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:
How can I decide which lambda to use? Should I repeat the cv.glmnet() for many times? How many time should I go for?