The nlme software 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). Some of the new
features of nlme 3.0 are:
- Linear and nonlinear multilevel models for data with multiple nested
grouping levels. These use new computational methods described in
(PostScript)
http://nlme.stat.wisc.edu/CompMulti.ps
or (Adobe Portable Document or "Acrobat" Format)
http://nlme.stat.wisc.edu/CompMulti.pdf
- a new class, called groupedData, for representing data grouped
according to one, or several nested factors. Methods for plotting,
summarizing, and fitting groupedData objects are also included.
- extensive use of Trellis plots for exploring grouped data and
checking models fitted to such data. The plot methods for fitted
objects include a formula argument which gives them unlimited
flexibility for generating diagnostic plots.
- new classes of correlation structures including ARMA(p,q) models,
spatial correlation structures (Gaussian, exponential, etc) with and
without nugget effects, general correlation with no particular
structure, and a Huyn-Feldt structure.
- new and redesigned classes of variance functions using arbitrary
covariates and grouping of parameters (the previous version used the
fitted values as the covariate for the built-in variance
functions). Because the correlation structure and the variance
function are independently specified, heterogeneous AR1, ARMA, etc
models are naturally handled.
- a gls function for fitting linear regression models with correlation
structures and/or variance functions. That is, lme without random
effects (or lm with correlation structures and/or variance functions).
- a gnls function for fitting nonlinear regression models with
correlation structures and/or variance functions. That is, nlme
without random effects (or nls with correlation structures and/or
variance functions).
- an extensive set of examples. A separate directory contains
groupedData objects for the datasets used in the book "SAS System for
Mixed Models", by Littel, Milliken, Stroup, and Wolfinger. Sample lme
analyses that parallel the PROC MIXED analyses from that book are also
given. We hope this will enable people who are familiar with PROC
MIXED to learn lme more quickly.
Those interested in becoming beta testers can obtain the code from
http://cm.bell-labs.com/stat/project/nlme/Beta
ftp://cm.bell-labs.com/cm/ms/departments/sia/project/nlme/Beta
or
http://nlme.stat.wisc.edu/Beta
ftp://nlme.stat.wisc.edu/pub/NLME/Beta
This new release is available as a gzip'd tar file (nlme3_0b5.tar.gz).
If you use ftp to transfer one of these files, remember to set binary
mode for the transfer.
If you decide to test the code, we strongly recommend you subscribe to
nlme-announce@stat.wisc.edu and also to nlme-help@stat.wisc.edu. Send
a message with the word "subscribe" in the body to
nlme-announce-request@stat.wisc.edu and to
nlme-help-request@stat.wisc.edu. The first list should be very low
traffic as it will just provide announcements of new beta versions
(can you say "bug fixes"?). The second is the recommended list for
beta-testers to ask questions about why things don't work the way they
expect them to.
-- Jose' Pinheiro (jcp@research.bell-labs.com)
-- Douglas Bates (bates@stat.wisc.edu)
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