lme.lmList               package:nlme               R Documentation

_L_M_E _f_i_t _f_r_o_m _l_m_L_i_s_t _O_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

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

_A_r_g_u_m_e_n_t_s:

   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?

_V_a_l_u_e:

     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.

_A_u_t_h_o_r(_s):

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

_R_e_f_e_r_e_n_c_e_s:

     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, (1997). 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.

_S_e_e _A_l_s_o:

     'lme', 'lmList', 'lmeObject'

_E_x_a_m_p_l_e_s:

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

