> On Apr 10, 2016, at 3:11 AM, Murray Efford <murray.efford at otago.ac.nz> wrote: > > Martin - > Thanks, but although hatvalues() is useful for calculating PRESS, I can't find anything directly relevant to my question in the influence help pages. After some burrowing in the literature I'm doubting there is an answer out there (PRESS R^2 is always presented in a fairly ad hoc way). > This is a new topic, as you say, and perhaps better handled on a statistics list. > Murray Efford > > [BTW > stats ::: influence.lm > just gets me > function (model, do.coef = TRUE, ...) > lm.influence(model, do.coef = do.coef, ...) > <bytecode: 0x00000000081023b8> > <environment: namespace:stats> > which is not very helpful]influence.lm is just saying you should be looking at lm.influence #Try typing: lm.influence Admittedly the meat of that function is probably encapsulated in C with the results delivered by: res <- .Call(C_influence, mqr, do.coef, e, tol) Perhaps looking at: https://svn.r-project.org/R/trunk/src/library/stats/src/influence.c I haven't been following the rest of the thread so this is just commenting on your difficulties reading R code. -- David.> > ________________________________________ > From: Martin Maechler <maechler at stat.math.ethz.ch> > Sent: Sunday, 10 April 2016 4:07 a.m. > To: Murray Efford > Cc: peter dalgaard; Duncan Murdoch; r-help at r-project.org > Subject: Re: [R] R.squared in summary.lm with weights > >>>>>> Murray Efford <murray.efford at otago.ac.nz> >>>>>> on Fri, 8 Apr 2016 18:45:33 +0000 writes: > >> Thanks for these perfectly consistent replies - I didn't >> understand the purpose of m = sum(w * f/sum(w)) and saw it >> merely as a weighted average of the fitted values. My >> ultimate concern is how to compute an appropriate weighted >> TSS (or equivalently, MSS) for PRESS-R^2 = 1 - PRESS/TSS >> 1 - PRESS/ (MSS + PRESS). Do you think it then makes sense >> to substitute the vector of leave-one-out fitted values >> for f here? > > --> A new topic really. > > I think you should find the answer on the help pages (and in the > source) of > > ? influence.measures (which documents a host of such functions) > and > ? influence > > Note that influence is S3 generic and > > methods(influence) > > indicates that the 'lm' and 'glm' methods are hidden. > Of course I do recommend reading the real R source code (which > also contains the comments and has some logical order in all the > function definitions), > but you can use stats ::: influence.lm > to show a version of the function that looks not too different > from the source. > > Martin Maechler, ETH Zurich > > > ______________________________________________ > 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.David Winsemius Alameda, CA, USA
> On Apr 10, 2016, at 9:38 AM, David Winsemius <dwinsemius at comcast.net> wrote: > >> >> On Apr 10, 2016, at 3:11 AM, Murray Efford <murray.efford at otago.ac.nz> wrote: >> >> Martin - >> Thanks, but although hatvalues() is useful for calculating PRESS, I can't find anything directly relevant to my question in the influence help pages. After some burrowing in the literature I'm doubting there is an answer out there (PRESS R^2 is always presented in a fairly ad hoc way). >> This is a new topic, as you say, and perhaps better handled on a statistics list. >> Murray Efford >> >> [BTW >> stats ::: influence.lm >> just gets me >> function (model, do.coef = TRUE, ...) >> lm.influence(model, do.coef = do.coef, ...) >> <bytecode: 0x00000000081023b8> >> <environment: namespace:stats> >> which is not very helpful] > > influence.lm is just saying you should be looking at lm.influence > > #Try typing: > lm.influence > > Admittedly the meat of that function is probably encapsulated in C with the results delivered by: > > res <- .Call(C_influence, mqr, do.coef, e, tol) > > Perhaps looking at: > > https://svn.r-project.org/R/trunk/src/library/stats/src/influence.c > > > I haven't been following the rest of the thread so this is just commenting on your difficulties reading R code.When I do attempt filling in blanks