Dear Rui, I really thank you a lot for your response and your R code.
Best, Sacha Le mardi 24 octobre 2023 à 16:37:56 UTC+2, Rui Barradas <ruipbarra...@sapo.pt> a écrit : 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 -- Este e-mail foi analisado pelo software antivírus AVG para verificar a presença de vírus. www.avg.com ______________________________________________ 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.