lme.lmList               package:nlme               R Documentation

LME fit from lmList Object

Description:

     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.

Usage:

     ## S3 method for class 'lmList':
     lme(fixed, data, random, correlation, weights, subset, method,
         na.action, control, contrasts, keep.data)

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 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 homocesdatic 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?

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.

Author(s):

     Jose Pinheiro Jose.Pinheiro@pharma.novartis.com and Douglas Bates
     bates@stat.wisc.edu

References:

     The computational methods follow 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).

     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:

     'lme', 'lmList', 'lmeObject'

Examples:

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

