If the random effects names defined in random are a subset of
the lmList object coefficient names, initial estimates for the
covariance matrix of the random effects are obtained (overwriting any
values given in random). formula(fixed) and the
data argument in the calling sequence used to obtain
fixed are passed as the fixed and data arguments
to lme.formula, together with any other additional arguments in
the function call. See the documentation on lme.formula for a
description of that function.
# S3 method for lmList
lme(fixed, data, random, correlation, weights, subset, method,
na.action, control, contrasts, keep.data)an object of class lme representing the linear mixed-effects
model fit. Generic functions such as print, plot and
summary have methods to show the results of the fit. See
lmeObject for the components of the fit. The functions
resid, coef, fitted, fixed.effects, and
random.effects can be used to extract some of its components.
an object inheriting from class "lmList.",
representing a list of lm fits with a common model.
this argument is included for consistency with the generic function. It is ignored in this method function.
an optional one-sided linear formula with no conditioning
expression, or a pdMat object with a formula
attribute. Multiple levels of grouping are not allowed with this
method function. Defaults to a formula consisting of the right hand
side of formula(fixed).
an optional corStruct object describing the
within-group correlation structure. See the documentation of
corClasses for a description of the available corStruct
classes. Defaults to NULL,
corresponding to no within-group correlations.
an optional varFunc object or one-sided formula
describing the within-group heteroscedasticity structure. If given as
a formula, it is used as the argument to varFixed,
corresponding to fixed variance weights. See the documentation on
varClasses for a description of the available varFunc
classes. Defaults to NULL, corresponding to homoscedastic
within-group errors.
an optional expression indicating the subset of the rows of
data that should be used in the fit. This can be a logical
vector, or a numeric vector indicating which observation numbers are
to be included, or a character vector of the row names to be
included. All observations are included by default.
a character string. If "REML" the model is fit by
maximizing the restricted log-likelihood. If "ML" the
log-likelihood is maximized. Defaults to "REML".
a function that indicates what should happen when the
data contain NAs. The default action (na.fail) causes
lme to print an error message and terminate if there are any
incomplete observations.
a list of control values for the estimation algorithm to
replace the default values returned by the function lmeControl.
Defaults to an empty list.
an optional list. See the contrasts.arg
of model.matrix.default.
logical: should the data argument (if supplied
and a data frame) be saved as part of the model object?
José Pinheiro and Douglas Bates bates@stat.wisc.edu
The computational methods follow the general framework of Lindstrom
and Bates (1988). The model formulation is described in Laird and Ware
(1982). The variance-covariance parametrizations are described in
Pinheiro and Bates (1996). The different correlation structures
available for the correlation argument are described in Box,
Jenkins and Reinse (1994), Littel et al (1996), and Venables and
Ripley, (2002). The use of variance functions for linear and nonlinear
mixed effects models is presented in detail in Davidian and Giltinan
(1995).
Box, G.E.P., Jenkins, G.M., and Reinsel G.C. (1994) "Time Series Analysis: Forecasting and Control", 3rd Edition, Holden--Day.
Davidian, M. and Giltinan, D.M. (1995) "Nonlinear Mixed Effects Models for Repeated Measurement Data", Chapman and Hall.
Laird, N.M. and Ware, J.H. (1982) "Random-Effects Models for Longitudinal Data", Biometrics, 38, 963--974.
Lindstrom, M.J. and Bates, D.M. (1988) "Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data", Journal of the American Statistical Association, 83, 1014--1022.
Littel, R.C., Milliken, G.A., Stroup, W.W., and Wolfinger, R.D. (1996) "SAS Systems for Mixed Models", SAS Institute.
Pinheiro, J.C. and Bates., D.M. (1996) "Unconstrained Parametrizations for Variance-Covariance Matrices", Statistics and Computing, 6, 289--296.
Venables, W.N. and Ripley, B.D. (2002) "Modern Applied Statistics with S", 4th Edition, Springer-Verlag.
lme, lmList,
lmeObject
fm1 <- lmList(Orthodont)
fm2 <- lme(fm1)
summary(fm1)
summary(fm2)
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