Comparing emmeans to ggeffect - default weights

Question

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?

Answer 1

It depends on a lot of things. But let's start with...

• How many factors are there?
• Are interactions included in the model?
• If not, should interactions be included? E.g., have you even tried it, and looked at whether any of them are significant.
• Have you looked at residual plots?

If interactions are playing a role here, it is quite possible that you shouldn't even be considering marginal means -- in which case the question of weights becomes moot.

Once you have a defensible model, the next step is to plot the predictions, e.g., using emmip().

Sometimes questions like those above seem annoying. But often, I see people racing for the finish line when they haven't even tied their shoes yet.