How to find the significance of different levels of an interaction in a generalised linear mixed model? I am using a glmm to find the effect of lockdown (Yes or No), sex (Male or Female), age (Young, Teenager, Adult) and site (A or B) on hormone concentrations (Continuous). Using lrtest, I have compared different nested models with each other to determine the significance of the different explanatory variables. However, I am unsure how to find the significance of different levels.
My final model is: Concentration ~ Lockdown + Age + Sex + Site + LockdownSex + SiteSex + (1|individual).
When I run "anova(model)" it gives me the values for only one level of each variable, i.e. SiteA:SexMale. How can I get the significance of other levels in the interaction, i.e. SiteB:SexFemale?



I had a similar question in this post and answered my question. Please review it for further explanation.
You can use car::Anova function:
Anova(model5, type = 3)

If an interaction  exist (for example, LOCKDOWN * SEX), you can use emmeans function to detect difference:
emmeans(model5, pairwise ~ LOCKDOWN * SEX, adjust = "bonferroni", mode = "linear.predictor", type = "Score")

