Interrupted time series on grouped data with count outcome in glmmTMB
Hi all, This is my first time sending a question to this list so apologies if I miss something that is typically expected. I am trying to fit an interrupted time series type model on grouped data (by individual). Specifically, I am modeling compliance with a remote survey tool that delivered 5 surveys a day for 28 days across two bursts. The outcome variable is a count of missed surveys on days with any engagement (so range 0-4) (https://i.stack.imgur.com/FThRs.png). Ignoring covariates, the three focal predictors are thus time (studyday_new), burst (wave), and time after burst 2 starts (studyday_new_post). The model is specified as follows (and I acknowledge I could be making a mistake here, I tried to follow Ben Bolker's thoughts on fitting this models with temporal autocorrelation in glmmTMB from https://bbolker.github.io/mixedmodels-misc/notes/corr_braindump.html): mod1_mde <- glmmTMB(missed_surveys ~ w1age + school + w1sex + w1hislat + w1racer + studyday_new + wave + studyday_new_post + ar1(studyday_new_t + 0|id) + (1 |id), data = daily_data3_mde, family = "poisson") This model converges. But when I plot the predicted values (using ggpredict from ggeffects) over the observed daily means, the predicted values appear to be underestimating the observed means. Here is the plot: https://i.stack.imgur.com/bRfue.png Now what's interesting to me is that when I fit this model to a gaussian distribution instead, the resulting predicted values are reasonable: https://i.stack.imgur.com/rIYnZ.png And when I fit this model to a poisson distribution but exclude the AR covariance structure and random intercept (both of which are obviously critical) I also get reasonable predicted values: https://i.stack.imgur.com/dWlho.png (Please ignore the different labels on the plots I just copied the syntax from other parts of my code when I was writing it). So is there something I'm missing about getting predicted values from this model? All the best, -- Alex
*Alexander W. Sokolovsky, PhD* Assistant Professor Center for Alcohol and Addiction Studies Department of Behavioral and Social Sciences E: Alexander_Sokolovsky at Brown.edu P: (401) 863-6629(401) 863-6697 (Fax) A: Box G-S121-5, Providence, RI 02912 <https://maps.google.com/?q=Box%20G-S121-5%2C%20Providence%2C%20RI%2002912> W: https://vivo.brown.edu/display/asokolo1 [image: Twitter]AlexSokoPhD <https://twitter.com/AlexSokoPhD> ?The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge.? - Stephen Hawking [[alternative HTML version deleted]]