nlmeControl: 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, ...)

Value

a list with components for each of the possible arguments.

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).

Author

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

Examples

Run this code

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