lme4 (version 1.1-7)

lme4-package: Linear, generalized linear, and nonlinear mixed models

Description

lme4 provides functions for fitting and analyzing mixed models: linear (lmer), generalized linear (glmer) and nonlinear (nlmer.)

Arguments

Differences between <pkg>nlme</pkg> and <pkg>lme4</pkg>

lme4 covers approximately the same ground as the earlier nlme package. The most important differences are:
  • lme4uses modern, efficient linear algebra methods as implemented in theEigenpackage, and uses reference classes to avoid undue copying of large objects; it is therefore likely to be faster and more memory-efficient thannlme.
  • lme4includes generalized linear mixed model (GLMM) capabilities, via theglmerfunction.
  • lme4doesnotcurrently implementnlme's features for modeling heteroscedasticity and correlation of residuals.
  • lme4does not currently offer the same flexibility asnlmefor composing complex variance-covariance structures, but it does implement crossed random effects in a way that is both easier for the user and much faster.
  • lme4offers built-in facilities for likelihood profiling and parametric bootstrapping.
  • lme4is designed to be more modular thannlme, making it easier for downstream package developers and end-users to re-use its components for extensions of the basic mixed model framework. It also allows more flexibility for specifying different functions for optimizing over the random-effects variance-covariance parameters.
  • lme4is not (yet) as well-documented asnlme.

Differences between current (1.0.+) and previous versions of <pkg>lme4</pkg>

  • [gn]lmernow produces objects of classmerModrather than classmeras before
  • the new version uses a combination of S3 and reference classes (seeReferenceClasses,merPredD-class, andlmResp-class) as well as S4 classes; partly for this reason it is more interoperable withnlme
  • The internal structure of [gn]lmer is now more modular, allowing finer control of the different steps of argument checking; construction of design matrices and data structures; parameter estimation; and construction of the finalmerModobject (seemodular)
  • profiling and parametric bootstrapping are new in the current version
  • the new version oflme4doesnotprovide anmcmcsamp(post-hoc MCMC sampling) method, because this was deemed to be unreliable. Alternatives for computing p-values include parametric bootstrapping (bootMer) or methods implemented in thepbkrtestpackage and leveraged by thelmerTestpackage and theAnovafunction in thecarpackage (seepvaluesfor more details).

Caveats and trouble-shooting

  • Some users who have previously installed versions of the RcppEigen and minqa packages may encounter segmentation faults (!!); the solution is to make sure to re-install these packages before installinglme4. (Because the problem is not with the explicitversionof the packages, but with running packages that were built with different versions ofRcppin conjunction with each other, simply making sure you have the latest version, or usingupdate.packages, will not necessarily solve the problem; you must actually re-install the packages. The problem is most likely withminqa.)