
In the package lasso2, there is a Prostate Data. To find coefficients in the prostate cancer example we could impose L1 constraint on the parameters.
code is:
data(Prostate)
p.mean < apply(Prostate, 5,mean)
pros < sweep(Prostate, 5, p.mean, "")
p.std < apply(pros, 5, var)
pros < sweep(pros, 5, sqrt(p.std),"/")
pros[, "lpsa"] < Prostate[, "lpsa"]
l1ce(lpsa ~ . , pros, bound = 0.44)
I can't figure out what dose 0.44 come from. On the paper it said it was from generalized crossvalidation
paper name: Regression Shrinkage and Selection via the Lasso
author: Robert Tibshirani
