[R] running crossvalidation many times MSE for Lasso regression
Rui Barradas
ru|pb@rr@d@@ @end|ng |rom @@po@pt
Tue Oct 24 16:37:48 CEST 2023
Às 20:12 de 23/10/2023, varin sacha via R-help escreveu:
> 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.
>
> ______________________________________________
> 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.
Hello,
In your OP, the following two code lines are where that error comes from.
predictLasso=predict(cv_model, newx=test1)
ypred=predict(predictLasso,newdata=test1)
predictLasso already are predictions, it's the output of predict. So
when you run the 2nd line above you are passing it a matrix, not a
fitted model, and the error is thrown.
After the several suggestion in this thread, don't you want something
like this instead of your for loop?
# make the results reproducible
set.seed(2023)
# this is better than what you had
z <- TT[c("x1", "x2")] |> as.matrix()
y <- TT[["y"]]
cv_model <- cv.glmnet(z, y, alpha = 1, type.measure = "mse")
best_lambda <- cv_model$lambda.min
best_lambda
# these two values should be the same, and they are
# index to minimum mse
(i <- cv_model$index[1])
which(cv_model$lambda == cv_model$lambda.min)
# these two values should be the same, and they are
# value of minimum mse
cv_model$cvm[i]
min(cv_model$cvm)
plot(cv_model)
Hope this helps,
Rui Barradas
--
Este e-mail foi analisado pelo software antivírus AVG para verificar a presença de vírus.
www.avg.com
More information about the R-help
mailing list