# lme.groupedData

##### LME fit from groupedData Object

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.

- Keywords
- models

##### Usage

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

##### Arguments

- fixed
- a data frame inheriting from class
`groupedData`

. - data
- this argument is included for consistency with the generic function. It is ignored in this method function.
- 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
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. (1997). The use of variance functions for linear
and nonlinear mixed effects models is presented in detail in Davidian,
M. and Giltinan, D.M. (1995).

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 <- lme(Orthodont)
summary(fm1)
```

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