfbetas-%d9%81%d9%8a-%d8%b5","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/dfbetas-%d9%81%d9%8a-%d8%b5\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u062d\u0633\u0627\u0628 dfbetas \u0641\u064a r"},"content":{"rendered":"

<\/p>\n

\n

\u0641\u064a \u0627\u0644\u0625\u062d\u0635\u0627\u0621\u060c \u063a\u0627\u0644\u0628\u064b\u0627 \u0645\u0627 \u0646\u0631\u063a\u0628 \u0641\u064a \u0645\u0639\u0631\u0641\u0629 \u062a\u0623\u062b\u064a\u0631 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a<\/a> \u0627\u0644\u0645\u062e\u062a\u0644\u0641\u0629 \u0641\u064a \u0646\u0645\u0627\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631.<\/span><\/p>\n

\u062a\u062a\u0645\u062b\u0644 \u0625\u062d\u062f\u0649 \u0637\u0631\u0642 \u062d\u0633\u0627\u0628 \u062a\u0623\u062b\u064a\u0631 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0641\u064a \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0645\u0642\u064a\u0627\u0633 \u064a\u064f\u0639\u0631\u0641 \u0628\u0627\u0633\u0645 DFBETAS<\/strong> \u060c \u0648\u0627\u0644\u0630\u064a \u064a\u062e\u0628\u0631\u0646\u0627 \u0628\u0627\u0644\u062a\u0623\u062b\u064a\u0631 \u0627\u0644\u0645\u0639\u064a\u0627\u0631\u064a \u0639\u0644\u0649 \u0643\u0644 \u0645\u0639\u0627\u0645\u0644 \u0644\u0625\u0632\u0627\u0644\u0629 \u0643\u0644 \u0645\u0644\u0627\u062d\u0638\u0629 \u0639\u0644\u0649 \u062d\u062f\u0629.<\/span><\/p>\n

\u064a\u0645\u0646\u062d\u0646\u0627 \u0647\u0630\u0627 \u0627\u0644\u0645\u0642\u064a\u0627\u0633 \u0641\u0643\u0631\u0629 \u0639\u0646 \u062a\u0623\u062b\u064a\u0631 \u0643\u0644 \u0645\u0644\u0627\u062d\u0638\u0629 \u0639\u0644\u0649 \u0643\u0644 \u062a\u0642\u062f\u064a\u0631 \u0644\u0644\u0645\u0639\u0627\u0645\u0644 \u0641\u064a \u0646\u0645\u0648\u0630\u062c \u0627\u0646\u062d\u062f\u0627\u0631 \u0645\u0639\u064a\u0646.<\/span><\/p>\n

\u064a\u0639\u0631\u0636 \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0645\u062b\u0627\u0644\u064b\u0627 \u062e\u0637\u0648\u0629 \u0628\u062e\u0637\u0648\u0629 \u0644\u0643\u064a\u0641\u064a\u0629 \u062d\u0633\u0627\u0628 \u0648\u062a\u0635\u0648\u0631 DFBETAS \u0644\u0643\u0644 \u0645\u0644\u0627\u062d\u0638\u0629 \u0641\u064a \u0646\u0645\u0648\u0630\u062c \u0641\u064a R.<\/span><\/p>\n

\u0627\u0644\u062e\u0637\u0648\u0629 1: \u0625\u0646\u0634\u0627\u0621 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631<\/strong><\/span><\/h3>\n