in my knowledge regarding PRESS, I am reminded by MarkMail that this question came up 5-6 years ago and I went looking for an answer: http://markmail.org/search/?q=list%3Aorg.r-project.r-help+PRESS#query:list%3Aorg.r-project.r-help%20PRESS+page:1+mid:k2mbz5sov5eo5ejw+state:results I also see that other packages have implemented PRESS at least as reported by others: Subject: [R] I need help computing PRESS statistics (qpcR package) of...: From: Francisco Goes (xico... at hotmail.com) Date: Jun 4, 2014 4:05:03 pm I tried a current search, although I admit that the fact that "press" is an acronym shared by other topics does seem to complicate that process. I counted 9 packages with PRESS functions even after excluding the ones related to "the Press" and "protein residues" when I did a search with: sos::findFn("PRESS") -- David.> > -- > > David. > > >> >> ________________________________________ >> From: Martin Maechler <maechler at stat.math.ethz.ch> >> Sent: Sunday, 10 April 2016 4:07 a.m. >> To: Murray Efford >> Cc: peter dalgaard; Duncan Murdoch; r-help at r-project.org >> Subject: Re: [R] R.squared in summary.lm with weights >> >>>>>>> Murray Efford <murray.efford at otago.ac.nz> >>>>>>> on Fri, 8 Apr 2016 18:45:33 +0000 writes: >> >>> Thanks for these perfectly consistent replies - I didn't >>> understand the purpose of m = sum(w * f/sum(w)) and saw it >>> merely as a weighted average of the fitted values. My >>> ultimate concern is how to compute an appropriate weighted >>> TSS (or equivalently, MSS) for PRESS-R^2 = 1 - PRESS/TSS >>> 1 - PRESS/ (MSS + PRESS). Do you think it then makes sense >>> to substitute the vector of leave-one-out fitted values >>> for f here? >> >> --> A new topic really. >> >> I think you should find the answer on the help pages (and in the >> source) of >> >> ? influence.measures (which documents a host of such functions) >> and >> ? influence >> >> Note that influence is S3 generic and >> >> methods(influence) >> >> indicates that the 'lm' and 'glm' methods are hidden. >> Of course I do recommend reading the real R source code (which >> also contains the comments and has some logical order in all the >> function definitions), >> but you can use stats ::: influence.lm >> to show a version of the function that looks not too different >> from the source. >> >> Martin Maechler, ETH Zurich >> >> >> ______________________________________________ >> 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. > > David Winsemius > Alameda, CA, USA > > ______________________________________________ > 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.David Winsemius Alameda, CA, USA
Among the 6547 matches for 'PRESS' in an sos search I find 7 packages
(asbio, DAAG, qpcR, CombMSC, rknn, MPV, mixlm) with a relevant 'press'
or 'PRESS' function. Of these only qpcR (PRESS), mixlm (R2pred), and
rknn (rqsp) attempt to calculate PRESS R^2, as far as I can tell. None of these
confronts the possibility of weights explicitly.
By way of example:
## generate a simple dataset and fit weighted regression
x <- 1:20
df <- data.frame(x = x, y = 2*x + 10* rnorm(20), wt = runif(20) * 10)
fitwt <- lm(y~x, data = df, weights = wt)
fitnowt <- lm(y~x, data = df)
## apply PRESS R^2 methods from 3 packages
otherpressR2 <- function (fit) {
require(mixlm)
require(qpcR)
require(rknn)
c(qpcR = qpcR::PRESS(fit, verbose = FALSE)$P.square,
mixlm = mixlm::R2pred(fit),
rknn = rknn::rsqp(fit))
}
otherpressR2(fitwt)
# qpcR mixlm rknn
# 0.42865 0.56124 0.56124
# There were 21 warnings (use warnings() to see them)
otherpressR2(fitnowt)
# qpcR mixlm rknn
# 0.59391 0.59391 0.59391
# There were 21 warnings (use warnings() to see them)
(The warnings from qpcR are not material).
Two different versions of PRESS-R^2 are implemented, and I see no reason to
trust either version in the case of weighted regression. The key issue is the
appropriate calculation of MSS or TSS as indicated before.
Murray Efford
________________________________________
From: David Winsemius <dwinsemius at comcast.net>
Sent: Monday, 11 April 2016 5:31 a.m.
To: Murray Efford
Cc: r-help at r-project.org; Martin Maechler; peter dalgaard
Subject: Re: [R] R.squared in summary.lm with weights
> On Apr 10, 2016, at 9:38 AM, David Winsemius <dwinsemius at
comcast.net> wrote:
>
>>
>> On Apr 10, 2016, at 3:11 AM, Murray Efford <murray.efford at
otago.ac.nz> wrote:
>>
>> Martin -
>> Thanks, but although hatvalues() is useful for calculating PRESS, I
can't find anything directly relevant to my question in the influence help
pages. After some burrowing in the literature I'm doubting there is an
answer out there (PRESS R^2 is always presented in a fairly ad hoc way).
>> This is a new topic, as you say, and perhaps better handled on a
statistics list.
>> Murray Efford
>>
>> [BTW
>> stats ::: influence.lm
>> just gets me
>> function (model, do.coef = TRUE, ...)
>> lm.influence(model, do.coef = do.coef, ...)
>> <bytecode: 0x00000000081023b8>
>> <environment: namespace:stats>
>> which is not very helpful]
>
> influence.lm is just saying you should be looking at lm.influence
>
> #Try typing:
> lm.influence
>
> Admittedly the meat of that function is probably encapsulated in C with the
results delivered by:
>
> res <- .Call(C_influence, mqr, do.coef, e, tol)
>
> Perhaps looking at:
>
> https://svn.r-project.org/R/trunk/src/library/stats/src/influence.c
>
>
> I haven't been following the rest of the thread so this is just
commenting on your difficulties reading R code.
When I do attempt filling in blanks in my knowledge regarding PRESS, I am
reminded by MarkMail that this question came up 5-6 years ago and I went looking
for an answer:
http://markmail.org/search/?q=list%3Aorg.r-project.r-help+PRESS#query:list%3Aorg.r-project.r-help%20PRESS+page:1+mid:k2mbz5sov5eo5ejw+state:results
I also see that other packages have implemented PRESS at least as reported by
others:
Subject: [R] I need help computing PRESS statistics (qpcR package) of...:
From: Francisco Goes (xico... at hotmail.com)
Date: Jun 4, 2014 4:05:03 pm
I tried a current search, although I admit that the fact that "press"
is an acronym shared by other topics does seem to complicate that process. I
counted 9 packages with PRESS functions even after excluding the ones related to
"the Press" and "protein residues" when I did a search with:
sos::findFn("PRESS")
--
David.
>
> --
>
> David.
>
>
>>
>> ________________________________________
>> From: Martin Maechler <maechler at stat.math.ethz.ch>
>> Sent: Sunday, 10 April 2016 4:07 a.m.
>> To: Murray Efford
>> Cc: peter dalgaard; Duncan Murdoch; r-help at r-project.org
>> Subject: Re: [R] R.squared in summary.lm with weights
>>
>>>>>>> Murray Efford <murray.efford at otago.ac.nz>
>>>>>>> on Fri, 8 Apr 2016 18:45:33 +0000 writes:
>>
>>> Thanks for these perfectly consistent replies - I didn't
>>> understand the purpose of m = sum(w * f/sum(w)) and saw it
>>> merely as a weighted average of the fitted values. My
>>> ultimate concern is how to compute an appropriate weighted
>>> TSS (or equivalently, MSS) for PRESS-R^2 = 1 - PRESS/TSS
>>> 1 - PRESS/ (MSS + PRESS). Do you think it then makes sense
>>> to substitute the vector of leave-one-out fitted values
>>> for f here?
>>
>> --> A new topic really.
>>
>> I think you should find the answer on the help pages (and in the
>> source) of
>>
>> ? influence.measures (which documents a host of such functions)
>> and
>> ? influence
>>
>> Note that influence is S3 generic and
>>
>> methods(influence)
>>
>> indicates that the 'lm' and 'glm' methods are hidden.
>> Of course I do recommend reading the real R source code (which
>> also contains the comments and has some logical order in all the
>> function definitions),
>> but you can use stats ::: influence.lm
>> to show a version of the function that looks not too different
>> from the source.
>>
>> Martin Maechler, ETH Zurich
>>
>>
>> ______________________________________________
>> 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.
>
> David Winsemius
> Alameda, CA, USA
>
> ______________________________________________
> 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.
David Winsemius
Alameda, CA, USA