For what it's worth it looks like spm2 is specifically for *spatial* predictive modeling; presumably its version of CV is doing something spatially aware.

I agree that glmnet is old and reliable. One might want to use a tidymodels wrapper to create pipelines where you can more easily switch among predictive algorithms (see the `parsnip` package), but otherwise sticking to glmnet seems wise.

On 2023-10-23 4:38 a.m., Martin Maechler wrote:
Jin Li
     on Mon, 23 Oct 2023 15:42:14 +1100 writes:

     > 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.

... and once you open that can of w..:   the  glmnet package itself
contains a function  cv.glmnet()  which we (our students) use when teaching.

What's the advantage of the spm2 package ?
At least, the glmnet package is authored by the same who originated and
first published (as in "peer reviewed" ..) these algorithms.



     > 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

     > [[alternative HTML version deleted]]

     > ______________________________________________
     > 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
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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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