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

[Francesco Romano]

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>
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> 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. (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]]
> >
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>


-- 
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

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