It happens BC the model went to the simple effect parameterization (which
has the same number of parameters): a main effect plus simple effect of the
second factor at all three levels of the first.

I assume that this is the default as removing factors while keeping the
interaction usually does not make sense.

Flo

On Mon, Sep 19, 2016, 08:23 Titus von der Malsburg <malsb...@posteo.de>
wrote:

>
> Hi Rachel!
>
> The Df column in the anova output suggests that both your models had the
> same number of parameters.  This is odd because you removed one
> parameter in the formula.  The only way I think this could happen is
> when the factor Listener.f is constant.  In this case, I think lmer
> would drop Listener.f such that it is in none of the two models.
>
> Hope that helps,
>
>   Titus
>
>
> On 2016-09-18 Sun 21:57, Rachel Ostrand wrote:
> > Hi everyone,
> >
> > I'm having trouble with some 2-factor glmer models that I'm trying to
> run,
> > such that the model with one of the main effects removed is coming out
> > identical to the full model. Some colleagues suggested that this might be
> > due to the coding of my factors, specifically because I have a factor
> that
> > has 3 levels, and that one needs to be treated differently, but I'm not
> > sure how - or why - to do that.
> >
> > Brief summary of my data:
> > -My DV (called Target_E2_pref) is a binary categorical variable.
> > -There are two categorical IVs: Listener (2 levels) and SyntaxType (3
> > levels).
> > -Listener varies by both subject and item (i.e., picture); SyntaxType
> only
> > varies by subject.
> >
> > When I dummy coded my variables using contr.treatment(), the model with
> the
> > main effect of Listener removed from the fixed effects comes out
> identical
> > to the full model:
> >
> > SoleTrain = read.table(paste(path, "SoleTrain.dat", sep=""), header=T)
> > SoleTrain$Listener.f = factor(SoleTrain$Listener, labels=c("E1", "E2"))
> > contrasts(SoleTrain$Listener.f) = contr.treatment(2)
> > SoleTrain$SyntaxType.f = factor(SoleTrain$SyntaxType,
> > labels=c("Transitive", "Locative", "Dative"))
> > contrasts(SoleTrain$SyntaxType.f) = contr.treatment(3)
> >
> > # Create full model:
> > SoleTrain.full<- glmer(Target_E2_pref ~ Listener.f*SyntaxType.f + (1 +
> > Listener.f*SyntaxType.f|Subject) + (1 + Listener.f|Picture), data =
> > SoleTrain, family = binomial, verbose=T,
> > control=glmerControl(optCtrl=list(maxfun=20000)))
> >
> > # Create model with main effect of Listener removed:
> > SoleTrain.noListener<- glmer(Target_E2_pref ~ SyntaxType.f +
> > Listener.f:SyntaxType.f + (1 + Listener.f*SyntaxType.f|Subject) + (1 +
> > Listener.f|Picture), data = SoleTrain, family = binomial, verbose=T,
> > control=glmerControl(optCtrl=list(maxfun=20000)))
> >
> >> anova(SoleTrain.full, SoleTrain.noListener)
> > Data: SoleTrain
> > Models:
> > SoleTrain.full: Target_E2_pref ~ Listener.f * SyntaxType.f + (1 +
> > Listener.f * SyntaxType.f | Subject) + (1 + Listener.f | Picture)
> > SoleTrain.noListener: Target_E2_pref ~ SyntaxType.f +
> > Listener.f:SyntaxType.f + (1 + Listener.f * SyntaxType.f | Subject) + (1
> +
> > Listener.f | Picture)
> >                      Df    AIC    BIC  logLik deviance Chisq Chi Df
> > Pr(>Chisq)
> > SoleTrain.full       30 2732.5 2908.5 -1336.2   2672.5
> >
> > SoleTrain.noListener 30 2732.5 2908.5 -1336.2   2672.5     0      0
> >  1
> >
> > However, I don't have this problem when I test for the main effect of
> > SyntaxType, and remove the SyntaxType.f factor from the fixed effects.
> > (That is, this produces a different model than the full model.)
> >
> > Someone suggested that Helmert coding was better for factors with more
> than
> > two levels, so I tried running the same models except with Helmert coding
> > [contrasts(SoleTrain$SyntaxType.f) = contr.helmert(3)], but the models
> come
> > out identical to the way they do with dummy coding. So why does the model
> > with the main effect of Listener removed the same as the model with the
> > main effect of Listener retained?
> >
> > Any suggestions as to what I'm doing wrong?
> >
> > Thanks!
> > Rachel
>
>

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