lmeControl               package:nlme               R Documentation

Control Values for lme Fit

Description:

     The values supplied in the function call replace the defaults and
     a list with all possible arguments is returned. The returned list
     is used as the 'control' argument to the 'lme' function.

Usage:

     lmeControl(maxIter, msMaxIter, tolerance, niterEM, msMaxEval, msTol,
                msScale, msVerbose, returnObject, gradHess, apVar,
                .relStep, minAbsParApVar, nlmStepMax,
                opt = c("nlminb", "optim"), optimMethod,
                natural)

Arguments:

 maxIter: maximum number of iterations for the 'lme' optimization
          algorithm. Default is 50.

msMaxIter: maximum number of iterations for the 'nlm' optimization step
          inside the 'lme' optimization. Default is 50.

tolerance: tolerance for the convergence criterion in the 'lme'
          algorithm. Default is 1e-6.

 niterEM: number of iterations for the EM algorithm used to refine the
          initial estimates of the random effects variance-covariance
          coefficients. Default is 25.

msMaxEval: maximum number of evaluations of the objective function
          permitted for nlminb. Default is 200. 

   msTol: tolerance for the convergence criterion in 'nlm', passed as
          the 'rel.tolerance' argument to the function (see
          documentation on 'nlm'). Default is 1e-7. 

 msScale: scale function passed as the 'scale' argument to the 'nlm'
          function (see documentation on that function). Default is
          'lmeScale'.

msVerbose: a logical value passed as the 'trace' argument to 'nlm' (see
          documentation on that function). Default is 'FALSE'.

returnObject: a logical value indicating whether the fitted object
          should be returned when the maximum number of iterations is
          reached without convergence of the algorithm. Default is
          'FALSE'.

gradHess: a logical value indicating whether numerical gradient vectors
          and Hessian matrices of the log-likelihood function should be
          used in the 'nlm' optimization. This option is only available
          when the correlation structure ('corStruct') and the variance
          function structure ('varFunc') have no "varying" parameters
          and the 'pdMat' classes used in the random effects structure
          are 'pdSymm' (general positive-definite), 'pdDiag'
          (diagonal), 'pdIdent' (multiple of the identity),  or
          'pdCompSymm' (compound symmetry). Default is 'TRUE'.

   apVar: a logical value indicating whether the approximate covariance
          matrix of the variance-covariance parameters should be
          calculated. Default is 'TRUE'.

.relStep: relative step for numerical derivatives calculations. Default
          is '.Machine$double.eps^(1/3)'.

nlmStepMax: stepmax value to be passed to nlm. See 'nlm' for details.
          Default is 100.0

     opt: the optimizer to be used, either 'nlminb' (the default since
          (R 2.2.0) or 'optim' (the previous default).

optimMethod: character - the optimization method to be used with the
          'optim' optimizer. The default is '"BFGS"'.  An alternative
          is '"L-BFGS-B"'.

minAbsParApVar: numeric value - minimum absolute parameter value in the
          approximate variance calculation.  The default is '0.05'.

 natural: a logical value indicating whether the 'pdNatural'
          parametrization should be used for general positive-definite
          matrices ('pdSymm') in 'reStruct', when the approximate
          covariance matrix of the estimators is calculated. Default is
          'TRUE'.

Value:

     a list with components for each of the possible arguments.

Author(s):

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

See Also:

     'lme', 'nlm', 'optim', 'lmeScale'

Examples:

     # decrease the maximum number iterations in the ms call and
     # request that information on the evolution of the ms iterations be printed
     lmeControl(msMaxIter = 20, msVerbose = TRUE)

