lme.groupedData {nlme}  R Documentation 
The response variable and primary covariate in formula(fixed)
are used to construct the fixed effects model formula. This formula
and the groupedData
object 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 'groupedData' lme(fixed, data, random, correlation, weights, subset, method, na.action, control, contrasts, keep.data = TRUE)
fixed 
a data frame inheriting from class 
data 
this argument is included for consistency with the generic function. It is ignored in this method function. 
random 
optionally, any of the following: (i) a onesided formula
of the form 
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 on the general framework of Lindstrom,
M.J. and Bates, D.M. (1988). The model formulation is described in
Laird, N.M. and Ware, J.H. (1982). The variancecovariance
parametrizations are described in Pinheiro, J.C. and Bates., D.M.
(1996). The different correlation structures available for the
correlation
argument are described in Box, G.E.P., Jenkins,
G.M., and Reinsel G.C. (1994), Littel, R.C., Milliken, G.A., Stroup,
W.W., and Wolfinger, R.D. (1996), and Venables, W.N. and Ripley,
B.D. (2002). The use of variance functions for linear and nonlinear
mixed effects models is presented in detail in Davidian, M. and
Giltinan, D.M. (1995).
Box, G.E.P., Jenkins, G.M., and Reinsel G.C. (1994) "Time Series Analysis: Forecasting and Control", 3rd Edition, HoldenDay.
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, 963974.
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, 10141022.
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, 289296.
Pinheiro, J.C., and Bates, D.M. (2000) "MixedEffects Models in S and SPLUS", Springer.
Venables, W.N. and Ripley, B.D. (2002) "Modern Applied Statistics with S", 4th Edition, SpringerVerlag.
fm1 < lme(Orthodont) summary(fm1)