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 <bbol...@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.dun...@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@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
       >> >> 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@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@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@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@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@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@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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