pvalues {lme4}

Getting p-values for fitted models

Description

One of the most frequently asked questions about lme4 is "how do I calculate p-values for estimated parameters?" Previous versions of lme4 provided the mcmcsamp function, which efficiently generated a Markov chain Monte Carlo sample from the posterior distribution of the parameters, assuming flat (scaled likelihood) priors. Due to difficulty in constructing a version of mcmcsamp that was reliable even in cases where the estimated random effect variances were near zero (e.g. https://stat.ethz.ch/pipermail/r-sig-mixed-models/2009q4/003115.html), mcmcsamp has been withdrawn (or more precisely, not updated to work with lme4 versions >=1.0.0).

Many users, including users of the aovlmer.fnc function from the languageR package which relies on mcmcsamp, will be deeply disappointed by this lacuna. Users who need p-values have a variety of options. In the list below, the methods marked MC provide explicit model comparisons; CI denotes confidence intervals; and P denotes parameter-level or sequential tests of all effects in a model. The starred (*) suggestions provide finite-size corrections (important when the number of groups is <50); those marked (+) support GLMMs as well as LMMs.

• likelihood ratio tests via anova or drop1 (MC,+)

• profile confidence intervals via profile.merMod and confint.merMod (CI,+)

• parametric bootstrap confidence intervals and model comparisons via bootMer (or PBmodcomp in the pbkrtest package) (MC/CI,*,+)

• for random effects, simulation tests via the RLRsim package (MC,*)

• for fixed effects, F tests via Kenward-Roger approximation using KRmodcomp from the pbkrtest package (MC,*)

• car::Anova and lmerTest::anova provide wrappers for Kenward-Roger-corrected tests using pbkrtest: lmerTest::anova also provides t tests via the Satterthwaite approximation (P,*)

• afex::mixed is another wrapper for pbkrtest and anova providing "Type 3" tests of all effects (P,*,+)

arm::sim, or bootMer, can be used to compute confidence intervals on predictions.

For glmer models, the summary output provides p-values based on asymptotic Wald tests (P); while this is standard practice for generalized linear models, these tests make assumptions both about the shape of the log-likelihood surface and about the accuracy of a chi-squared approximation to differences in log-likelihoods.

When all else fails, don't forget to keep p-values in perspective: https://phdcomics.com/comics/archive.php?comicid=905