I have survival data and large numbers of predictors. I am trying to use
LASSO' orelastic net' in package `glmnet' in R to select appropriate covariates.
I use following code:
cv.glmnet(x, y, family="cox",alpha=0.5,nfolds=20, grouped=TRUE) # for Elastic net and
cv.glmnet(x, y, family = "cox",alpha=1, nfolds=20, grouped=TRUE) # For LASSO
Where $x$ is the matrix of predictors and $y$ is containing two columns of survival time and censoring status. I don't get any Error or warning, but it seems there is some convergence issue as it gives the minimum value of $\lambda$ at the extreme and using that $\lambda$, it choose no predictor. Is there any way to improve this code to get the optimum value of $\lambda$? Or is there any other way which I can have `LASSO' and 'Elastic net' together and compare them?
Note: I have checked with `nfold'=10 and get the same result.