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.

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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 > >