Re: [R] running crossvalidation many times MSE for Lasso regression
> On Oct 22, 2023, at 4:01 PM, 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.)
>
> -- 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$x2),ncol=2)
>>
>> predictLasso=predict(cv_model, newx=test1)
>>
>>
>> ypred=predict(predictLasso,newdata=test1)
The error I got was:
Error in UseMethod("predict") :
no applicable method for 'predict' applied to an object of class "c('matrix',
'array', 'double', 'numeric')"
I'm not sure why the name of the object was cv_model since it was not created
as a cross-validation result.
The loops called predict() twice and it was the second call that produced the
error since the predictLasso object was not a glmnet classed object.
If the OP had left out the second use of predict and then subtracted
predictLasso from the y vector a result would have appeared
y=T[-sam,]$y
MSE = mean((y-predictLasso)^2)
...
> mean(unlist(lst))
[1] 23.39621
Whether this is meaningful is hard to tell. It also makes the fundamental error
of overwriting the original data object `y` with another intermediate result.
--
David
>> 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.
Re: [R] running crossvalidation many times MSE for Lasso regression
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
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 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))
>> >> >>
>>##
>> >> >>
>> >> >>
>> >> >>
>> >> >>
>> >> >> __
>>
Re: [R] running crossvalidation many times MSE for Lasso regression
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 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
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
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
> >> http:/
Re: [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
> 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&hl=en
> [[alternati
Re: [R] running crossvalidation many times MSE for Lasso regression
> 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&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 h
Re: [R] running crossvalidation many times MSE for Lasso regression
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.
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&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.
Re: [R] running crossvalidation many times MSE for Lasso regression
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,]$y
MSE = mean((y-ypred)^2)
MSE
lst[i]<-MSE
}
mean(unlist(lst))
##
__
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https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
__
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] running crossvalidation many times MSE for Lasso regression
No error message shown Please include the error message so that it is
not necessary to rerun your code. This might enable someone to see the
problem without running the code (e.g. downloading packages, etc.)
-- Bert
On Sun, Oct 22, 2023 at 1:36 PM varin sacha via R-help
wrote:
>
> Dear R-experts,
>
> Here below my R code with an error message. Can somebody help me to fix this
> error?
> Really appreciate your help.
>
> Best,
>
>
> # MSE CROSSVALIDATION Lasso regression
>
> library(glmnet)
>
>
> x1=c(34,35,12,13,15,37,65,45,47,67,87,45,46,39,87,98,67,51,10,30,65,34,57,68,98,86,45,65,34,78,98,123,202,231,154,21,34,26,56,78,99,83,46,58,91)
> x2=c(1,3,2,4,5,6,7,3,8,9,10,11,12,1,3,4,2,3,4,5,4,6,8,7,9,4,3,6,7,9,8,4,7,6,1,3,2,5,6,8,7,1,1,2,9)
> y=c(2,6,5,4,6,7,8,10,11,2,3,1,3,5,4,6,5,3.4,5.6,-2.4,-5.4,5,3,6,5,-3,-5,3,2,-1,-8,5,8,6,9,4,5,-3,-7,-9,-9,8,7,1,2)
> T=data.frame(y,x1,x2)
>
> z=matrix(c(x1,x2), ncol=2)
> cv_model=glmnet(z,y,alpha=1)
> best_lambda=cv_model$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,]$y
>
> MSE = mean((y-ypred)^2)
> MSE
> lst[i]<-MSE
> }
> mean(unlist(lst))
> ##
>
>
>
>
> __
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] running crossvalidation many times MSE for Lasso regression
Dear R-experts,
Here below my R code with an error message. Can somebody help me to fix this
error?
Really appreciate your help.
Best,
# MSE CROSSVALIDATION Lasso regression
library(glmnet)
x1=c(34,35,12,13,15,37,65,45,47,67,87,45,46,39,87,98,67,51,10,30,65,34,57,68,98,86,45,65,34,78,98,123,202,231,154,21,34,26,56,78,99,83,46,58,91)
x2=c(1,3,2,4,5,6,7,3,8,9,10,11,12,1,3,4,2,3,4,5,4,6,8,7,9,4,3,6,7,9,8,4,7,6,1,3,2,5,6,8,7,1,1,2,9)
y=c(2,6,5,4,6,7,8,10,11,2,3,1,3,5,4,6,5,3.4,5.6,-2.4,-5.4,5,3,6,5,-3,-5,3,2,-1,-8,5,8,6,9,4,5,-3,-7,-9,-9,8,7,1,2)
T=data.frame(y,x1,x2)
z=matrix(c(x1,x2), ncol=2)
cv_model=glmnet(z,y,alpha=1)
best_lambda=cv_model$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.
