lme.lmList {nlme}  R Documentation 
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 class 'lmList' lme(fixed, data, random, correlation, weights, subset, method, na.action, control, contrasts, keep.data)
fixed 
an object inheriting from class 
data 
this argument is included for consistency with the generic function. It is ignored in this method function. 
random 
an optional onesided linear formula with no conditioning
expression, or a 
correlation 
an optional 
weights 
an optional 
subset 
an optional expression indicating the subset of the rows of

method 
a character string. If 
na.action 
a function that indicates what should happen when the
data contain 
control 
a list of control values for the estimation algorithm to
replace the default values returned by the function 
contrasts 
an optional list. See the 
keep.data 
logical: should the 
an object of class lme
representing the linear mixedeffects
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.
JosÃ© 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 variancecovariance 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) "RandomEffects Models for Longitudinal Data", Biometrics, 38, 963–974.
Lindstrom, M.J. and Bates, D.M. (1988) "NewtonRaphson and EM Algorithms for Linear MixedEffects Models for RepeatedMeasures 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 VarianceCovariance Matrices", Statistics and Computing, 6, 289–296.
Venables, W.N. and Ripley, B.D. (2002) "Modern Applied Statistics with S", 4th Edition, SpringerVerlag.
fm1 < lmList(Orthodont) fm2 < lme(fm1) summary(fm1) summary(fm2)