# lambda.min in lasso for correlated variable selection

Lasso uses cross-validation to determine both the number of included predictors and the degree of shrinkage to avoid over-fitting. I have used the glmnet package to do this.

    fit=glmnet(x,y)
cvob1= cv.glmnet(x,y)
lambda1=cvob1$lambda.min cvob2=cv.glmnet(x,y) lambda2=cvob2$lambda.min


There is a high possibility that lambda1 and lambda2 are different. I need to get a proper lambda.min for the next step to obtain fitted values for the sparse solution to the model:

coef(fit,s=cvob1$lambda.min)  How can I decide which lambda to use? Should I repeat the cv.glmnet() for many times? How many time should I go for? ## 1 Answer CV is a random process, which is why you are getting different lambda.mins. But they should be close to eachother. If you want a singular, reproducible lambda, try setting a seed:  set.seed(1234)  • would different lambdas affect my next step: coef(fit,s=cvob1$lambda.min) – Zen Mar 14 '18 at 19:52
• Yes, since different lambdas would yield different coefficients. (Remember large $\lambda$, small $\|\beta\|$). – Tim Atreides Mar 15 '18 at 11:13