R: Control Values for lme Fit

lmeControl {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 and Douglas Bates bates@stat.wisc.edu

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)

[Package nlme version 3.1-104 Index]