I am running an ordinal model on rating data, with 1 random effect (subject) and 2 fixed effects (condition with 3 levels, probe.position with 6). I use the clmm function from the ordinal package. I conducted the following:
ordinal.GMSI <- clmm(response ~ condition * probe.position + (1|pp), data = ratingdata)
Now I would like to obtain some omnibus statistics for the main effects and interactions. I first did this manually, by defining models leaving one factor out and then comparing them, for example for the factor condition:
ordinal.minus.condition <- clmm(response ~ probe.position + (1|pp), data = ratingdata) maineffectcondi <- anova(ordinalresults,ordinal.minus.condition)
This gives me a result showing a likelihood ratio test (chi squared):
Likelihood ratio tests of cumulative link models: formula: link: threshold: ordinal.minus.condition response ~ probe.position + (1 | pp) logit flexible ordinalresults response ~ condition * probe.position + (1 | pp) logit flexible no.par AIC logLik LR.stat df Pr(>Chisq) ordinal.minus.condition 9 41335 -20658 ordinalresults 21 38600 -19279 2758.9 12 < 2.2e-16 ***
However, I can also look at the main effects using the emmeans package and the joint_tests function. This seems much easier, especially since I may start adding factors to the model, and doing everything manually then quickly becomes a lot of work. I would then do this:
which gives me:
model term df1 df2 F.ratio p.value condition 2 Inf 158.387 <.0001 probe.position 5 Inf 236.343 <.0001 condition:probe.position 10 Inf 231.791 <.0001
Here, the factor condition is again quite significant, but it's an F-test, not a Chi-square test. So which is the correct to use?
ps. I'm new to R and new to this forum, so please let me know if my question is unclear, inappropriate, or otherwise problematic!