Users who use emmeans functions as part of a pipeline -- or post-process those results in some other way -- are likely missing some important information.
Details
Your best bet is to display the actual results without any post-processing.
That's because emmeans and its relatives have their own summary
and print methods that display annotations that may be helpful in
explaining what you have. If you just pipe the results into the next step,
those annotations are stripped away and you never see them. Statistical
analysis is not just a workflow; it is a discipline that involves care in
interpreting intermediate results, and thinking before moving on.
Examples
neur.glm <- glm(Pain ~ Treatment + Sex + Age, family = binomial(),
data = neuralgia)
### The actual results with annotations (e.g. ests are on logit scale):
emmeans(neur.glm, "Treatment")
#> Treatment emmean SE df asymp.LCL asymp.UCL
#> A -1.40 0.664 Inf -2.699 -0.0951
#> B -1.94 0.744 Inf -3.403 -0.4867
#> P 1.78 0.683 Inf 0.444 3.1198
#>
#> Results are averaged over the levels of: Sex
#> Results are given on the logit (not the response) scale.
#> Confidence level used: 0.95
### Post-processed results lose the annotations
if(requireNamespace("tibble")) {
emmeans(neur.glm, "Treatment") |> tibble::as_tibble()
}
#> # A tibble: 3 × 6
#> Treatment emmean SE df asymp.LCL asymp.UCL
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 A -1.40 0.664 Inf -2.70 -0.0951
#> 2 B -1.94 0.744 Inf -3.40 -0.487
#> 3 P 1.78 0.683 Inf 0.444 3.12