If you are interested in other validation methods (e.g., LOO or n-fold)
with more predictive accuracy measures, the function, glmnetcv, in the spm2
package can be directly used, and some reproducible examples are
also available in ?glmnetcv.
On Mon, Oct 23, 2023 at 10:59 AM Duncan Murdoch <murdoch.dun...@gmail.com>
wrote:
> On 22/10/2023 7:01 p.m., Bert Gunter wrote:
> > No error message shown Please include the error message so that it is
> > not necessary to rerun your code. This might enable someone to see the
> > problem without running the code (e.g. downloading packages, etc.)
>
> And it's not necessarily true that someone else would see the same error
> message.
>
> Duncan Murdoch
>
> >
> > -- Bert
> >
> > On Sun, Oct 22, 2023 at 1:36 PM varin sacha via R-help
> > <r-help@r-project.org> wrote:
> >>
> >> Dear R-experts,
> >>
> >> Here below my R code with an error message. Can somebody help me to fix
> this error?
> >> Really appreciate your help.
> >>
> >> Best,
> >>
> >> ############################################################
> >> # MSE CROSSVALIDATION Lasso regression
> >>
> >> library(glmnet)
> >>
> >>
> >>
> x1=c(34,35,12,13,15,37,65,45,47,67,87,45,46,39,87,98,67,51,10,30,65,34,57,68,98,86,45,65,34,78,98,123,202,231,154,21,34,26,56,78,99,83,46,58,91)
> >>
> x2=c(1,3,2,4,5,6,7,3,8,9,10,11,12,1,3,4,2,3,4,5,4,6,8,7,9,4,3,6,7,9,8,4,7,6,1,3,2,5,6,8,7,1,1,2,9)
> >>
> y=c(2,6,5,4,6,7,8,10,11,2,3,1,3,5,4,6,5,3.4,5.6,-2.4,-5.4,5,3,6,5,-3,-5,3,2,-1,-8,5,8,6,9,4,5,-3,-7,-9,-9,8,7,1,2)
> >> T=data.frame(y,x1,x2)
> >>
> >> z=matrix(c(x1,x2), ncol=2)
> >> cv_model=glmnet(z,y,alpha=1)
> >> best_lambda=cv_model$lambda.min
> >> best_lambda
> >>
> >>
> >> # Create a list to store the results
> >> lst<-list()
> >>
> >> # This statement does the repetitions (looping)
> >> for(i in 1 :1000) {
> >>
> >> n=45
> >>
> >> p=0.667
> >>
> >> sam=sample(1 :n,floor(p*n),replace=FALSE)
> >>
> >> Training =T [sam,]
> >> Testing = T [-sam,]
> >>
> >> test1=matrix(c(Testing$x1,Testing$x2),ncol=2)
> >>
> >> predictLasso=predict(cv_model, newx=test1)
> >>
> >>
> >> ypred=predict(predictLasso,newdata=test1)
> >> y=T[-sam,]$y
> >>
> >> MSE = mean((y-ypred)^2)
> >> MSE
> >> lst[i]<-MSE
> >> }
> >> mean(unlist(lst))
> >> ##################################################################
> >>
> >>
> >>
> >>
> >> ______________________________________________
> >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> >> 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.
> >
> > ______________________________________________
> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > 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.
>
> ______________________________________________
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> 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.
>
--
Jin
------------------------------------------
Jin Li, PhD
Founder, Data2action, Australia
https://www.researchgate.net/profile/Jin_Li32
https://scholar.google.com/citations?user=Jeot53EAAAAJ&hl=en
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R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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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.
