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, 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 the `nlm` optimization step inside the `nlme` optimization. Default is 50.
`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` argument to `nlm` (see documentation on that function). 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)`.
`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`, 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.

### See Also

`nlme`, `nlm`, `optim`, `nlmeStruct`

### Examples

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

[Package nlme version 3.1-131 Index]