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Linear mixed-effects models (LMMs) are an important class of statistical models
that can be used to analyze correlated data. Such data include clustered
observations, repeated measurements, longitudinal measurements, multivariate
observations, etc.
The aim of our book is to help readers in fitting LMMs using R software. R
(www.r-project.org) is a language and an environment aimed at facilitating
implementation of statistical methodology and graphics. It is an open-source
software, which can be freely downloaded and used under the GNU General
Public License. In particular, users can define and share their own functions, which
implement various methods and extend the functionality of R. This feature makes R
a very useful platform for propagating the knowledge and use of statistical methods.
We believe that, by describing selected tools available in R for fitting LMMs,
we can promote the broader application of the models. To help readers less familiar
with this class of linear models (LMs), we include in our book a description of the
most important theoretical concepts and features of LMMs. Moreover, we present
examples of applications of the models to real-life datasets from various areas to
illustrate the main features of both theory and software. |
