A method to fit linear mixed effects models robustly. Robustness is achieved by modification of the scoring equations combined with the Design Adaptive Scale approach.
robustlmm provides functions for estimating linear mixed
effects models in a robust way.
The main workhorse is the function
rlmer; it is implemented as direct
robust analogue of the popular
lmer function of the
lme4 package. The
two functions have similar abilities and limitations. A wide range of data
structures can be modeled: mixed effects models with hierarchical as well
as complete or partially crossed random effects structures are
possible. While the
lmer function is optimized to handle large datasets
efficiently, the computations employed in the
rlmer function are more
complex and for this reason also more expensive to compute. The two
functions have the same limitations in the support of different random
effect and residual error covariance structures. Both support only diagonal
and unstructured random effect covariance structures.
robustlmm package implements most of the analysis tool chain as is
customary in R. The usual functions such as
etc. are provided as long as they are applicable for this type of models
rlmerMod-class for a full list). The functions are designed to be as
similar as possible to the ones in the
lme4 package to make switching
between the two packages easy.
This R-package is available on CRAN. Install it directly in R with the command
This package requires
lme4 version at least
1.1 and other
packages. Make sure to install them as well.
You can also install the package directly from github:
install.packages("devtools") ## if not already installed require(devtools) install_github("robustlmm", "kollerma") require(robustlmm)