[R] LASSO: glmpath and cv.glmpath

[Peter Schüffler]

Hi,

perhaps you can help me to find out, how to find the best Lambda in a 
LASSO-model.

I have a feature selection problem with 150 proteins potentially 
predicting Cancer or Noncancer. With a lasso model

fit.glm <- glmpath(x=as.matrix(X), y=target, family="binomial")

(target is 0, 1 <- Cancer non cancer, X the proteins, numerical in 
expression), I get following path (PICTURE 1)
One of these models is the best, according to its crossvalidation 
(PICTURE 2), the red line corresponds to the best crossvalidation. Its 
produced by

cv <- cv.glmpath(x=as.matrix(X), y=unclass(T)-1, family="binomial", type 
="response", plot.it=TRUE, se=TRUE)
abline(v= cv$fraction[max(which(cv$cv.error==min(cv$cv.error)))], 
col="red", lty=2, lwd=3)


Does anyone know, how to conclude from the Normfraction in PICTURE 2 to 
the corresponding model in PICTURE 1? What is the best model? Which 
coefficients does it have? I can only see the best model's cross 
validation error, but not the actual model. How to see it?

Thank you,

Peter



PICTURE 1:


PICTURE 2: