robustlmm-package | R Documentation |
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.
The robustlmm
package implements most of the analysis tool chain
as is customary in R. The usual functions such as
summary
, coef
,
resid
, etc. are provided as long as they are applicable
for this type of models (see 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.
Details on the implementation and example analyses are provided in the
package vignette available via vignette("rlmer")
(Koller 2016).
Manuel Koller (2016). robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models. Journal of Statistical Software, 75(6), 1-24. doi:10.18637/jss.v075.i06
Koller M, Stahel WA (2022). "Robust Estimation of General Linear Mixed Effects Models.” In PM Yi, PK Nordhausen (eds.), Robust and Multivariate Statistical Methods, Springer Nature Switzerland AG.
Manuel Koller (2013). Robust estimation of linear mixed models. (Doctoral dissertation, Diss., Eidgenössische Technische Hochschule ETH Zürich, Nr. 20997, 2013).
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