I have a glmer() model with a categorical predictor that has twelve levels. When I print the summary(), it has all twelve levels, but for some reason, using ggeffects functions (ggpredict() or ggemmeans()) only print eight.
> model.contcons <- data %>%
glmer(bin_choice ~ Wshort + (1|id), data = ., family = binomial, control = glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
> coef(summary(model.contcons))
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.844979105 0.08002527 -23.0549555 1.311832e-117
Wshortcutwelfare 0.028561627 0.10428477 0.2738811 7.841760e-01
Wshortdiscipline -0.349133890 0.11034237 -3.1640963 1.555653e-03
Wshortfreedom -0.003641179 0.10245239 -0.0355402 9.716490e-01
WshortineqincOK 0.107650142 0.10173642 1.0581278 2.899972e-01
Wshortleader 0.125679107 0.10041647 1.2515786 2.107235e-01
Wshortpolice 0.217560461 0.10133894 2.1468595 3.180447e-02
Wshortpolitduty 0.177802391 0.09991568 1.7795244 7.515383e-02
Wshortrefugees 0.109247617 0.10508525 1.0396094 2.985214e-01
WshortRussia 0.115529761 0.10101235 1.1437192 2.527401e-01
Wshorttaxesdown 0.176320660 0.10252782 1.7197347 8.548067e-02
Wshortworse-off -0.016075802 0.10455777 -0.1537504 8.778065e-01
> ggemmeans(model.contcons, "Wshort [all]")
# Predicted probabilities of bin_choice
Wshort | Predicted | 95% CI
-------------------------------------
climate | 0.14 | [0.12, 0.16]
cutwelfare | 0.14 | [0.12, 0.16]
freedom | 0.14 | [0.12, 0.15]
ineqincOK | 0.15 | [0.13, 0.17]
leader | 0.15 | [0.13, 0.17]
politduty | 0.16 | [0.14, 0.18]
refugees | 0.15 | [0.13, 0.17]
worse-off | 0.13 | [0.12, 0.15]
How do I get all twelve? I can't see any parameter that limits what's printed in the help, nor any limits to how many categories a predictor may have. The ones dropped don't even appear to be in any particular order, so why specifically these four (discipline, police, Russia, taxesdown) are missing?
Googling ggeffects and categories didn't help either. I know how to calculate marginal means manually, but ggeffects would be so handy.