Replicating type III anova tests for glmer/GLMM

Hi Francesco,

As far as I see it, there are basically two ways to get these tests easily.

1) afex::mixed()
You simple pass your model as you would to glmer() while also specifying 
that you want likelihood-ratio tests as method for testing the effects, 
e.g.,
m1 <- mixed(x~y + (y|z), data = d, method = "LRT", family = binomial)
m1 # prints tests of effects
nice(m1) # same, see also anova(m1)
summary(m1) # shows summary of full model

Note that mixed already takes care of the correct coding (i.e., uses 
sum.coding as default) and uses type 3 tests as default. You can also 
obtain bootstrapped p-values instead of approximate likelihood ratio 
tests by specifying method = "PB" in the call to mixed (but this might 
take some time, see the mixed examples of how to use multicore).

2) car::Anova()
You first need to fit the model but need to specify the correct coding 
and then pass it to car::Anova() to obtain Wald-tests of the effects, e.g.,:

afex::set_sum_contrasts() # sets sum coding globally
m2 <- glmer(x~y + (y|z), data = d, family = binomial)
car::Anova(m2, type = 3)


The difference between the two options is that mixed() fits a model for 
each effect to be tested while car::Anova() only reuqires one model to 
be fitted. The consequence is that the former might take quite longer 
(again, you can distribute the fitting of the individual models across 
cores, see the mixed examples). On the other hand, 
likelihood-ratio-tests are in principle less problematic than Wald tests 
(http://glmm.wikidot.com/faq#toc4).

Finally, mixed allows to easily suppress the estimation of correlation 
among random slopes for factors by using the expand_re argument which 
may help with model convergence:
m3 <- mixed(x~y + (y||z), data = d, method = "LRT", family = binomial, 
expand_re = TRUE)

What I would report would probably the Chi-square values and associated 
p-values for the tests of effects. Reporting coefficients and associated 
standard errors usually only makes sense if there are two groups. And 
for interactions they can also be difficult to interpret. So I would go 
with the chi-square values in the first place.

Hope that helps,
Henrik


Am 23.02.2016 um 02:12 schrieb Francesco Romano:

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.

Dear Francesco,

The Anova() function in the car package might do what you want. See ?Anova. "Effect coding" in R is implemented by contr.sum().

I hope this helps,
 John

-----------------------------
John Fox, Professor
McMaster University
Hamilton, Ontario
Canada L8S 4M4
web: socserv.mcmaster.ca/jfox

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

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R-sig-mixed-models at r-project.org mailing list
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And don't forget car::contr.Sum(), which is just the same as 
contr.sum from base R but provides saner contrast names ...

Dear Francesco,

The Anova() function in the car package might do what you want. See
?Anova. "Effect coding" in R is implemented by contr.sum().

I hope this helps, John

----------------------------- John Fox, Professor McMaster
University Hamilton, Ontario Canada L8S 4M4 web:
socserv.mcmaster.ca/jfox

________________________________________ From: R-sig-mixed-models
[r-sig-mixed-models-bounces at r-project.org] on behalf of Francesco
Romano [francescobryanromano at gmail.com] Sent: February 22, 2016 8:12
PM To: r-sig-mixed-models at r-project.org Subject: [R-sig-ME]
Replicating type III anova tests for glmer/GLMM

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

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