For a model estimated in glmmTMB with a zero-truncated negative binomial distribution, I am trying the following to probe an interaction:
- emmeans() to estimate marginal means
- pairs(emmeans()) to estimate pairwise comparisons
- ggeffect() to prepare the marginal means for plotting
It looks like the marginal means are different across the emmeans and ggeffects package due to different default weights. In emmeans(),
weights = "equal" is the default, whereas I need to specify
weights = "proportional" in emmeans() to have the results match ggeffect().
The pairwise comparisons for the "equal" and "proportional" marginal means yield different results. The estimates are similar, but the SEs and resulting p values are different. How should one determine which weighting to use if the original design is unbalanced?