R: Control Values for nlme Fit

nlmeControl {nlme}

R Documentation

Control Values for nlme 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 nlme function.

Usage

nlmeControl(maxIter, pnlsMaxIter, msMaxIter, minScale,
            tolerance, niterEM, pnlsTol, msTol,
            returnObject, msVerbose, msWarnNoConv,
            gradHess, apVar, .relStep, minAbsParApVar = 0.05,
            opt = c("nlminb", "nlm"), natural = TRUE, sigma = NULL, ...)

Arguments

`maxIter`	maximum number of iterations for the `nlme` optimization algorithm. Default is 50.
`pnlsMaxIter`	maximum number of iterations for the `PNLS` optimization step inside the `nlme` optimization. Default is 7.
`msMaxIter`	maximum number of iterations for `nlminb` (`iter.max`) or the `nlm` (`iterlim`, from the 10-th step) optimization step inside the `nlme` optimization. Default is 50 (which may be too small for e.g. for overparametrized cases).
`minScale`	minimum factor by which to shrink the default step size in an attempt to decrease the sum of squares in the `PNLS` step. Default `0.001`.
`tolerance`	tolerance for the convergence criterion in the `nlme` 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.
`pnlsTol`	tolerance for the convergence criterion in `PNLS` step. Default is `1e-3`.
`msTol`	tolerance for the convergence criterion in `nlm`, passed as the `gradtol` argument to the function (see documentation on `nlm`). Default is `1e-7`.
`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`.
`msVerbose`	a logical value passed as the `trace` to `nlminb(.., control= list(trace = *, ..))` or as argument `print.level` to `nlm()`. Default is `FALSE`.
`msWarnNoConv`	logical indicating if a `warning` should be signalled whenever the minimization (by `opt`) in the LME step does not converge; defaults to `TRUE`.
`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)`.
`minAbsParApVar`	numeric value - minimum absolute parameter value in the approximate variance calculation. The default is `0.05`.
`opt`	the optimizer to be used, either `"nlminb"` (the default) or `"nlm"`.
`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`.
`sigma`	optionally a positive number to fix the residual error at. If `NULL`, as by default, or `0`, sigma is estimated.
`...`	Further, named control arguments to be passed to `nlminb` (apart from `trace` and `iter.max` mentioned above), where used (`eval.max` and those from `abs.tol` down).

Value

a list with components for each of the possible arguments.

Author(s)

José Pinheiro and Douglas Bates bates@stat.wisc.edu; the sigma option: Siem Heisterkamp and Bert van Willigen.

Examples

# decrease the maximum number of iterations and request tracing
nlmeControl(msMaxIter = 20, msVerbose = TRUE)

[Package nlme version 3.1-166 Index]