My data has a binary response (correct/incorrect), one continuous predictor
score, three categorical predictors (
emotion) and a random intercept for the random factor
subj. All predictors are within-subject. One of the categorical factor has 3 levels, the other have two.
I need advice on obtaining "global" p-values for each categorical factor (in an "ANOVA like" way)
Here is how I proceed :
I fitted a binomial GLMM using 'glmer' from the lme4 package (because 'glmmML' doesn't compute on my data and glmmPQL does not provide AIC) and did model selection using
drop1 repeatedly until no more terms can be dropped. Here is the final model (let's assume it has been validated):
library(lme4) M5 <- glmer(acc ~ race + sex + emotion + sex:emotion + race:emotion + score +(1|subj), family=binomial, data=subset) # apparently using family with lmer is deprecated drop1(M5, test="Chisq") summary(M5)
drop1 gives p-values for the higher level terms only (the two 2-way interactions +
summarygives p-values for every term, but separates the different levels of each categorical factor.
How can I get "global" p-values for each factor? I need to report them even if they are not the most relevant or meaningful estimates of signifiance here. How should I proceed? I tried searching on the web and ended up reading about likelihood ratios or the "Wald test" but I am not sure if or how this would apply here.
(PS: This is a duplicate from my "anonymous" post here that needed editing: https://stats.stackexchange.com/questions/90487/binomial-mixed-model-with-categorical-predictors-model-selection-and-getting-p Sorry about that.)