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 cross-validation paper name: Regression Shrinkage and Selection via the Lasso author: Robert Tibshirani -- View this message in context: http://r.789695.n4.nabble.com/relative-L1-bound-tp4504620p4504620.html Sent from the R help mailing list archive at Nabble.com.