Say I simply model how two categories (group
) predict an ordinal outcome (coded as integers -3 to 3) using a latent probit model:
# Data
library(brms)
D = data.frame(
response = ordered(sample(-3:3, 50, replace=T)),
group = rep(c('A', 'B'), each=25))
# Fit
fit = brm(response ~ group, D, family=cumulative('probit'), chains = 1)
The parameter estimates (fixef(fit)
) are:
Estimate Est.Error Q2.5 Q97.5
Intercept[1] -1.5296313 0.4675201 -2.50225569 -0.6709311
Intercept[2] -0.3400179 0.4008245 -1.11796242 0.4344084
Intercept[3] 0.1475556 0.3957674 -0.61386887 0.9406194
Intercept[4] 0.7612172 0.4036700 0.01900015 1.5740368
Intercept[5] 1.2638932 0.4173757 0.49971783 2.1210693
Intercept[6] 2.0226779 0.4736091 1.12749921 2.9540043
groupB 0.6102738 0.5117214 -0.36214454 1.6832346
Is there a way to get the “absolute” posterior for the latent group A
and B
on the scale of the response variable and not as a contrast to the other group? I.e., something similar to doing intercept + groupB
for group B in a normal model. I am particularly interested in doing a one-sided test on each of the groups whether they rate greater than 0. My intuitions fail me a bit whether this is even possible/meaningful since response-zero is somewhere in between the latent thresholds Intercept[3]
and Intercept[4]
.
- Operating System: Windows 10
- brms Version: 2.7