I have two dependent variables: Gaze Duration and Total Time that are reading time (RT), and three independent variables: cCue (grammatical, semantic), cAttachment (low, high) and cGroup (HS, L2) that are dummy coded as (-0.5 and 0.5). I carried out maximal random effects structures, my model is:
w2.lme.full <- w2 %>% group_by(Reading_Measure) %>% nest() %>% mutate(fit = map(data, ~ lmer(RT ~ cCue*cAttachment*cGroup + (1|Participants) + (1|item), REML = FALSE, data = .x))) str(w2.lme.full$fit[1:2], max.level = 1)
The results show interaction between cCue:cGroup. To follow up on this interaction I want to create a model with a single effect that contains the unique combinations from the interaction, let’s call it CueGroup. Then test only this effect:
RT ~ CueGroup + (1|Participants) + (1|Item)
glht(new model, linfct = mcp(cCuecGroup = "Thkey"))
However, I am having difficulty creating that single effect with interaction. Is there another way to do a post-hoc on interaction?