\u0623\u0648\u0644\u0627\u064b\u060c \u0633\u0646\u0642\u0648\u0645 \u0628\u0625\u0646\u0634\u0627\u0621 \u0646\u0645\u0648\u0630\u062c \u0627\u0646\u062d\u062f\u0627\u0631 \u062e\u0637\u064a \u0645\u062a\u0639\u062f\u062f<\/a> \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a mtcars<\/strong> \u0627\u0644\u0645\u0636\u0645\u0646\u0629 \u0641\u064a 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
 \u0627\u0644\u062e\u0637\u0648\u0629 2: \u062d\u0633\u0627\u0628 DFBETAS \u0644\u0643\u0644 \u0645\u0644\u0627\u062d\u0638\u0629<\/strong><\/span><\/h3>\n
 \u0628\u0639\u062f \u0630\u0644\u0643\u060c \u0633\u0648\u0641 \u0646\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 dfbetas()<\/strong> \u0627\u0644\u0645\u0636\u0645\u0646\u0629 \u0644\u062d\u0633\u0627\u0628 \u0642\u064a\u0645 DFBETAS \u0644\u0643\u0644 \u0645\u0644\u0627\u062d\u0638\u0629 \u0641\u064a \u0627\u0644\u0646\u0645\u0648\u0630\u062c:<\/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
 \u0644\u0643\u0644 \u0645\u0644\u0627\u062d\u0638\u0629\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0627\u0644\u0641\u0631\u0642 \u0641\u064a \u062a\u0642\u062f\u064a\u0631 \u0627\u0644\u0645\u0639\u0627\u0645\u0644 \u0644\u0644\u0623\u0635\u0644\u060c \u0648\u0645\u062a\u063a\u064a\u0631 disp<\/em> \u060c \u0648\u0645\u062a\u063a\u064a\u0631 hp<\/em> \u0627\u0644\u0630\u064a \u064a\u062d\u062f\u062b \u0639\u0646\u062f\u0645\u0627 \u0646\u0632\u064a\u0644 \u062a\u0644\u0643 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0629 \u0627\u0644\u0645\u062d\u062f\u062f\u0629.<\/span><\/p>\n
 \u0628\u0634\u0643\u0644 \u0639\u0627\u0645\u060c \u0646\u062d\u0646 \u0646\u0639\u062a\u0628\u0631 \u0623\u0646 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0629 \u0644\u0647\u0627 \u062a\u0623\u062b\u064a\u0631 \u0642\u0648\u064a \u0639\u0644\u0649 \u062a\u0642\u062f\u064a\u0631 \u0645\u0639\u0627\u0645\u0644 \u0645\u0639\u064a\u0646 \u0625\u0630\u0627 \u0643\u0627\u0646\u062a \u0642\u064a\u0645\u0629 DBETAS \u0623\u0643\u0628\u0631 \u0645\u0646 \u0639\u062a\u0628\u0629 2\/\u221a n<\/span> \u062d\u064a\u062b n<\/em> \u0647\u0648 \u0639\u062f\u062f \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a.<\/span><\/p>\n
 \u0641\u064a \u0647\u0630\u0627 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u0633\u062a\u0643\u0648\u0646 \u0627\u0644\u0639\u062a\u0628\u0629 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
 \u0627\u0644\u062e\u0637\u0648\u0629 3: \u062a\u0635\u0648\u0631 DFBETAS<\/strong><\/p>\n
 \u0623\u062e\u064a\u0631\u064b\u0627\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0625\u0646\u0634\u0627\u0621 \u0645\u062e\u0637\u0637\u0627\u062a \u0644\u062a\u0635\u0648\u0631 \u0642\u064a\u0645\u0629 DFBETAS \u0644\u0643\u0644 \u0645\u0644\u0627\u062d\u0638\u0629 \u0648\u0644\u0643\u0644 \u0645\u062a\u0646\u0628\u0626 \u0641\u064a \u0627\u0644\u0646\u0645\u0648\u0630\u062c:<\/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
\"\u062f\u0641\u0628\u064a\u062a\u0627\u0633<\/p>\n
 \u0641\u064a \u0643\u0644 \u0645\u062e\u0637\u0637\u060c \u064a\u0639\u0631\u0636 \u0627\u0644\u0645\u062d\u0648\u0631 x \u0641\u0647\u0631\u0633 \u0643\u0644 \u0645\u0644\u0627\u062d\u0638\u0629 \u0641\u064a \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0648\u062a\u0639\u0631\u0636 \u0627\u0644\u0642\u064a\u0645\u0629 y DFBETAS \u0627\u0644\u0645\u0642\u0627\u0628\u0644\u0629 \u0644\u0643\u0644 \u0645\u0644\u0627\u062d\u0638\u0629 \u0648\u0643\u0644 \u0645\u062a\u0646\u0628\u0626.<\/span><\/p>\n
