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 and it is the optimal choice. paper name: Regression Shrinkage and Selection via the Lasso author: Robert Tibshirani -- View this message in context: http://r.789695.n4.nabble.com/lasso-constraint-tp4508998p4508998.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
