<\/p>\n
R’deki glm()<\/strong> i\u015flevinden regresyon katsay\u0131lar\u0131n\u0131 \u00e7\u0131karmak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri kullanabilirsiniz:<\/span><\/p>\n Y\u00f6ntem 1: t\u00fcm regresyon katsay\u0131lar\u0131n\u0131 \u00e7\u0131kar\u0131n<\/strong><\/span><\/p>\n Y\u00f6ntem 2: Belirli bir de\u011fi\u015fken i\u00e7in regresyon katsay\u0131s\u0131n\u0131 \u00e7\u0131kar\u0131n<\/strong><\/span><\/p>\n Y\u00f6ntem 3: T\u00fcm regresyon katsay\u0131lar\u0131n\u0131 standart hata, Z de\u011feri ve P de\u011feriyle \u00e7\u0131kar\u0131n<\/strong><\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekte bu y\u00f6ntemlerin pratikte nas\u0131l kullan\u0131laca\u011f\u0131 g\u00f6sterilmektedir.<\/span><\/p>\n ISLR<\/strong> paketindeki varsay\u0131lan<\/strong> veri k\u00fcmesini kullanarak bir lojistik regresyon modeline<\/a> uydu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n Modelden t\u00fcm regresyon katsay\u0131lar\u0131n\u0131 \u00e7\u0131karmak i\u00e7in model$katsay\u0131lar\u0131<\/strong> yazabiliriz:<\/span><\/p>\n Yaln\u0131zca denge<\/strong> de\u011fi\u015fkenine ili\u015fkin regresyon katsay\u0131s\u0131n\u0131 \u00e7\u0131karmak i\u00e7in model$katsay\u0131lar[‘denge’]<\/strong> de yazabiliriz:<\/span><\/span><\/p>\n Regresyon katsay\u0131lar\u0131n\u0131 standart hatalar\u0131, z de\u011ferleri ve p de\u011ferleri<\/a> ile birlikte g\u00f6r\u00fcnt\u00fclemek i\u00e7in Summary(model)$ katsay\u0131lar\u0131n\u0131<\/strong> a\u015fa\u011f\u0131daki gibi kullanabiliriz:<\/span><\/p>\n Bu \u00e7\u0131kt\u0131da belirli de\u011ferlere de ula\u015fabiliyoruz.<\/span><\/p>\n \u00d6rne\u011fin, denge<\/b> de\u011fi\u015fkeninin p de\u011ferine eri\u015fmek i\u00e7in a\u015fa\u011f\u0131daki kodu kullanabiliriz:<\/span><\/p>\n Veya regresyon katsay\u0131lar\u0131n\u0131n her birinin p de\u011ferine eri\u015fmek i\u00e7in a\u015fa\u011f\u0131daki kodu kullanabiliriz:<\/span><\/p>\n Modeldeki her regresyon katsay\u0131s\u0131 i\u00e7in P de\u011ferleri g\u00f6sterilmektedir.<\/span><\/p>\n \u00c7\u0131kt\u0131daki herhangi bir de\u011fere eri\u015fmek i\u00e7in benzer s\u00f6zdizimini kullanabilirsiniz.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde R’de di\u011fer ortak g\u00f6revlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n R’de basit do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a> R’deki glm() i\u015flevinden regresyon katsay\u0131lar\u0131n\u0131 \u00e7\u0131karmak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri kullanabilirsiniz: Y\u00f6ntem 1: t\u00fcm regresyon katsay\u0131lar\u0131n\u0131 \u00e7\u0131kar\u0131n model$coefficients Y\u00f6ntem 2: Belirli bir de\u011fi\u015fken i\u00e7in regresyon katsay\u0131s\u0131n\u0131 \u00e7\u0131kar\u0131n model$coefficients[‘ my_variable ‘] Y\u00f6ntem 3: T\u00fcm regresyon katsay\u0131lar\u0131n\u0131 standart hata, Z de\u011feri ve P de\u011feriyle \u00e7\u0131kar\u0131n summary(model)$coefficients A\u015fa\u011f\u0131daki \u00f6rnekte bu y\u00f6ntemlerin pratikte nas\u0131l kullan\u0131laca\u011f\u0131 g\u00f6sterilmektedir. \u00d6rnek: R’deki glm()’den regresyon […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-4278","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n model$coefficients\n<\/strong><\/pre>\n
