Hy,
no need to find the best s value. CV does it for you:
cvres<-cv.lars(X,Y,K=10,type='lasso')
sAtBest<-cvres$fraction[which.min(cvres$cv)]
fits <- predict.lars(object, type="coefficients", s=sAtBest,
mode="fraction")
...
Ciao
Bruno
> Hello,
>
> I have a question about the lars package. I am using this package to get
the coefficients at a specific LASSO parameter s.
>
>
> data(diabetes)
> attach(diabetes)
>
> object <- lars(x,y,type="lasso")
>
> cvres<-cv.lars(x,y,K=10,fraction = seq(from = 0, to = 1, length = 100))
>
> fits <- predict.lars(object, type="coefficients", s=0.1,
mode="fraction")
>
>
> Can I assign automatically the best s value to predict.lars which is given
by the CV process (cv.lars)? Or, do I need to manually find the s value that
gives the minimum cv value from cv.lars, and assign it as the s value in
predict.lars?
>
> I would appreciate any advice on this. Thanks,
> Seungho Huh
>
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> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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