Adding to my own statements (below) : >>>>> "MM" == Martin Maechler <maech...@stat.math.ethz.ch> >>>>> on Tue, 18 May 2010 13:05:27 +0200 writes:
>>>>> "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"))) MM> {well, modulo the fact that "..." should really allow to do MM> this for *several* models simultaneously} MM> In addition to that (and more replying to Doug Bates): MM> Given nlme's tradition of explicitly providing BIC(), and in MM> analogue to the S3 semantics of the AIC() methods, MM> - I think lme4 (and "lme4a" on R-forge) should end up having MM> working AIC() and BIC() directly for fitted models, instead of MM> having to use MM> AIC(logLik(.)) and BIC(logLik(.)) MM> The reason that even the first of this currently does *not* MM> work is that lme4 imports AIC from "stats" but should do so MM> from "stats4". MM> --> I'm about to change that for 'lme4' (and 'lme4a'). MM> However, for the BIC case, ... see below MM> - I tend to agree with Gabor (for once! :-) that MM> basic BIC methods (S3, alas) should move from nlme to stats. MM> For this reason, I'm breaking the rule of "do not cross-post" MM> for once, and am hereby diverting this thread to R-devel What I *did* find is that the stats4 package has already had all necessary BIC methods -- S4, not S3. So for lme4 (and R-forge's "lme4a"), I've only needed to change the NAMESPACE file to have both importFrom("stats4", AIC, BIC, logLik)# so S4 methods are used! and later export(AIC, BIC, .....) and also add 'stats4' to the 'Imports: ' line in DESCRIPTION. So both (development versions of) lme4 and lme4a now have working AIC() and BIC(), and I guess Doug could release a new version of lme4 (not .."a") pretty soon. I got private e-mail suggestions for extensive S3 methods for AIC, BIC and logLik. I think these should happen more in public (i.e. here, on R-devel), and while I still advocate that a BIC S3 generic + simple default methods should be added (as above), I'd be happy if others joined into the discussion, (and possibly provided simple patches). 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 >>>>> MM> ______________________________________________ MM> R-devel@r-project.org mailing list MM> https://stat.ethz.ch/mailman/listinfo/r-devel ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel