lmeControl {lme4} | R Documentation

## Control values for lme

### 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 in the `lme` function.

### Usage

    lmeControl(maxIter, msMaxIter, tolerance, niterEM, msTol,
               msScale, msVerbose, returnObject, gradHess, apVar,
               .relStep, minAbsParApVar, nlmStepMax, natural, optimizer,
               EMverbose, analyticGradient, analyticHessian)

### 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.
`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 `pdLogChol` (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`.
`nlmStepMax` | stepmax value to be passed to nlm. See `nlm` for details. Default is 100.0
`natural` | a logical value indicating whether the `pdNatural` parametrization should be used for general positive-definite matrices (`pdLogChol`) in `reStruct`, when the approximate covariance matrix of the estimators is calculated. Default is `TRUE`.
`optimizer` | the optimizer to be used - either `"optim"`, the default, or `"nlm"`
`EMverbose` | a logical value indicating if verbose output should be produced during the EM iterations. Default is `FALSE`.
`analyticGradient` | a logical value indicating if the analytic gradient of the objective should be used. This option is for testing purposes and would not normally be changed from the default. Default is `TRUE`.
`analyticHessian` | a logical value indicating if the analytic hessian of the objective should be calculated. This is an experimental feature and at present the default is `FALSE`. In future we may use the analytic Hessian in the optimization.

### Value

a list with a component for each of the possible arguments.

### 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)

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