[R-sig-ME] Replicating type III anova tests for glmer/GLMM

[Phillip Alday]

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:
https://mailman.stat.ethz.ch/pipermail/r-sig-mixed-models/2016q1/024465.html
) 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. :-(

Best,
Phillip

On 23/02/16 20:03, Francesco Romano wrote:
> Yes. An ANOVA with my final bglmer model yields:
> 
>> anova(recallmodel4x6a)
> 
> 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
> 
> https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html
> 
> 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() ?
> 
>     Best,
>     Phillip
> 
>     [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
>     following
>     > papers:
>     >
>     > Cai, Pickering, and Branigan (2012). Mapping concepts to syntax:
>     Evidence
>     > 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.
>     Language,
>     > 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
>     random
>     > intercepts modeling a binary outcome, exactly as in the articles
>     mentioned.
>     >
>     > 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
> 
> /LinkedIn/
> https://it.linkedin.com/pub/francesco-bryan-romano/33/1/162
> 
> /Academia.edu/
> https://sheffield.academia.edu/FrancescoRomano