Cross Validated Asked by Evy on December 13, 2021
I am fitting my data (around 10.000 datapoints) using the following generalised linear mixed model from the lme4 package:
model1<-glmer(reactionTimes ~ congruency * condition + frequencyItem + lengthItem + (1|subject) + (1|item), data = datadf, family=Gamma(link="identity"), control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=2e5)))
This is a full within participants design. My goal is to find out whether my reaction times are influenced by congruency (factor, congruent vs incongruent) and condition (factor, cond1, cond2, cond3). When I run the model by setting the reference level as “cond1” I have the following output in the Anova:
car::Anova(model1)
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: reactionTimes
Chisq Df Pr(>Chisq)
congruency 115.6393 1 < 2.2e-16 ***
condition 3.3445 2 0.1878
frequencyItem 663.8526 1 < 2.2e-16 ***
lengthItem 97.4373 1 < 2.2e-16 ***
congruency:condition 32.9506 2 6.996e-08 ***
I then re-run the same model, but this time releveling the contrasts to “cond2”.
The output of car::Anova of this model is the following:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: reactionTimes
Chisq Df Pr(>Chisq)
congruency 107.6610 1 < 2.2e-16 ***
condition 1.4125 2 0.4934930
frequencyItem 398.3679 1 < 2.2e-16 ***
lengthItem 54.1887 1 1.821e-13 ***
congruency:condition 16.7656 2 0.0002288 ***
Someone can explain why the Anova results change?
Thanks a lot
my R version: R-4.0.0
lme4 version: lme4_1.1-23
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