date:20231023

Re: [R] running crossvalidation many times MSE for Lasso regression

2023-10-23 Thread Jin Li

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  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.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
> >  >> >  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))
> >

Re: [R] running crossvalidation many times MSE for Lasso regression

2023-10-23 Thread varin sacha via R-help

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  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 
>
>      > 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
>      >> >  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
>      >>

Re: [R] running crossvalidation many times MSE for Lasso regression

2023-10-23 Thread Ben Bolker

  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 

 > 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
 >> >  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=Jeot53EJ=en

 > [[alternative

Re: [R] Technical Help Request for "Version Differences" (i.e., CYTOFKIT package)

2023-10-23 Thread Ivan Krylov

Dear Nader Habibzadeh,

В Sat, 21 Oct 2023 15:11:23 +0330
Nader Habibzadeh  пишет:

> I came across the same issue you have demonstrated in "r-help" but
> with the shiny package installation.

Do any of the replies to Murat's question, e.g. [1], help?

If you have a different problem, let us know what exactly it looks like
[2] and we'll try to help.

[1]: https://stat.ethz.ch/pipermail/r-help/2023-August/477830.html

[2]: https://www.chiark.greenend.org.uk/~sgtatham/bugs.html

-- 
Best regards,
Ivan

__
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.

Re: [R] running crossvalidation many times MSE for Lasso regression

2023-10-23 Thread Martin Maechler

> 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 
> 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
>> >  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=Jeot53EJ=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

[R] Technical Help Request for "Version Differences" (i.e., CYTOFKIT package)

2023-10-23 Thread Nader Habibzadeh

Dear Murat,
I came across the same issue you have demonstrated in "r-help" but with the
shiny package installation. I was wondering if you could send me any
information that you have gotten for figuring out this problem.
Best regards,
Nader

-- 
*Nader Habibzadeh, Ph.D.,*
*Department of Environmental Science,*
*Tabriz Branch, I.A.U.,*
*Tel: +98(914)312-9524*
*https://www.researchgate.net/profile/Nader_Habibzadeh
*
*https://megadata.shinyapps.io/stats/
*

[[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.

Re: [R] running crossvalidation many times MSE for Lasso regression

Re: [R] running crossvalidation many times MSE for Lasso regression

Re: [R] running crossvalidation many times MSE for Lasso regression

Re: [R] Technical Help Request for "Version Differences" (i.e., CYTOFKIT package)

Re: [R] running crossvalidation many times MSE for Lasso regression

[R] Technical Help Request for "Version Differences" (i.e., CYTOFKIT package)

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