>>>>> "MM" == Martin Maechler <maech...@stat.math.ethz.ch>
>>>>> on Tue, 18 May 2010 12:37:21 +0200 writes:
>>>>> "GaGr" == Gabor Grothendieck <ggrothendi...@gmail.com>
>>>>> on Mon, 17 May 2010 09:45:00 -0400 writes:
GaGr> BIC seems like something that would logically go into stats in the
GaGr> core of R, as AIC is already, and then various packages could define
GaGr> methods for it.
MM> Well, if you look at help(AIC):
>> Usage:
>> AIC(object, ..., k = 2)
>> Arguments:
>> object: a fitted model object, for which there exists a ‘logLik’
>> method to extract the corresponding log-likelihood, or an
>> object inheriting from class ‘logLik’.
>> ...: optionally more fitted model objects.
>> k: numeric, the _penalty_ per parameter to be used; the default
>> ‘k = 2’ is the classical AIC.
MM> you may note that the original authors of AIC where always
MM> allowing the AIC() function (and its methods) to compute the BIC,
MM> simply by using 'k = log(n)' where of course n must be correct.
MM> I do like the concept that BIC is just a variation of AIC and
MM> AFAIK, AIC was really first (historically).
MM> Typically (and with lme4), the 'n' needed is already part of the
logLik()
MM> attributes :
>> AIC((ll <- logLik(fm1)), k = log(attr(ll,"nobs")))
MM> REML
MM> 1774.786
MM> indeed gives the BIC (where the "REML" name may or may not be a
MM> bit overkill)
MM> A stats-package based BIC function could then simply be defined as
> BIC <- function (object, ...) UseMethod("BIC")
> BIC.default <- function (object, ...) BIC(logLik(object), ...)
> BIC.logLik <- function (object, ...)
> AIC(object, ..., k = log(attr(object,"nobs")))
{well, modulo the fact that "..." should really allow to do
this for *several* models simultaneously}
In addition to that (and more replying to Doug Bates):
Given nlme's tradition of explicitly providing BIC(), and in
analogue to the S3 semantics of the AIC() methods,
- I think lme4 (and "lme4a" on R-forge) should end up having
working AIC() and BIC() directly for fitted models, instead of
having to use
AIC(logLik(.)) and BIC(logLik(.))
The reason that even the first of this currently does *not*
work is that lme4 imports AIC from "stats" but should do so
from "stats4".
--> I'm about to change that for 'lme4' (and 'lme4a').
However, for the BIC case, ... see below
- I tend to agree with Gabor (for once! :-) that
basic BIC methods (S3, alas) should move from nlme to stats.
For this reason, I'm breaking the rule of "do not cross-post"
for once, and am hereby diverting this thread to R-devel
Martin
MM> --
MM> Martin Maechler, ETH Zurich
GaGr> On Mon, May 17, 2010 at 9:29 AM, Douglas Bates <ba...@stat.wisc.edu>
wrote:
>>> On Mon, May 17, 2010 at 5:54 AM, Andy Fugard (Work)
>>> <andy.fug...@sbg.ac.at> wrote:
>>>> Greetings,
>>>>
>>>> Assuming you're using lmer, here's an example which does what you need:
>>>>
>>>>> (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy))
>>>> Linear mixed model fit by REML
>>>> Formula: Reaction ~ Days + (Days | Subject)
>>>> Data: sleepstudy
>>>> AIC BIC logLik deviance REMLdev
>>>> 1756 1775 -871.8 1752 1744
>>>> Random effects:
>>>> Groups Name Variance Std.Dev. Corr
>>>> Subject (Intercept) 612.092 24.7405
>>>> Days 35.072 5.9221 0.066
>>>> Residual 654.941 25.5918
>>>> Number of obs: 180, groups: Subject, 18
>>>>
>>>> Fixed effects:
>>>> Estimate Std. Error t value
>>>> (Intercept) 251.405 6.825 36.84
>>>> Days 10.467 1.546 6.77
>>>>
>>>> Correlation of Fixed Effects:
>>>> (Intr)
>>>> Days -0.138
>>>>
>>>>> (fm1fit <- summary(fm1)@AICtab)
>>>> AIC BIC logLik deviance REMLdev
>>>> 1755.628 1774.786 -871.8141 1751.986 1743.628
>>>>
>>>>> fm1fit$BIC
>>>> [1] 1774.786
>>>
>>> That's one way of doing it but it relies on a particular
>>> representation of the object returned by summary, and that is subject
>>> to change.
>>>
>>> I had thought that it would work to use
>>>
>>> BIC(logLik(fm1))
>>>
>>> but that doesn't because the BIC function is imported from the nlme
>>> package but not later exported. The situation is rather tricky - at
>>> one point I defined a generic for BIC in the lme4 package but that led
>>> to conflicts when multiple packages defined different versions. The
>>> order in which the packages were loaded became important in
>>> determining which version was used.
>>>
>>> We agreed to use the generic from the nlme package, which is what is
>>> now done. However, I don't want to make the entire nlme package
>>> visible when you have loaded lme4 because of resulting conflicts.
>>>
>>> I can get the result as
>>>
>>>> (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy))
>>> Linear mixed model fit by REML
>>> Formula: Reaction ~ Days + (Days | Subject)
>>> Data: sleepstudy
>>> AIC BIC logLik deviance REMLdev
>>> 1756 1775 -871.8 1752 1744
>>> Random effects:
>>> Groups Name Variance Std.Dev. Corr
>>> Subject (Intercept) 612.090 24.7405
>>> Days 35.072 5.9221 0.066
>>> Residual 654.941 25.5918
>>> Number of obs: 180, groups: Subject, 18
>>>
>>> Fixed effects:
>>> Estimate Std. Error t value
>>> (Intercept) 251.405 6.825 36.84
>>> Days 10.467 1.546 6.77
>>>
>>> Correlation of Fixed Effects:
>>> (Intr)
>>> Days -0.138
>>>> nlme:::BIC(logLik(fm1))
>>> REML
>>> 1774.786
>>>
>>> but that is unintuitive. I am not sure what the best approach is.
>>> Perhaps Martin (or anyone else who knows namespace intricacies) can
>>> suggest something.
>>>
>>>
>>>> Tahira Jamil wrote:
>>>>> Hi
>>>>> I can extract the AIC value of a model like this
>>>>>
>>>>> AIC(logLik(fm0)
>>>>>
>>>>> How can I extract the BIC value if I need!
>>>>>
>>>>> Cheers
>>>>> Tahira
>>>>> Biometris
>>>>> Wageningen University
>>>>>
>>>>> _______________________________________________
>>>>> r-sig-mixed-mod...@r-project.org mailing list
>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>
>>>>
>>>> --
>>>> Andy Fugard, Postdoctoral researcher, ESF LogICCC project
>>>> "Modeling human inference within the framework of probability logic"
>>>> Department of Psychology, University of Salzburg, Austria
>>>> http://www.andyfugard.info
>>>>
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