Kesirli Polinomlu Lojistik Regresyon Modeli

Year 2015, Volume: 8 Issue: 2, 27 - 35, 25.12.2015

Nihal Ata Tutkun

Abstract

fonksiyonel ilişkinin olduğu varsayılmaktadır. Doğrusallık varsayımının sağlanmaması modelin yanlış

belirlenmesine neden olmaktadır. Sürekli bir açıklayıcı değişken kategorik biçime dönüştürülerek

kullanıldığında ise düzey sayısı arttıkça tahmin edilmesi gereken parametre sayısı arttığından modeli

çözümlemek ve yorumlamak zorlaşmaktadır. Bu nedenlerden dolayı, sürekli açıklayıcı değişkenlerin

fonsksiyonel yapısının belirlenmesi için kesirli polinomlar kullanılmaktadır. Bu çalışmada kesirli polinomlu

lojistik regresyon modeli incelenmiş ve gerçek bir veri kümesine uygulanmıştır. Doğrusal ilişkili lojistik

regresyon modeli ve kategorik değişkenli lojistik regresyon modeli için de sonuçlar elde edilmiş ve

tartışılmıştır.

Keywords

Doğrusal olmayan ilişki, Kesirli polinomlar, Sürekli bağımsız değişken.

Logistic Regression Model with Fractional Polynomial

Abstract

In logistic regression analysis, there is a linearity assumption between the independent and dependent

variables. The violation of linearity assumption leads to wrong final model. When a continuous variable is

categorized to use in a model, the analysis and interpretation of the model may become difficult especially

with the high number of levels. For these reasons, fractional polynomials are used to determine the functional

structure of the continuous variables. In this study, the fractional polynomials are examined and applied to a

real data set. The results for logistic regression model with an assumption of linearity and logistic regression

model with a categorical independent variable were also obtained and discussed.

Keywords

Nonlinear relationship, Fractional polynomials, Continuous independent variable.

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