I have seen a couple of similar questions to my own without sufficient answers, so posting this here. I am running a generalised linear mixed effect regression model in R using the glmer() function in the lme4 package. My code is as follows:
model <- glmer(Response ~ step * type + duration + StemArousal + StemValency + stemBNCfreq + (1|ParticipantID)+(1|word),family=binomial, data=demodata, nAGQ = 0)
Response is a binary numerical variable (0 & 1). Step and type are both categorical variables (step has 7 levels, type has 3) and the other 4 predictors are numerical. Both random effects are categorical. I have 389 participants and 20 words.
Currently, I also include the argument 'nAGQ = 0' which I found via another post (https://stats.stackexchange.com/questions/77313/why-cant-i-match-glmer-family-binomial-output-with-manual-implementation-of-g). If I don't do this, then the model does not converge.
I found an explanation elsewhere of the difference between 'nAGQ = 1' and 'nAGQ = 0' (https://stat.ethz.ch/pipermail/r-sig-mixed-models/2017q3/025942.html) which suggests that what I have done is less precise as there is less interaction with the random effects.
Is there a general consensus about this acceptability of this approach? and does anyone have a reliable source for a discussion about it?
Apologies if this sounds like a cross-post/repeat question; it's just that nothing clear seemed to have been resolved.
