[R] running crossvalidation many times MSE for Lasso regression
varin sacha
v@r|n@@ch@ @end|ng |rom y@hoo@|r
Mon Oct 23 21:12:14 CEST 2023
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 à 19:59:15 UTC+2, Ben Bolker <bbolker using gmail.com> a écrit :
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 using 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 using 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 using 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 using 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 using 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 using 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 using 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.
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