[R] lasso constraint

[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 and it is the optimal choice. 

paper name: Regression Shrinkage and Selection via the Lasso 

author: Robert Tibshirani 



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