[R] R.squared in summary.lm with weights
Murray Efford
murray.efford at otago.ac.nz
Sun Apr 10 12:11:33 CEST 2016
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]
________________________________________
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
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