nlme.nlsList              package:nlme              R Documentation

_N_L_M_E _f_i_t _f_r_o_m _n_l_s_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 'nlme.formula', together
     with any other additional arguments in the function call. See the
     documentation on 'nlme.formula' for a description of that
     function.

_U_s_a_g_e:

     ## S3 method for class 'nlsList':
     nlme(model, data, fixed, random, groups, start, correlation, weights,
          subset, method, na.action, naPattern, control, verbose)

_A_r_g_u_m_e_n_t_s:

   model: an object inheriting from class 'nlsList', representing a
          list of 'nls' fits with a common model.

    data: this argument is included for consistency with the generic
          function. It is ignored in this method function.

   fixed: 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)'.

  groups: an optional one-sided formula of the form '~g1' (single level
          of nesting) or '~g1/.../gQ' (multiple levels of nesting),
          specifying the partitions of the data over which the random
          effects vary. 'g1,...,gQ' must evaluate to factors in 'data'.
          The order of nesting, when multiple levels are present, is
          taken from left to right (i.e. 'g1' is the first level, 'g2'
          the second, etc.).

   start: an optional numeric vector, or list of initial estimates for
          the fixed effects and random effects. If declared as a
          numeric vector, it is converted internally to a list with a
          single component 'fixed', given by the vector. The 'fixed'
          component is required, unless the model function inherits
          from class 'selfStart', in which case initial values will be
          derived from a call to 'nlsList'. An optional 'random'
          component is used to specify initial values for the random
          effects and should consist of a matrix, or a list of matrices
          with length equal to the number of grouping levels. Each
          matrix should have as many rows as the number of groups at
          the corresponding level and as many columns as the number of
          random effects in that level.

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 homoscesdatic 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 '"ML"'.

na.action: a function that indicates what should happen when the data
          contain 'NA's.  The default action ('na.fail') causes 'nlme'
          to print an error message and terminate if there are any
          incomplete observations.

naPattern: an expression or formula object, specifying which returned
          values are to be regarded as missing.

 control: a list of control values for the estimation algorithm to
          replace the default values returned by the function
          'nlmeControl'. Defaults to an empty list.

 verbose: an optional logical value. If 'TRUE' information on the
          evolution of the iterative algorithm is printed. Default is
          'FALSE'.

_V_a_l_u_e:

     an object of class 'nlme' 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 'nlmeObject' 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.

     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:

     'nlme', 'lmList', 'nlmeObject'

_E_x_a_m_p_l_e_s:

     fm1 <- nlsList(SSasymp, data = Loblolly)
     fm2 <- nlme(fm1, random = Asym ~ 1)
     summary(fm1)
     summary(fm2)

