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.)Differences between <span class="pkg">nlme</span> and <span class="pkg">lme4</span>
lme4 covers approximately the same ground as the earlier
nlme package. The most important differences are:
- lme4 uses modern, efficient linear algebra methods
as implemented in the
Eigen package, and uses reference
classes to avoid undue copying of large objects; it is therefore likely
to be faster and more memory-efficient than nlme.
- lme4 includes generalized linear mixed model (GLMM)
capabilities, via the
glmer function.
- lme4 does not currently implement nlme's
features for modeling heteroscedasticity and
correlation of residuals.
- lme4 does not currently offer the same flexibility
as nlme for 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.
- lme4 offers built-in facilities for likelihood
profiling and parametric bootstrapping.
- lme4 is designed to be more modular than nlme,
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.
- lme4 is not (yet) as well-documented as nlme.
Differences between current (1.0.+) and previous versions of
<span class="pkg">lme4</span>
-
[gn]lmer now produces objects of class mer as before
- the new version uses a combination of S3 and reference classes
(see
ReferenceClasses, merPredD-class, and
lmResp-class) as well as S4 classes; partly for this reason
it is more interoperable with nlme
- 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 final
merMod object (see modular)
- profiling and parametric bootstrapping are new in
the current version
- the new version of lme4 does not provide
an
mcmcsamp (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 the pbkrtest package and leveraged by the
lmerTest package and the Anova function in the car package
(see pvalues for 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
installing lme4. (Because the problem is not with the
explicit version of the packages, but with running
packages that were built with different versions of Rcpp
in conjunction with each other, simply making sure you have
the latest version, or using
update.packages, will
not necessarily solve the problem; you must actually re-install
the packages. The problem is most likely with minqa.)