model$coefficients[' my_variable<\/span> ']<\/strong><\/pre>\n summary(model)$coefficients<\/strong><\/pre>\n
\u00d6rnek: R’deki glm()’den regresyon katsay\u0131lar\u0131n\u0131 \u00e7\u0131kar\u0131n<\/strong><\/span><\/h2>\n
#load dataset\n<\/span>data <- ISLR::Default\n\n#view first six rows of data\n<\/span>head(data)\n\n default student balance income\n1 No No 729.5265 44361.625\n2 No Yes 817.1804 12106.135\n3 No No 1073.5492 31767.139\n4 No No 529.2506 35704.494\n5 No No 785.6559 38463.496\n6 No Yes 919.5885 7491.559\n\n#fit logistic regression model\n<\/span>model <- glm(default~student+balance+income, family=' binomial<\/span> ', data=data)\n\n#view summary of logistic regression model\n<\/span>summary(model)\n\nCall:\nglm(formula = default ~ student + balance + income, family = \"binomial\", \n data = data)\n\nDeviance Residuals: \n Min 1Q Median 3Q Max \n-2.4691 -0.1418 -0.0557 -0.0203 3.7383 \n\nCoefficients:\n Estimate Std. Error z value Pr(>|z|) \n(Intercept) -1.087e+01 4.923e-01 -22.080 < 2e-16 ***\nstudentYes -6.468e-01 2.363e-01 -2.738 0.00619 ** \nbalance 5.737e-03 2.319e-04 24.738 < 2e-16 ***\nincome 3.033e-06 8.203e-06 0.370 0.71152 \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n Null deviance: 2920.6 on 9999 degrees of freedom\nResidual deviance: 1571.5 on 9996 degrees of freedom\nAIC: 1579.5\n\nNumber of Fisher Scoring iterations: 8<\/strong><\/pre>\n #extract all regression coefficients\n<\/span>model$coefficients\n\n (Intercept) studentYes balance income \n-1.086905e+01 -6.467758e-01 5.736505e-03 3.033450e-06\n<\/strong><\/pre>\n #extract coefficient for 'balance'\n<\/span>model$coefficients[' balance<\/span> ']\n\n<\/span><\/span>balance \n0.005736505\n<\/strong><\/pre>\n #view regression coefficients with standard errors, z values and p-values\n<\/span>summary(model)$coefficients\n\n Estimate Std. Error z value Pr(>|z|)\n(Intercept) -1.086905e+01 4.922555e-01 -22.080088 4.911280e-108\nstudentYes -6.467758e-01 2.362525e-01 -2.737646 6.188063e-03\nbalance 5.736505e-03 2.318945e-04 24.737563 4.219578e-135\nincome 3.033450e-06 8.202615e-06 0.369815 7.115203e-01<\/strong><\/pre>\n #view p-value for balance variable\n<\/span>summary(model)$coefficients[' balance<\/span> ', ' Pr(>|z|)<\/span> ']\n\n[1] 4.219578e-135\n<\/strong><\/pre>\n #view p-value for all variables\n<\/span>summary(model)$coefficients[, ' Pr(>|z|)<\/span> ']\n\n (Intercept) studentYes balance income \n4.911280e-108 6.188063e-03 4.219578e-135 7.115203e-01 \n<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
R’de \u00e7oklu do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a>
R’de lojistik regresyon nas\u0131l ger\u00e7ekle\u015ftirilir
R’de ikinci dereceden regresyon nas\u0131l ger\u00e7ekle\u015ftirilir?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"