[R-sig-ME] Replicating type III anova tests for glmer/GLMM
Phillip.Alday at unisa.edu.au
Tue Feb 23 10:51:37 CET 2016
lme4:anova() is not the same thing as car::Anova()!
A quick R note that might have avoided the confusion:
The :: syntax in R refers to scope, so you can specify a function
unambiguously via package::function.name(). Moreover, R is case
sensitive, so Anova() and anova() are generally different things.
Henrik's message (posted to the list so if you don't suscribe, you need
to look here:
) describes how to do this with either his afex package (for
likelihood-ratio tests) or John Fox's car package (for analysis of
deviance / Wald tests).
If you just want to perform likelihood-ratio tests in lme4, then you
should look at the drop1() function or you can use anova(reduced.model,
full.model). Henrik also does a nice job summarizing some of the issues
here, so I won't repeat them.
One final note: not everything that holds for normal LMM holds for GLMM
-- GLMM tends to be much more complicated. :-(
On 23/02/16 20:03, Francesco Romano wrote:
> Yes. An ANOVA with my final bglmer model yields:
> Analysis of Variance Table
> Df Sum Sq Mean Sq F value
> syntax12 1 1.7670 1.7670 1.7670
> animacy12 1 3.4036 3.4036 3.4036
> group123 2 5.7213 2.8607 2.8607
> animacy12:group123 2 4.5546 2.2773 2.2773
> syntax12:group123 2 8.1732 4.0866 4.0866
> which is counterintuitively not what the authors of the papers
> apparently used to generate coefficients to report their main effects
> and interactions. It looks to me more like ML fitting. Elsewhere,
> and more typically, main effects and interactions are obtained by
> comparing a
> model with the main fixed effect to a model without the
> main fixed effect in terms of log-likelihood ratio tests
> (Raffray et al., 2013, http://dx.doi.org/10.1016/j.jml.2013.09.004, p.6).
> I understand obtaining p-values from a summary
> of linear mixed models fit by lmer is a contentious issue
> but I guess I might be missing something here.
> On Tue, Feb 23, 2016 at 2:21 AM, Phillip Alday
> <Phillip.Alday at unisa.edu.au <mailto:Phillip.Alday at unisa.edu.au>> wrote:
> Have you looked at car::Anova() ?
> [forgot to cc the list]
> > On 23 Feb 2016, at 11:42, Francesco Romano <francescobryanromano at gmail.com
> <mailto:francescobryanromano at gmail.com>> wrote:
> > Dear all,
> > I'm trying to report my analysis replicating the method in the
> > papers:
> > Cai, Pickering, and Branigan (2012). Mapping concepts to syntax:
> > from structural priming in Mandarin Chinese. Journal of Memory and
> Language 66
> > (2012) 833–849 <tel:%282012%29%20833%E2%80%93849>. (looking at pg.
> 842, "Combined analysis of Experiments 1
> > and 2" section)
> > Filiaci, Sorace, and Carreiras (2013). Anaphoric biases of null
> and overt
> > subjects in Italian and Spanish: a cross-linguistic comparison.
> > Cognition, and Neuroscience DOI:10.1080/01690965.2013.801502
> (looking at
> > pg.11, first two paragraphs)
> > This is because I have a glmer model with three fixed effects, two
> > intercepts modeling a binary outcome, exactly as in the articles
> > The difficulty I'm finding is with locating information on commands
> > generating coefficients, SE, z, and p values (e.g. maximum likelihood
> > (Laplace Approximation)) to report main effects and interactions
> with the
> > anova or afex:mixed commands, following application of effect
> coding. I
> > have looked in several places, including Ben Bolker's FAQ
> > http://glmm.wikidot.com/faq and past posts on the topic in this r-sig.
> > Although there appears to be a plethora of material for lmer, I
> can't seem
> > to locate anything in the right direction for glmer.
> > Many thanks for any help.
> > --
> > Frank Romano Ph.D.
> > *LinkedIn*
> > https://it.linkedin.com/pub/francesco-bryan-romano/33/1/162
> > *Academia.edu*
> > https://sheffield.academia.edu/FrancescoRomano
> > [[alternative HTML version deleted]]
> > _______________________________________________
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> Frank Romano Ph.D.
> Tel. +39 3911639149
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