FWIW p. 206 of the White Book gives the following for
names(binomial()): family, names, link, inverse, deriv, initialize,
variance, deviance, weight.
So $aic wasn't there In The Beginning. I haven't done any more
archaeology to try to figure out when/by whom it was first introduced
...
Section 6.3.3, on extending families, doesn't give any other relevant info.
A patch for src/library/stats/man/family.Rd below: please check what
I've said about $aic and $mu.eta to make sure they're correct! I can
submit this to the r bug list if preferred.
----
Index: family.Rd
===================================================================
--- family.Rd (revision 74904)
+++ family.Rd (working copy)
@@ -31,7 +31,7 @@
\arguments{
\item{link}{a specification for the model link function. This can be
a name/expression, a literal character string, a length-one character
- vector or an object of class
+ vector, or an object of class
\code{"\link[=make.link]{link-glm}"} (such as generated by
\code{\link{make.link}}) provided it is not specified
\emph{via} one of the standard names given next.
@@ -45,7 +45,7 @@
the \code{Gamma} family the links \code{inverse}, \code{identity}
and \code{log};
the \code{poisson} family the links \code{log}, \code{identity},
- and \code{sqrt} and the \code{inverse.gaussian} family the links
+ and \code{sqrt}; and the \code{inverse.gaussian} family the links
\code{1/mu^2}, \code{inverse}, \code{identity}
and \code{log}.
@@ -105,8 +105,8 @@
\note{
The \code{link} and \code{variance} arguments have rather awkward
semantics for back-compatibility. The recommended way is to supply
- them is as quoted character strings, but they can also be supplied
- unquoted (as names or expressions). In addition, they can also be
+ them as quoted character strings, but they can also be supplied
+ unquoted (as names or expressions). Additionally, they can be
supplied as a length-one character vector giving the name of one of
the options, or as a list (for \code{link}, of class
\code{"link-glm"}). The restrictions apply only to links given as
@@ -130,10 +130,18 @@
\item{dev.resids}{function giving the deviance residuals as a function
of \code{(y, mu, wt)}.}
\item{aic}{function giving the AIC value if appropriate (but \code{NA}
- for the quasi- families). See \code{\link{logLik}} for the assumptions
- made about the dispersion parameter.}
- \item{mu.eta}{function: derivative \code{function(eta)}
- \eqn{d\mu/d\eta}.}
+ for the quasi- families). More precisely, this function
+ returns \eqn{-2 L + 2 s}, where \eqn{L} is the log-likelihood and \eqn{s}
+ is the number of estimated scale parameters; the penalty term for the
+ location parameters is added elsewhere.
+ See \code{\link{logLik}} for the assumptions
+ made about the dispersion parameter.}
+ \item{mu.eta}{function: derivative of the inverse-link function
+ with respect to the linear predictor. If the inverse-link
+ function is \eqn{\mu=g^{-1}(\eta)}{mu=ginv(eta)}
+ where \eqn{eta}{\eta} is the value
+ of the linear predictor, then this function returns
+ \eqn{d(g^{-1})/d\eta=d\mu/d\eta}{d(ginv(eta))/d(eta)=d(mu)/d(eta)}.}
\item{initialize}{expression. This needs to set up whatever data
objects are needed for the family as well as \code{n} (needed for
AIC in the binomial family) and \code{mustart} (see \code{\link{glm}}).}
@@ -224,8 +232,8 @@
## which case use an offset of 0 in the corresponding formula
## to get the null deviance right.
-## Binomial with identity link: often not a good idea.
-\dontrun{binomial(link = make.link("identity"))}
+## Binomial with identity link: often computationally and
conceptually difficult
+\dontrun{binomial(link = "identity")}
## tests of quasi
x <- rnorm(100)
@@ -236,7 +244,7 @@
glm(y ~ x, family = quasi(variance = "mu^2", link = "log"))
\dontrun{glm(y ~ x, family = quasi(variance = "mu^3", link = "log")) # fails}
y <- rbinom(100, 1, plogis(x))
-# needs to set a starting value for the next fit
+# need to set a starting value for the next fit
glm(y ~ x, family = quasi(variance = "mu(1-mu)", link = "logit"),
start = c(0,1))
}
\keyword{models}
On Mon, Jun 4, 2018 at 10:46 AM, Martin Maechler
<maech...@stat.math.ethz.ch> wrote:
>>>>>> Ben Bolker
>>>>>> on Sun, 3 Jun 2018 17:33:18 -0400 writes:
>
> > Is it generally known/has it been previously discussed here that the
> > $aic() component in GLM-family objects (e.g. results of binomial(),
> > poisson(), etc.) does not as implemented actually return the AIC, but
> > rather -2*log-likelihood + 2*(model_has_scale_parameter) ?
>
> This rings a faint bell from the last millennium with me,
> and the following "fortune" may contain the answer implicitly :
>
> ------------------------------------------------------------
> > if(!require("fortunes")) install.packages("fortunes")
> > fortune("bug compatib")
>
> For quite a while, bug-for-bug compatibility with S-PLUS v 3.x was
> considered
> important to allow people to port their packages between systems.
> -- Peter Dalgaard
> R-help (February 2009)
> >
> ------------------------------------------------------------
>
> Ideally, readers who still have access to a version of S-PLUS / S+
> or who have read and internalized or (even co-written !)
> "The white book", notably Ch.6, may be able to shed a historic
> light on this.
>
> I note that the white book's Appendix B with function help
> pages, has a page ?family.object, accessible here
> https://sites.oxy.edu/lengyel/M150/Sueselbeck/helpfiles/family.object.html
>
> which does *not* mention a <fam>$dev.resid() component, but instead
> allows to use <fam>$residuals(*, residuals=TRUE)
> get the
> "
> vector of deviance residual, whose weighted sum of
> squares is the deviance
> "
>
> Given the above, and also the ?glm entry
>
> || Author(s):
> ||
> || The original R implementation of ‘glm’ was written by Simon Davies
> || working for Ross Ihaka at the University of Auckland, but has
> || since been extensively re-written by members of the R Core team.
> ||
> || The design was inspired by the S function of the same name
> || described in Hastie & Pregibon (1992).
>
> actually suggest that it may be hard nowadays to find the
> original "design specs" that Simon and Ross had used at the
> time, and also that they only were _inspired_ by the white book
> chapter 6 (= Hastie & Pregibon (1992)).
>
>
>
> > Can anyone in this forum gauge how a documentation patch
> > would be received?
>
> It depends on further answers to your questions (i.e, this
> thread), but I'd currently say "gratefully".
> I'd expect it would be a patch mainly to
> src/library/stats/man/family.Rd
>
> Note that help(AIC) has a non-small 'Details' section, but
> indeed it does not mention the family(*)$aic function.
>
> > This behaviour does not seem to be documented in ?family (or anywhere
> > else I can find), which says:
>
> > aic: function giving the AIC value if appropriate (but ‘NA’ for
> > the quasi- families). See ‘logLik’ for the assumptions made
> > about the dispersion parameter.
>
>
> > For a demonstration that e.g. binomial()$aic() is really -2*log L and
> > not the AIC, see:
>
> >
> https://github.com/wch/r-source/blob/trunk/src/library/stats/R/family.R#L317
>
> > This document
> > <https://github.com/lme4/lme4/blob/master/misc/notes/deviance.rmd>
> > explicates the details a bit more ('L' denotes log-likelihood):
>
> > * family()$aic computes $-2L$, which glm.fit translates to an AIC by
> > adding $2k$ and storing it in model$aic
> > * logLik.default retrieves model$aic and converts it back to a
> > log-likelihood
> > * stats:::AIC.default retrieves the log-likelihood and converts it
> > back to an AIC (!)
> > * family()$dev.resid() computes the squared deviance residuals
> > * stats:::residuals.glm retrieves these values and takes the signed
> > square root
>
> > cheers
> > Ben Bolker
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