# LASSO: optimal $\lambda$ drops all predictors from model

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.

• I chose not to vote to close because, although the OP phrases this as a software/convergence issue, it is in fact about a situation where the regularization drops all the predictors from the model, which is an issue not special to any particular software. I rephrased the question title to be consistent with this. – Jake Westfall Jun 8 '17 at 21:05

• But giving minimum, $\lambda$ at the extreme value all the time (with any fixed seed) means there is some problem. In fact for some seed when I use LASSO' it gives some warning which says for some value of $\lambda$ it is not converged. – Sedi Jun 8 '17 at 18:35