[R] Logistic Regression - Variable Selection Methods With Prediction

[RAJ]

Hello,

I am pretty new to R, I have always used SAS and SAS products. My
target variable is binary ('Y' and 'N') and i have about 14 predictor
variables. My goal is to compare different variable selection methods
like Forward, Backward, All possible subsests. I am using
misclassification rate to pick the winner method.

This is what i have as of now,

Reg <- glm (Graduation ~., DFtrain,family=binomial(link="logit"))
		step <- extractAIC(Reg, direction="forward")
		pred <- predict(Reg, DFtest,type="response")
		mis <- mean({pred > 0.5} != {DFtest[,"Graduation"] == "Y"})
This program actually works but I needed to check to make sure am
doing this right. Also, I am getting the same misclassification rates
for all different methods.

I also tried to use

Reg <- leaps(Graduation ~., DFtrain)
		pred <- predict(Reg, DFtest,type="response")
		mis <- mean({pred > 0.5} != {DFtest[,"Graduation"] == "Y"})
		#print(summary(mis))
which doesnt work

and

Reg <- regsubsets(Graduation ~., DFtrain)
		pred <- predict(Reg, DFtest,type="response")
		mis <- mean({pred > 0.5} != {DFtest[,"Graduation"] == "Y"})
		#print(summary(mis))

The Regsubsets will work but the 'predict' function does not work with
it. Is there any other way to do predictions when using regsubsets

Any help is appreciated.

Thanks,