Hy, no need to find the best s value. CV does it for you: cvres<-cv.lars(X,Y,K=10,type='lasso') sAtBest<-cvres$fraction[which.min(cvres$cv)] fits <- predict.lars(object, type="coefficients", s=sAtBest, mode="fraction") ... Ciao Bruno
Hello, I have a question about the lars package. I am using this package to get the coefficients at a specific LASSO parameter s. data(diabetes) attach(diabetes) object <- lars(x,y,type="lasso") cvres<-cv.lars(x,y,K=10,fraction = seq(from = 0, to = 1, length = 100)) fits <- predict.lars(object, type="coefficients", s=0.1, mode="fraction") Can I assign automatically the best s value to predict.lars which is given by the CV process (cv.lars)? Or, do I need to manually find the s value that gives the minimum cv value from cv.lars, and assign it as the s value in predict.lars? I would appreciate any advice on this. Thanks, Seungho Huh
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