<\/p>\n
R’de kar\u015f\u0131la\u015fabilece\u011finiz bir hata mesaj\u0131:<\/span><\/p>\n Bu hata mesaj\u0131, R’de glm()<\/strong> i\u015flevini kullanarak bir model uydurdu\u011funuzda ve tahmin de\u011fi\u015fkenlerinizin iki veya daha fazlas\u0131n\u0131n birbirleriyle tam bir do\u011frusal ili\u015fkiye sahip oldu\u011fu durumlarda ortaya \u00e7\u0131kar; bu, m\u00fckemmel \u00e7oklu do\u011frusall\u0131k<\/a> olarak bilinir.<\/span><\/p>\n Bu hatay\u0131 d\u00fczeltmek i\u00e7in, veri k\u00fcmenizdeki birbiriyle m\u00fckemmel korelasyona sahip de\u011fi\u015fkenleri tan\u0131mlamak i\u00e7in cor()<\/strong> i\u015flevini kullanabilir ve bu de\u011fi\u015fkenlerden birini regresyon modelinden kald\u0131rabilirsiniz.<\/span><\/span><\/p>\n Bu e\u011fitimde, bu hata mesaj\u0131yla pratikte nas\u0131l ba\u015fa \u00e7\u0131k\u0131laca\u011f\u0131 a\u00e7\u0131klanmaktad\u0131r.<\/span><\/p>\n R’deki a\u015fa\u011f\u0131daki veri \u00e7er\u00e7evesine bir lojistik regresyon modeli<\/a> uydurdu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n Katsay\u0131 \u00e7\u0131kt\u0131s\u0131ndan hemen \u00f6nce \u015fu mesaj\u0131 ald\u0131\u011f\u0131m\u0131z\u0131 unutmay\u0131n:<\/span><\/p>\n Bu durum, modeldeki iki veya daha fazla yorday\u0131c\u0131 de\u011fi\u015fkenin m\u00fckemmel bir do\u011frusal ili\u015fkiye sahip oldu\u011funu ve dolay\u0131s\u0131yla modeldeki t\u00fcm regresyon katsay\u0131lar\u0131n\u0131n tahmin edilemedi\u011fini g\u00f6stermektedir.<\/span><\/p>\n \u00d6rne\u011fin, yorday\u0131c\u0131 de\u011fi\u015fken x 2<\/sub><\/strong> i\u00e7in hi\u00e7bir katsay\u0131 tahmini yap\u0131lamayaca\u011f\u0131n\u0131 unutmay\u0131n.<\/span><\/p>\n Bu hataya hangi yorday\u0131c\u0131 de\u011fi\u015fkenlerin neden oldu\u011funu belirlemek i\u00e7in cor()<\/strong> fonksiyonunu kullanarak birkorelasyon matrisi<\/a> olu\u015fturabilir ve hangi de\u011fi\u015fkenlerin birbiriyle tam olarak 1<\/strong> korelasyona sahip oldu\u011funu inceleyebiliriz:<\/span><\/p>\n Korelasyon matrisinden x 1<\/sub><\/strong> ve x 2<\/sub><\/strong> de\u011fi\u015fkenlerinin m\u00fckemmel korelasyona sahip oldu\u011funu g\u00f6rebiliriz.<\/span><\/p>\n Bu hatay\u0131 \u00e7\u00f6zmek i\u00e7in, regresyon modelinde asl\u0131nda benzersiz veya ba\u011f\u0131ms\u0131z bilgi sa\u011flamad\u0131klar\u0131 i\u00e7in bu iki de\u011fi\u015fkenden birini basit\u00e7e modelden kald\u0131rabiliriz.<\/span><\/p>\n \u00d6rne\u011fin, x 2’yi<\/sub><\/strong> \u00e7\u0131kard\u0131\u011f\u0131m\u0131z\u0131 ve a\u015fa\u011f\u0131daki lojistik regresyon modelini uygulad\u0131\u011f\u0131m\u0131z\u0131 varsayal\u0131m:<\/span><\/p>\n Bu sefer \u201ctekillikler nedeniyle tan\u0131ms\u0131z\u201d hata mesaj\u0131 almad\u0131\u011f\u0131m\u0131z\u0131 unutmay\u0131n.<\/span><\/p>\n Not<\/strong> : x 1<\/sub> veya x 2’yi<\/sub> kald\u0131rmam\u0131z\u0131n bir \u00f6nemi yoktur. Nihai model, tutmaya karar verdi\u011finiz de\u011fi\u015fken i\u00e7in ayn\u0131 katsay\u0131 tahminini i\u00e7erecek ve modelin genel uyum iyili\u011fi ayn\u0131 olacakt\u0131r.