I would have expected the same results (with rescaled lambda sequences)
data(colon); X.bm <- as.big.matrix(colon$X); y <- colon$y
fit.lasso <- biglasso(X.bm, y, family = 'gaussian')
fit.lasso2 <- biglasso(X.bm, y, family = 'gaussian',
penalty.factor = rep(100, ncol(X.bm)))
Looking at the code of {glmnet}, it seems that they rescale the multiplicative factors (by dividing by their mean). Should {biglasso} do the same here?
What is the (implementation) problem with having some penalty factor as 0? (you don't allow unpenalized variables in the current version)
Two questions/remarks:
I would have expected the same results (with rescaled lambda sequences)
Looking at the code of {glmnet}, it seems that they rescale the multiplicative factors (by dividing by their mean). Should {biglasso} do the same here?
What is the (implementation) problem with having some penalty factor as 0? (you don't allow unpenalized variables in the current version)