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, msScale,
                returnObject, msVerbose, gradHess, apVar, .relStep,
                nlmStepMax, minAbsParApVar, natural)

### 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 `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`.
`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)`.
`nlmStepMax` | stepmax value to be passed to nlm. See `nlm` for details. Default is 100.0
`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

`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)

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