I am trying to estimate a LMEM model with three fixed effects (two regressors and their interaction) and one random effect (a by-subject random intercept). Both regressors are dichotomous.
I'm using R's lme4 command, and I'm just having some problems understanding the residual degrees of freedom.
The model is below. Per my training, I estimated p-values using a Type III Wald F test with Kenward-Rogers degrees of freedom.
conflict1 <- lmer(neurosynth_conflict ~ contrast*Teen_vs_Adult + (1 | participant_id),dCon)
car::Anova(conflict1, type = 3, test = 'F')
The model converges just fine and the output mostly aligns with my expectations, but I'm just really thrown by the residual degrees of freedom. I get that residual degrees of freedom will almost never be integers under REML, but I don't understand why the values are so different across my regressors? Can anyone help me wrap my head around this?
F Df Df.res Pr(>F) (Intercept) 20.0400 1 377.20 1.006e-05 *** contrast 10.5006 1 496.74 0.001273 ** Teen_vs_Adult 2.7166 1 388.47 0.100118 contrast:Teen_vs_Adult 5.9632 1 499.22 0.014953 * ```