Confidence Intervals – Alternatives to pvals.fnc for Computing Confidence Intervals for Fixed Effects

confidence intervallme4-nlmemixed modelr

Lately I keep encountering the same problem and I'm wondering whether other people have been able to get around it. I'm running a mixed effects model using lmer(). My model has by-subject and by-item intercepts and slopes, and random correlation parameters between them. Since the current version of lmer() does not have MCMC sampling implemented, I cannot use pvals.fnc(). I get this message:

Error in pvals.fnc(m, withMCMC = T) : 
MCMC sampling is not implemented in recent versions of lme4
for models with random correlation parameters

pvals.fnc() is also the function I use to get confidence intervals (HPD95lower and HPD95upper were two columns in the pvals.fnc output). Does anyone know of an alternative way of getting confidence intervals for the fixed effects estimates in the model? Or does using models with random correlations means that we can no longer get CIs from R?

Thanks!

NOTE: I've seen this question asked in other forums in slightly different ways. However, the answers always seem to involve (1) calculating something different as an alternative to the confidence intervals, (2) some complicated solution that is unclear (at least to me) how to implement. I would like to know if there is some alternative way of computing CIs that is both mainstream (so that other researchers can use it) and has a function to do it in R, since I am not a programmer and I feel that trying to create that function myself would be error prone.

Best Answer

Most probably the packages lmerTest and lsmeans provide readily available routines for what you are looking for. Mind you, neither of them uses MCMC methodology. If you want to use something resampling-based, you can use lme4's native bootMer() function to bootstrap your model and get parametric bootstrap estimates (ver. 1.0-4 or newer).

Related Question