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde R’deki di\u011fer hatalar\u0131n nas\u0131l ele al\u0131naca\u011f\u0131 a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n R’de nas\u0131l d\u00fczeltilir: ExtractVars’ta ge\u00e7ersiz \u015fablon form\u00fcl\u00fc R’de kar\u015f\u0131la\u015fabilece\u011finiz bir hata mesaj\u0131: Coefficients: (1 not defined because of singularities) Bu hata mesaj\u0131, R’de glm() i\u015flevini kullanarak bir model uydurdu\u011funuzda ve tahmin de\u011fi\u015fkenlerinizin iki veya daha fazlas\u0131n\u0131n birbirleriyle tam bir do\u011frusal ili\u015fkiye sahip oldu\u011fu durumlarda ortaya \u00e7\u0131kar; bu, m\u00fckemmel \u00e7oklu do\u011frusall\u0131k olarak bilinir. Bu hatay\u0131 d\u00fczeltmek i\u00e7in, veri k\u00fcmenizdeki birbiriyle m\u00fckemmel korelasyona sahip […]<\/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":[],"class_list":["post-2140","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n Coefficients: (1 not defined because of singularities) \n<\/strong><\/pre>\n
Hata nas\u0131l yeniden olu\u015fturulur?<\/strong><\/h3>\n
#define data\n<\/span>df <- data. frame<\/span> (y = c(0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1),\n x1 = c(3, 3, 4, 4, 3, 2, 5, 8, 9, 9, 9, 8, 9, 9, 9),\n x2 = c(6, 6, 8, 8, 6, 4, 10, 16, 18, 18, 18, 16, 18, 18, 18),\n x3 = c(4, 7, 7, 3, 8, 9, 9, 8, 7, 8, 9, 4, 9, 10, 13))\n\n#fit logistic regression model\n<\/span>model <- glm(y~x1+x2+x3, data=df, family=binomial)\n\n#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = y ~ x1 + x2 + x3, family = binomial, data = df)\n\nDeviance Residuals: \n Min 1Q Median 3Q Max \n-1.372e-05 -2.110e-08 2.110e-08 2.110e-08 1.575e-05 \n\nCoefficients: (1 not defined because of singularities)\n Estimate Std. Error z value Pr(>|z|)\n(Intercept) -75.496 176487.031 0.000 1\nx1 14.546 24314.459 0.001 1\nx2 NA NA NA NA\nx3 -2.258 20119.863 0.000 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n Null deviance: 2.0728e+01 on 14 degrees of freedom\nResidual deviance: 5.1523e-10 on 12 degrees of freedom\nAIC: 6\n\nNumber of Fisher Scoring iterations: 24\n<\/strong><\/span><\/pre>\n Coefficients: (1 not defined because of singularities)\n<\/strong><\/span><\/pre>\n Hata nas\u0131l ele al\u0131n\u0131r?<\/strong><\/span><\/h3>\n
#create correlation matrix<\/span>\ncor(df)\n\n y x1 x2 x3\ny 1.0000000 0.9675325 0.9675325 0.3610320\nx1 0.9675325 1.0000000 1.0000000 0.3872889\nx2 0.9675325 1.0000000 1.0000000 0.3872889\nx3 0.3610320 0.3872889 0.3872889 1.0000000\n<\/strong><\/span><\/pre>\n #fit logistic regression model\n<\/span>model <- glm(y~x1+x3, data=df, family=binomial)\n\n#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = y ~ x1 + x3, family = binomial, data = df)\n\nDeviance Residuals: \n Min 1Q Median 3Q Max \n-1.372e-05 -2.110e-08 2.110e-08 2.110e-08 1.575e-05 \n\nCoefficients:\n Estimate Std. Error z value Pr(>|z|)\n(Intercept) -75.496 176487.031 0.000 1\nx1 14.546 24314.459 0.001 1\nx3 -2.258 20119.863 0.000 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n Null deviance: 2.0728e+01 on 14 degrees of freedom\nResidual deviance: 5.1523e-10 on 12 degrees of freedom\nAIC: 6\n\nNumber of Fisher Scoring iterations: 24<\/strong><\/span><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
R’de nas\u0131l d\u00fczeltilir: arg\u00fcman ne say\u0131sal ne de mant\u0131ksal: return na<\/a>
Nas\u0131l d\u00fczeltilir: randomForest.default(m, y, \u2026): Yabanc\u0131 i\u015flev \u00e7a\u011fr\u0131s\u0131nda Na\/NaN\/Inf<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"