I'm fitting an LME (with
lmer in R) with one categorical variable that has many (80) different values. A fitting example for my problem would be how weight loss after fasting is distributed across different body parts and organs. I.e. my model would be:
weight change ~ 1+ bodypart + (1|subject)
A likelihood ratio test tells me that it makes sense to include the categorical variable as predictor.
Now my key question is at what values of the predictor there is a significant/relevant deviation from 0, i.e. in which organs is there a significant change in weight? I first thought the estimates of the predictors for the individual levels of the categorical variables will tell me this. However, if I understand correctly this would only be true if the intercept would be 0.
My intuition is that perhaps estimated marginal means (ie. using
emmeans in R) could help me here but I would not know how to ask the question correctly (in R and in general).