[R-sig-ME] Dropping fixed factor from a converged glmer model leads to singular fit

[Noam Tal-Perry]

Hey all,

I was fitting the following model to test whether condition (categorical, 4
levels) and reaction time (continuous, scaled) predict trial's result
(binomial, correct/incorrect).

model.acc.main_re = glmer(Result ~ 1 + Condition*scale(RT) + (1 +
Condition+scale(RT) | Subject_ID), data=behavioral_df.response_only, family
= binomial, control = glmerControl(optimizer ="bobyqa", optCtrl =
list(maxfun = 100000)))

This model successfully converged.
I was interested in doing an NHST for the interaction term, by comparing
this model with the same model sans the interaction (i.e. compute type-II
SS). So I dropped the interaction and fitted the following model:

model.acc.main_re.no_int = glmer(Result ~ 1 + Condition+scale(RT) + (1 +
Condition+scale(RT) | Subject_ID), data=behavioral_df.response_only, family
= binomial, control = glmerControl(optimizer ="bobyqa", optCtrl =
list(maxfun = 100000)))

Surprisingly, I got boundary (singular) fit error. I tried playing around
with the maxfun parameter but it didn't lead anywhere.

So I was wondering,
(1) How could this be? I was under the impression that with fewer factors,
it should be easier to fit the model, such that a sub-model of a converged
model should ought to converge as well.
(2) What is the recommended approach in such case? Should I drop re from
the main model (with the interaction) even though it converges, to reach a
point where the model without the interaction converges too, before
comparing with anova? Would it make more senes to drop re just from the
main effects model and preserve the re for the fuller model?

Would gladly share code and output if that may help.

Cheers,
NTP

Noam Tal-Perry
PhD student
Shlomit Yuval-Greenberg's Eye-Movement Research Lab
School of Psychological Sciences, Tel-Aviv University

	[[alternative HTML version deleted]]