I have generated a glm model with 20 or so predictors. I have carried out stepwise regression(forward and backwards selection) to identify the important predictor variables. My final model has 7 predictor variables. The results are below:
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.888112 0.859847 -9.174 < 2e-16 ***
age 0.028529 0.009212 3.097 0.00196 **
bmi 0.095759 0.015265 6.273 3.53e-10 ***
surgery11 0.923723 0.524588 1.761 0.07826 .
surgery21 1.607389 0.600113 2.678 0.00740 **
surgery31 1.544822 0.573972 2.691 0.00711 **
cvd1 0.624692 0.290005 2.154 0.03123 *
rt_1 -0.816374 0.353953 -2.306 0.02109 *
I want to see if multi-colinearity exists, so I have used the VIF function from the car package. My understanding is that VIF is used for linear models, so I was wondering whether it can be used in glm (logistic) models?
However, I am unsure whether VIF can be applied to a logistic model? I have the results below for VIF:
vif(model_logistic)
age bmi surgery1 surgery2
1.046694 1.008971 6.256793 3.658226
surgery_3 cvd rt_1
4.660840 1.038339 1.144582
