[R-sig-ME] Longitudinal logistic regression with continuous-time first-order autocorrelation structure

[Dennis Ruenger]

Thanks, Alain and Ben, for your replies.

My understanding is that for the kind of intensive longitudinal data I'm
dealing with, a mixed model with both random intercepts and slopes for the
time effect *and *autoregressive errors are recommended.

I'd like to follow Alain's suggestion and give glmmTMB a try. Based on a
description of the covariance structures available with glmmTMB (link
below), it looks like the Ornstein–Uhlenbeck covariance structure might be
what I'm looking for (i.e., something akin to corrCAR1() that works in a
GLMM).

So I tried:

df$time_hours <- numFactor(df$time_hours)
fit  <- glmmTMB(y ~ time_hours + (time_hours|id) + ou(time_hours-1|id),
family = binomial, data = df)

However, after about 10 minutes, I receive an error message about failed
memory allocation (on a laptop with a 7th gen Intel Core i5 processor and
8GB RAM). The data set includes 34 participants with up to 300 data points
per participants. Running the model for a subset of 5 participants also
resulted in memory allocation failure. The same was true for the spatial
Gaussian and spatial exponential covariance structures.

Does anyone see a way to make this work with glmmTMB?

Thanks a lot.

https://cran.r-project.org/web/packages/glmmTMB/vignettes/covstruct.html

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