Longitudinal logistic regression with continuous-time first-order autocorrelation structure
Don't use (time_hours|id) ... that will expand to a random effect with a full, unstructured covariance matrix term. If you have t distinct times measured, you'll end up with t*(t+1)/2 parameters to estimate. Try (1|time_hours) (and probably also include (1|id)) On Wed, Feb 28, 2018 at 8:44 PM, Dennis Ruenger
<dennis.ruenger at gmail.com> wrote:
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 [[alternative HTML version deleted]]
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