Message-ID: <ac848344-0c92-46be-9fc7-a0886bff7fd6@sapo.pt>
Date: 2023-10-24T14:37:48Z
From: Rui Barradas
Subject: running crossvalidation many times MSE for Lasso regression
In-Reply-To: <722137161.2165045.1698088334367@mail.yahoo.com>
?s 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 ? 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))
>> ? ? ? >> >> ##################################################################
>> ? ? ? >> >>
>> ? ? ? >> >>
>> ? ? ? >> >>
>> ? ? ? >> >>
>> ? ? ? >> >> ______________________________________________
>> ? ? ? >> >> R-help at 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 at 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 at 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 at 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 at 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 at 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 at 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
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