Pairwise Comparisons Alike Emmeans with Lmercens

[brianfcase]

Matthias,

I would like to implement a post hoc pairwise comparison output on a lmercens object like one can perform following a lme4 model output with emmeans ( ) + pair ( ), or simply anova ( ) to extract global F values for categorical fixed effects.

For example:

with lme4, I can do the following (testosterone is a left censored response variable below, and thus want to utilize lmercens as I do below with lme4). Pit is a unique individual identifier that needs to be accounted for with random effects. Outcome and period are both categorical variables (outcome: n = 3), (period: n = 4).

lmer_t <- lmer(testosterone ~ period * outcome + (1|pit), data = parent_t0, REML = FALSE)
summary(lmer_t) #for model output

THEN --
anova(lmer_t) #to get global F values for period and outcome (both categorical variables)
pairwise<-emmeans(lmer_t,"outcome","period")
pairs(pairwise) #to extract pairwise comparisons between outcome, nested within the group of period

With the lmercens object, I can't perform these anova ( ) or emmeans / pairs pairwise comparisons.

Is there a way to perform pairwise comparisons with a lmercens model with multiple categorical variables that are nested within each other, alike one would perform with emmeans?

Many thanks for any insights you might be able to provide !

Brian Case
PhD Candidate
Virginia Tech
Blacksburg, VA USA

[ghost]

Hi Brian,

Thanks for your interest in this package. The two feature requests are definitely useful and natural extensions.
I will have to look into lme4's anova and also into how to extend emmeans to new model classes.
I come back to you here, but I can't estimate the time.

Best,
-m

[brianfcase]

Matthias,

Thanks for the reply! Yes those two features would be super helpful. I think others would find them useful extensions to the package you've created (which in my research of various packages, seems to be the easiest to learn / use if working with a censored dataset, with the need for a mixed framework). Very helpful to simply use lme4 language.

Appreciate any time you have in the future to add these features !

Brian