I'd like to pick the optimal lambda and alpha using the Glmnet package. I'm open to all models (Ridge, Lasso, Elastic). I'm assuming some out of sample error/cross validation is the best model selection criteria.
Macro <- read.csv("P:/Earnest/Old/R/Input.csv") x <- Macro[1:13,3:21] x <- as.matrix(x) y <- Macro[1:13,2:2] y <- as.matrix(y) t <- Macro[14:14,3:21] t <- as.matrix(t)
Right now, I'm using the following code. The below code presupposes alpha = .5 (elastic), and that lambda.min is the ideal lambda.
fit <-glmnet(x, y, alpha = .5, lambda = NULL) cv.fit=cv.glmnet(x,y, alpha = .5, lambda = NULL) min <- cv.fit$lambda.min predict(fit ,t, s = min)
Questions: How do I know what is the ideal alpha and lambda? What code can I use to test various lambda/alpha combinations, to find the best out of sample error. Is this the right approach and utilization of Glmnet? What questions am I not considering that should be?