The library languageR provides a method (pvals.fnc) to do MCMC significance testing of the fixed effects in a mixed effect regression model fit using lmer. However, pvals.fnc gives an error when the lmer model includes random slopes.
Is there a way to do an MCMC hypothesis test of such models?
If so, how? (To be accepted an answer should have a worked example in R) If not, is there a conceptual/computation reason why there is no way?
This question might be related to this one but I didn't understand the content there well enough to be certain.
Edit 1: A proof of concept showing that pvals.fnc() still does 'something' with lme4 models, but that it doesn't do anything with random slope models.
library(lme4) library(languageR) #the example from pvals.fnc data(primingHeid) # remove extreme outliers primingHeid = primingHeid[primingHeid$RT < 7.1,] # fit mixed-effects model primingHeid.lmer = lmer(RT ~ RTtoPrime * ResponseToPrime + Condition + (1|Subject) + (1|Word), data = primingHeid) mcmc = pvals.fnc(primingHeid.lmer, nsim=10000, withMCMC=TRUE) #Subjects are in both conditions... table(primingHeid$Subject,primingHeid$Condition) #So I can fit a model that has a random slope of condition by participant primingHeid.lmer.rs = lmer(RT ~ RTtoPrime * ResponseToPrime + Condition + (1+Condition|Subject) + (1|Word), data = primingHeid) #However pvals.fnc fails here... mcmc.rs = pvals.fnc(primingHeid.lmer.rs)
It says: Error in pvals.fnc(primingHeid.lmer.rs) : MCMC sampling is not yet implemented in lme4_0.999375 for models with random correlation parameters
Additional question: Is pvals.fnc performing as expected for random intercept model? Should the outputs be trusted?