lme.groupedData             package:nlme             R Documentation

_L_M_E _f_i_t _f_r_o_m _g_r_o_u_p_e_d_D_a_t_a _O_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

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

_A_r_g_u_m_e_n_t_s:

   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.

  random: optionally, any of the following: (i) a one-sided formula of
          the form '~x1+...+xn | g1/.../gm', with 'x1+...+xn'
          specifying the model for the random effects and 'g1/.../gm'
          the grouping structure ('m' may be equal to 1, in which case
          no '/' is required). The random effects formula will be
          repeated for all levels of grouping, in the case of multiple
          levels of grouping; (ii) a list of one-sided formulas of the
          form '~x1+...+xn | g', with possibly different random effects
          models for each grouping level. The order of nesting will be
          assumed the same as the order of the elements in the list;
          (iii) a one-sided formula of the form '~x1+...+xn', or a
          'pdMat' object with a formula (i.e. a non-'NULL' value for
          'formula(object)'), or a list of such formulas or 'pdMat'
          objects. In this case, the grouping structure formula will be
          derived from the data used to fit the linear mixed-effects
          model, which should inherit from class 'groupedData'; (iv) a
          named list of formulas or 'pdMat' objects as in (iii), with
          the grouping factors as names. The order of nesting will be
          assumed the same as the order of the order of the elements in
          the list; (v) an 'reStruct' object. See the documentation on
          'pdClasses' for a description of the available 'pdMat'
          classes. Defaults to a formula consisting of the right hand
          side of '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 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).

     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.

     Pinheiro, J.C., and Bates, D.M. (2000) "Mixed-Effects Models in S
     and S-PLUS", Springer.  

     Venables, W.N. and Ripley, B.D. (1997) "Modern Applied Statistics
     with S-plus", 2nd Edition, Springer-Verlag.

_S_e_e _A_l_s_o:

     'lme', 'groupedData', 'lmeObject'

_E_x_a_m_p_l_e_s:

     fm1 <- lme(Orthodont)
     summary(fm1)

