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Hi, I am trying to use the glmnet package to do some simple feature selection. However, I would ideally like to be able to specify the number of features to return (the glmnet package, as far as I can tell, only allows specification of a regularization parameter, lambda, that in turn returns a model with a specific number of non-zero features). Is there a straightforward way of calculating the

Dear R-users I'm trying to use lasso in lars package for subset regression, I have a large matrix of size 1000x100 and my aim is to select a subset k of the 100 variables. Is there any way in lars to fix the number k (i.e. to select the best 10 variables) library(lars) aa=lars(X,Y,type="lasso",max.steps=200) plot(aa,plottype="Cp") aa$RSS which.min(aa$RSS)