R help - Jan 2012 - calibration curve for glmnet object

Hi,

I created a logistic regression model using the glmnet package. This model is of
class "glmnet" or "lognet". I wanted to plot a calibration
curve for this model using the calibrate() function from rms package, but the
objects used are different, rms requires a fit from lrm(). Is there another
function for getting the calibration plot for this glmnet object, or can anyone
drop some hints as to how to get it done?


Thanks for your help,

Dave.
 		 	   		  
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Ok, so just for anyone's interest, I managed to create the calibration plot
for the glmnet object using the val.prob() function from the rms package.

Now, my question moves slightly, how can I superimpose calibration curves from
two models, so that they can be graphically compared?

This is what I have tried. I start with two models, based on same predictors:
rms_fit and enet_fit
>validate(rms_fit,B=100)
>cal<-calibrate(rms_fit,B=100)
>
mypred<-predict(enet_fit,type="response",s=lambda.min,newx=myDesignMatrix[1:703,])
#get probabilities for training set
> val.prob(mypred, as.numeric(out.v), m=20, cex=.5) #out.v is a vector of
outcomes for each sample
>par(new=TRUE)
>plot(cal)
would superimpose both graphs completely, including labels and axis.

Also tried doing:
> val.prob(mypred, as.numeric(out.v), m=20, cex=.5) #out.v is a vector of
outcomes for each sample
>lines(plot(cal))
but this creates a new graph window for cal object.


Also, how can I colour them differently..

Thanks in advance,

Dave


 		 	   		  
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