>>>>> Ben Bolker
>>>>> on Sun, 1 Feb 2026 20:13:59 -0500 writes:
> The weighted.residuals() function has been under development
> recently:
yes, notably to make the subsequent *uses* of
weighted.residuals() in the influence() (*)
related functions dfbeta() [DEFBETAS in the Belsley, Kuh & Welsch etc}
see e.g.
https://stat.ethz.ch/R-manual/R-devel/library/stats/html/influence.measures.html
--
*) leave-one-out influence measures, not really robust, but that's another
story, ...
they are better than no influence measures, and quite widely
used ... well at least before ML and A"I" ..
[read on]
> the current version is
> ## see PR#7961,
> https://stat.ethz.ch/pipermail/r-devel/2011-January/059642.html
> ## (Note, Jan 2026: PR# 7961 suggested use of deviance residuals but
> then dfbeta et al. are
> ## not one-step approximations to leave-one-out coeff.)
> weighted.residuals <- function(obj, drop0 = TRUE)
> {
> # w <- weights(obj, type="working")
> # r <- residuals(obj, type="working")
> w <- naresid(obj$na.action, obj$weights)
> r <- naresid(obj$na.action, obj$residuals)
> if (!is.null(w)) r <- r * sqrt(w)
> if (inherits(obj, "glm")) w <- weights(obj, "prior")
> if(drop0 && !is.null(w)) {
> if(is.matrix(r)) r[w != 0, , drop = FALSE] # e.g. mlm fit
> else r[w != 0]
> } else r
> }
> The previous version was, as per
> https://bugs.r-project.org/show_bug.cgi?id=7961,
> ## see PR#7961
> weighted.residuals <- function(obj, drop0 = TRUE)
> {
> w <- weights(obj)
> r <- residuals(obj, type="deviance")
> if(drop0 && !is.null(w)) r[w != 0] else r
> }
{not quite, there was one more change later; the previous one
being
## see PR#7961, https://stat.ethz.ch/pipermail/r-devel/2011-January/059642.html
weighted.residuals <- function(obj, drop0 = TRUE)
{
w <- weights(obj)
r <- residuals(obj, type="deviance")
if(drop0 && !is.null(w)) {
if(is.matrix(r)) r[w != 0, , drop = FALSE] # e.g. mlm fit
else r[w != 0]
} else r
}
from
------------------------------------------------------------------------
r54046 | ripley | 2011-01-20 | fix weighted.residuals() on a "mlm" object
------------------------------------------------------------------------
However, the "mlm" related bug fix is not relevant to the
current subject.
> The problem with the current version is that it only works with
> list-like objects that have $na.action, $weights, and $residuals
> components. Among other things
> * lme4's merMod objects, which are objects with an S4 class, throw an
> error (because you can't access an S4 object element with $; even if you
> used getElement() you'd get an error because those slots don't exist).
> * glmmTMB objects return NULL (they're list-like but don't have the
> appropriate elements).
> A robust solution would use na.action(), residuals(), weights()
> accessor methods. However, we can't count on working weights being the
> default type, and some residuals.* or weights.* methods might not have
> type="working" allowed (an intermediate version of weighted.residuals()
> used weights(., type = "working"); lme4 throws an error for type =
> "working" with GLMMs ... glmmTMB takes a type argument but ignores it ...
As weighted.residuals() did use weights() and residuals() and
these are generic,
I think we should try to cooperate (with the relevant packages
authors) to agree that weights() and residuals() methods should _obey_ `type =
<string>`
settings in the sense to at least *work* for some of these.
{at least such as {glmmTMB}'s which seems to ignore them ..}
The reasoning would be that at least GLM-like models (which
would include some GLMM , GAM, probably) should provide these,
and users could use the influence measures (mentioned in the
above help page) for their models.
> Another solution to this would be to make weighted.residuals() an S3
> method and let other packages provide a method if they liked ...
yes.. that would be an alternative, more modular (but with extra
hassles, notably not ensuring at all that subsequent use of
rstudent(), dfbetas(), .... would be valid.
Martin
> Thoughts welcome.
> Ben Bolker
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