R help - Oct 2023 - running crossvalidation many times MSE for Lasso regression

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))
##################################################################?
?
?
?

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.)

-- 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))
> ##################################################################
>
>
>
>
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