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)
```

fixed

an object inheriting from class `"lmList."`

,
representing a list of `lm`

fits with a common model.

data

this argument is included for consistency with the generic function. It is ignored in this method function.

random

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)`

.

correlation

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.

weights

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.

subset

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.

method

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"`

.

na.action

a function that indicates what should happen when the
data contain `NA`

s. The default action (`na.fail`

) causes
`lme`

to print an error message and terminate if there are any
incomplete observations.

control

a list of control values for the estimation algorithm to
replace the default values returned by the function `lmeControl`

.
Defaults to an empty list.

contrasts

an optional list. See the `contrasts.arg`

of `model.matrix.default`

.

keep.data

logical: should the `data`

argument (if supplied
and a data frame) be saved as part of the model object?

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.

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.

```
# NOT RUN {
fm1 <- lmList(Orthodont)
fm2 <- lme(fm1)
summary(fm1)
summary(fm2)
# }
```

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