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-- 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
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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.
______________________________________________
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.
