Hi, your code has errors: apply function only has 1 or 2 as margin.
bound is used as turning parameter for summation of absolute coefficients. lasso runs on a grid of the turning parameter for varying strength of shrinkage. so each turning value may yield different sets of coefficients and values. cross validation is used to estimate the value of the turning parameter which gives the smallest errors (mse or deviance) on testing data. Weidong Gu On Tue, Mar 27, 2012 at 10:35 AM, yx78 <[email protected]> wrote: > 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. > > ______________________________________________ > [email protected] 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. ______________________________________________ [email protected] 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.