 \u0641\u064a \u0627\u0644\u0645\u062e\u0637\u0637 \u0627\u0644\u0623\u0648\u0644 \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0623\u0646 \u062b\u0644\u0627\u062b \u0645\u0644\u0627\u062d\u0638\u0627\u062a \u062a\u062a\u062c\u0627\u0648\u0632 \u0642\u064a\u0645\u0629 \u0627\u0644\u0639\u062a\u0628\u0629 \u0627\u0644\u0645\u0637\u0644\u0642\u0629 \u0627\u0644\u0628\u0627\u0644\u063a\u0629 0.3535534<\/strong> \u0648\u0641\u064a \u0627\u0644\u0645\u062e\u0637\u0637 \u0627\u0644\u062b\u0627\u0646\u064a \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0623\u0646 \u0645\u0644\u0627\u062d\u0638\u062a\u064a\u0646 \u062a\u062a\u062c\u0627\u0648\u0632 \u0642\u064a\u0645\u0629 \u0627\u0644\u0639\u062a\u0628\u0629 \u0627\u0644\u0645\u0637\u0644\u0642\u0629.<\/span><\/p>\n
 \u0642\u062f \u0646\u062e\u062a\u0627\u0631 \u062f\u0631\u0627\u0633\u0629 \u0647\u0630\u0647 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0639\u0646 \u0643\u062b\u0628 \u0644\u062a\u062d\u062f\u064a\u062f \u0645\u0627 \u0625\u0630\u0627 \u0643\u0627\u0646 \u0644\u0647\u0627 \u062a\u0623\u062b\u064a\u0631 \u063a\u064a\u0631 \u0636\u0631\u0648\u0631\u064a \u0639\u0644\u0649 \u062a\u0642\u062f\u064a\u0631 \u0645\u0639\u0627\u0645\u0644\u0627\u062a \u0627\u0644\u0646\u0645\u0648\u0630\u062c.<\/span><\/p>\n
 \u0645\u0635\u0627\u062f\u0631 \u0625\u0636\u0627\u0641\u064a\u0629<\/strong><\/span><\/h3>\n
 \u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0628\u0633\u064a\u0637 \u0641\u064a R<\/a>
 \u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0645\u062a\u0639\u062f\u062f \u0641\u064a R<\/a>
 \u0643\u064a\u0641\u064a\u0629 \u062d\u0633\u0627\u0628 \u0625\u062d\u0635\u0627\u0626\u064a\u0627\u062a \u0627\u0644\u0631\u0627\u0641\u0639\u0629 \u0627\u0644\u0645\u0627\u0644\u064a\u0629 \u0641\u064a R<\/a>
 \u0643\u064a\u0641\u064a\u0629 \u062d\u0633\u0627\u0628 DFFITS \u0641\u064a R<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0641\u064a \u0627\u0644\u0625\u062d\u0635\u0627\u0621\u060c \u063a\u0627\u0644\u0628\u064b\u0627 \u0645\u0627 \u0646\u0631\u063a\u0628 \u0641\u064a \u0645\u0639\u0631\u0641\u0629 \u062a\u0623\u062b\u064a\u0631 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0627\u0644\u0645\u062e\u062a\u0644\u0641\u0629 \u0641\u064a \u0646\u0645\u0627\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631. \u062a\u062a\u0645\u062b\u0644 \u0625\u062d\u062f\u0649 \u0637\u0631\u0642 \u062d\u0633\u0627\u0628 \u062a\u0623\u062b\u064a\u0631 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0641\u064a \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0645\u0642\u064a\u0627\u0633 \u064a\u064f\u0639\u0631\u0641 \u0628\u0627\u0633\u0645 DFBETAS \u060c \u0648\u0627\u0644\u0630\u064a \u064a\u062e\u0628\u0631\u0646\u0627 \u0628\u0627\u0644\u062a\u0623\u062b\u064a\u0631 \u0627\u0644\u0645\u0639\u064a\u0627\u0631\u064a \u0639\u0644\u0649 \u0643\u0644 \u0645\u0639\u0627\u0645\u0644 \u0644\u0625\u0632\u0627\u0644\u0629 \u0643\u0644 \u0645\u0644\u0627\u062d\u0638\u0629 \u0639\u0644\u0649 \u062d\u062f\u0629. \u064a\u0645\u0646\u062d\u0646\u0627 \u0647\u0630\u0627 \u0627\u0644\u0645\u0642\u064a\u0627\u0633 \u0641\u0643\u0631\u0629 \u0639\u0646 \u062a\u0623\u062b\u064a\u0631 \u0643\u0644 \u0645\u0644\u0627\u062d\u0638\u0629 \u0639\u0644\u0649 \u0643\u0644 \u062a\u0642\u062f\u064a\u0631 \u0644\u0644\u0645\u0639\u0627\u0645\u0644 \u0641\u064a \u0646\u0645\u0648\u0630\u062c \u0627\u0646\u062d\u062f\u0627\u0631 \u0645\u0639\u064a\u0646. \u064a\u0639\u0631\u0636 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"yoast_head":"\n\u0643\u064a\u0641\u064a\u0629 \u062d\u0633\u0627\u0628 DFBETAS \u0641\u064a R - 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