relative L1 bound

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relative L1 bound

yx78
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

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Re: relative L1 bound

yx78
by the way  0.44 is the optimal choice of lasso constrain.