Mixed-effects models provide a powerful and flexible tool for the analysis of balanced and unbalanced grouped data. These data arise in several areas of investigation and are characterized by the presence of correlation between observations within the same group. Some examples are repeated measures data, longitudinal studies, and nested designs. Classical modeling techniques which assume independence of the observations are not appropriate for grouped data.
The NLME software comprises a set of S (S-PLUS) functions, methods, and classes for the analysis of both linear and nonlinear mixed-effects models. It extends the linear and nonlinear modeling facilities available in release 3 of S and S-PLUS. Written by José C. Pinheiro and Douglas M. Bates , the NLME software is available for Unix and Windows platforms.
NLME library has been completely redesigned to
emphasize a modular, object-oriented design that facilitates
users' incorporating new methods for existing generic functions
(e.g. new classes of correlation structures and variance
functions). The latest release includes several bug corrections
with respect to Release 3.3; no new features have been added.
# S-PLUS 3.4 tar zxf nlme3_3_1.tgz # if you have the GNU version of tar gunzip -d -c nlme3_3.tgz | tar xf - # otherwise # S-PLUS 5.1 tar zxf nlme_SP5_3_3_1.tgz # if you have the GNU version of tar gunzip -d -c nlme_SP5_3_3_1.tgz | tar xf - # otherwise # S-PLUS 6.0 tar zxf nlme_SP6_3_3_1.tgz # if you have the GNU version of tar gunzip -d -c nlme_SP6_3_3_1.tgz | tar xf - # otherwiseThis will create three subdirectories
./SAS_Mixed. Read the file
WinZipto expand it. The nlme3.zip file for S-PLUS 6.0 is a drop-in replacement, which should be extracted directly into the library subdirectory of the S-PLUS 6 installation. Read the
INSTALLfiles for further instructions.
groupedData, for representing data grouped according to one or more nested factors. Methods for plotting, summarizing, and fitting groupedData objects are also included.
ARMA(p,q)models, spatial correlation structures (Gaussian, exponential, etc.) with and without nugget effects, and general correlation with no particular structure. Users can extend the NLME library by writing their own classes of correlation structures.
glsfunction for fitting linear models with correlation structures and/or variance functions. You can think of it as
lmewithout random effects, or
lmwith correlation structures and variance functions.
gnlsfunction for fitting nonlinear models with correlation structures and/or variance functions. You can think of it as
nlmewithout random effects, or
nlswith correlation structures and variance functions.
lmeanalyses that parallel the
PROC MIXEDanalyses from that book are also given. We hope this will enable people who are familiar with PROC MIXED to learn lme more quickly.
Mixed-Effects Models in S and S-PLUS A book published by Springer-Verlag describes the use of NLME in detail and includes many examples of real-life applications involving linear and nonlinear mixed-effects models.
The distribution of NLME 3.3.1 available at this site includes a (relatively incomplete) User's Guide in PDF format. Also included with this distribution is a PDF file with all help files available in NLME 3.3.1. On-line help is available for all functions and fitted objects in the NLME 3.3.1 library.
Documentation on NLME 2.1 is included in chapter 2 (pp. 19-79) of S-PLUS Version 3.4 for Unix Supplement , Data Analysis Products Division, MathSoft, Seattle. The complements to W. Venables and B. Ripley's Modern Applied Statistics with S-PLUS books include new sections on the NLME modeling facilities.
A general reference on nonlinear mixed effects models is M. Davidian and D. Giltinan, Nonlinear Models for Repeated Measurement Data, Chapman and Hall, 1995.