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 = 100.0, minAbsParApVar = 0.05,
            opt = c("nlminb", "nlm"), natural = TRUE)

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.
opt the optimizer to be used, either nlminb (the default since (R 2.2.0) or nlm (the previous default).
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)

[Package nlme version 3.1-80 Index]