This is a tentative draft of a FAQ list for the r-sig-mixed-models mailing list (the mailing list is at r-sig-mixed-models@r-project.org, with Gmane archives available as well).

The most commonly used functions for mixed modeling in R are

• linear mixed models: aov(), nlme::lme, lme4::lmer;
• generalized linear mixed models (GLMMs): MASS::glmmPQL, lme4::glmer, MCMCglmm::MCMCglmm;
• nonlinear mixed models: nlme::nlme, lme4::nlmer.
  

DISCLAIMERS:

• (G)LMMs are hard - harder than you may think based on what you may have learned in your second statistics class, which probably focused on picking the appropriate sums of squares terms and degrees of freedom for the numerator and denominator of an F test. ‘Modern’ mixed model approaches, which are much more powerful (they can handle complex designs, lack of balance, crossed random factors, some kinds of non-normally distributed responses, etc.), also require a new set of conceptual tools. In order to use these tools you should have at least a general acquaintance with classical mixed-model experimental designs but you should also, probably, read something about modern mixed model approaches (Littell et al. [[url=]20[/url]] and Pinheiro and Bates [[url=]23[/url]] are two places to start, although Pinheiro and Bates is probably more useful if you want to use R. Others? Faraway? Zuur's books) If you are going to use generalized linear mixed models, you should understand generalized linear models.
• All of the issues that arise with regular linear or generalized-linear modeling (e.g.: inadequacy of p-values alone for thorough statistical analysis; need to understand how models are parameterized; need to understand the principle of marginality and how interactions can be treated; dangers of overfitting, which are not mitigated by stepwise procedures; the non-existence of free lunches) also apply, and can apply more severely, to mixed models.
• When SAS (or Stata, or Genstat/AS-REML or …) and R differ in their answers, R may not be wrong. Both SAS and R may be ‘right’ but proceeding in a different way/answering different questions/using a different philosophical approach (or both may be wrong …)
• The advice in this FAQ comes with absolutely no warranty of any sort.