I am modelling a behavioural response (i.e., # times behaviour was observed/time observed [no longer an integer value]) in relation to disturbance levels (continuous) and the health status of the individual (2 categories: healthy/sick).
The behaviour was not observed in 311/352 observations, so I selected a zero-inflated linear mixed model for this analysis. The mixed component was included because most individuals were observed multiple times. After modelling the data, I used the DHARMa package to examine the residual plots, but since this is my first time using glmmTMB (and a zero-inflated linear mixed model), I'm uncertain about the interpretation of the resulting plots.
# my model m1 <- glmmTMB(behaviour ~ disturbance * health + (1|id), ziformula = ~1, data = df)) # checking plots res1 <- simulateResiduals(m1) plot(res1)
Here's what I think this is showing:
QQ plot residuals: the lower cluster of points are all the observations where the behaviour did not occur, and the upper points are all the observations where the behaviour did occur. Given that the points do not fall along the red line, the zero-inflation model is probably not accounting for all the excess zeros in the response.
Residuals vs predicted: downward trend in the values might be a missing variable, and all the points near the top of the graph may represent outliers?
Anything else I'm missing? Or ideas for addressing these issues?