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.
lme(fixed, data, random, correlation, weights, subset, method, na.action, control)
lmList
, representing a list of
lm
fits with a common model.
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)
.
lme
.
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.
Bates, D.M. and Pinheiro, J.C. (1998). Computational methods for multilevel models. Available in PostScript or PDF formats at http://nlme.stat.wisc.edu.
Box, G.E.P., Jenkins, G.M., and Reinsel G.C. (1994). Time Series Analysis: Forecasting and Control (3rd Edition). San Francisco: Holden-Day.
Davidian, M. and Giltinan, D.M. (1995). Nonlinear Mixed Effects Models for Repeated Measurement Data. London: 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. Cary, North Carolina: SAS Institute, Inc.
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. (1997). Modern Applied Statistics with S-PLUS (2nd Edition). New York: Springer-Verlag.
The computational methods are described in Bates and Pinheiro (1998), and follow on 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 Reinsel (1994), Littel, Milliken, Stroup, and Wolfinger (1996), and Venables and Ripley (1997).
The use of variance functions for linear and nonlinear mixed effects models is presented in detail in Davidian and Giltinan (1995).
fm1 <- lmList(Orthodont) fm2 <- lme(fm1)