Ben Bolker
2023-Oct-23 17:58 UTC
[R] running crossvalidation many times MSE for Lasso regression
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.
varin sacha
2023-Oct-23 19:12 UTC
[R] running crossvalidation many times MSE for Lasso regression
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.
Jin Li
2023-Oct-24 05:01 UTC
[R] running crossvalidation many times MSE for Lasso regression
Hi Ben, Martin and all, The function, glmnetcv, in the spm2 package was developed for the following main reasons: 1. The training and testing samples were generated using a stratified random sampling method instead of a simple random sampling method. By doing this, we hoped that it may be able to decluster the spatial data as Ben mentioned and also to reduce the variation in the perdictive accuarcy among iterations and produce a more reliable predictive accuracy. 2. It can be used to produce various prective accuracy measures (e.g., VEcv) as shown in the reproducible examples. 3. We also wanted that all methods compared in Spatial Predictive Modeling with R were based on cv functions that are using the same sampling methods (i.e., a number of cv functions were developed for this purpose), so that we could conclude that the differences in the accuracy of predictive methods were resulted from the methods themselves. Anyway, people interested can use their own data to test and see. Best, Jin On Tue, Oct 24, 2023 at 4:59?AM Ben Bolker <bbolker at gmail.com> wrote:> 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. >-- 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]]
Reasonably Related Threads
- running crossvalidation many times MSE for Lasso regression
- running crossvalidation many times MSE for Lasso regression
- running crossvalidation many times MSE for Lasso regression
- running crossvalidation many times MSE for Lasso regression
- GLMNET AUC vs. MSE