R help - Oct 2011 - Logistic Regression - Variable Selection Methods With Prediction

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,

Can I atleast get help with what pacakge to use for logistic
regression with all possible models and do prediction. I know i can
use regsubsets but i am not sure if it has any prediction functions to
go with it.

Thanks

On Oct 25, 6:54?pm, RAJ <dheerajathr... at gmail.com>
wrote:
> 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,
>
> ______________________________________________
> R-h... at r-project.org mailing
listhttps://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guidehttp://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

Try the glm package

Steve Friedman Ph. D.
Ecologist  / Spatial Statistical Analyst
Everglades and Dry Tortugas National Park
950 N Krome Ave (3rd Floor)
Homestead, Florida 33034

Steve_Friedman at nps.gov
Office (305) 224 - 4282
Fax     (305) 224 - 4147