# lme.lmList

##### LME fit from lmList Object

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.

- Keywords
- models

##### Usage

```
lme(fixed, data, random, correlation, weights, subset, method,
na.action, control)
```

##### Arguments

- 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 o - other arguments
- identical to the arguments in the generic
function call. See the documentation on
`lme`

.

##### Value

- 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.

##### References

The computational methods are described in Bates, D.M. and Pinheiro
(1998) and 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 variance-covariance parametrizations are described in

Bates, D.M. and Pinheiro, J.C. (1998) "Computational methods for multilevel models" available in PostScript or PDF formats at http://franz.stat.wisc.edu/pub/NLME/ 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. (1997) "Modern Applied Statistics with S-plus", 2nd Edition, Springer-Verlag.

##### See Also

##### Examples

`library(nlme)`

```
data(Orthodont)
fm1 <- lmList(Orthodont)
fm2 <- lme(fm1)
summary(fm1)
summary(fm2)
```

*Documentation reproduced from package nlme, version 3.1-1, License: GPL version 2 or later*