running crossvalidation many times MSE for Lasso regression

Dear R-experts,

I really thank you all a lot for your responses. So, here is the error (and warning) messages at the end of my R code.

Many thanks for your help.


Error in UseMethod("predict") :
 ? no applicable method for 'predict' applied to an object of class "c('matrix', 'array', 'double', 'numeric')"

mean(unlist(lst))

[1] NA
Warning message:
In mean.default(unlist(lst)) :
 ? argument is not numeric or logical: returning NA








Le lundi 23 octobre 2023 ? 19:59:15 UTC+2, Ben Bolker <bbolker at gmail.com> a ?crit :





 ? 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.duncan at 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 at 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))
 ? ? ? >> >> ##################################################################
 ? ? ? >> >>
 ? ? ? >> >>
 ? ? ? >> >>
 ? ? ? >> >>
 ? ? ? >> >> ______________________________________________

 ? ? ? > ______________________________________________