I recently found the great glmmTMB package which I hoped would help me with my models. My data are 60,000 facebook posts that are nested in 51 companies (i.e., the posts by these companies). My questions are 1) how to incorporate a random slope component exactly in a hurdle model and 2) what's the best overall procedure would look like. 3) I have found some strange effects and wonder if these are trustworthy
The background of the model is:
On the post level, I have a binary predictor "CSRcommunication" (yes vs. no) and the DV is the number of likes (NoL) that the post receives. On the organizational level, have the "reputation of the company as irresponsible" (RR) (wrt. to diverse dimensions, working place, environment etc.). I would like to model a cross-level interaction (posts with a CSR content should backfire when they stem from a bad reputation firm).
The NoL has zero-inflation as well as overdispersion. Hence, I tested a series of models -- but focused on the random intercept model containing my predictors (industry, CSRCom, and RR) - that is Poisson, NegBin, ZIP, ZINB and hurdle model. The winner of this series was the hurdle model. But when I now want to proceed with the random slope model and adding cross-level interaction, I do not know whether I have to incorporate the random slope component in the conditional part only or also in the ziformula.
My code looks like this:
glmmTMB(NoL ~ Industry + RR_gmcnt*CSRCom_cnt + (CSRCom_cnt | orgID), ziformula=~RR_gmcnt*CSRCom_cnt + (CSRCom_cnt | orgID), data=mldata, family=list(family="truncated_nbinom2",link="log"))
Notes: RR was grand mean centered, and CSRCom was within-firm centered. I had to leave out industry from the ziformula due to convergence problems.
Beyond this question: If I run this code, I get strange results - namely a negative effect of CSRCom in the conditional part indicating a lower NoL when posting about CSR (contrary to expectation). Likewise, the effect of RR is negative in the conditional part but positive in the zero-inflation part. This is especially notable as the effects in the random intercept model look completely different (as expected). In addition, when I add the random slope component, the standard errors explode. There is something problematic here.
As I haven't seen random slopes and cross-level interactions with glmmTMB, I would like to receive some advice. I don't want to draw the wrong conclusions.