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, natural, natUnconstrained, sigma) 

OPTIONAL 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 ms 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 ms, passed as the rel.tolerance argument to the function (see documentation on ms). Default is 1e-7.
msScale
scale function passed as the scale argument to the ms 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 ms (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 ms 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).
natural
a logical value, or a named list of logical values, indicating whether a natural parameterization should be used for the model structures, when the approximate covariance matrix of the estimators is calculated. If given as a single logical value, it is used for all model structures ( pdMat, corStruct, and varStruct objects) used in the fit. If given as a list, it must have names reStruct, corStruct, and varStruct corresponding to the model structures used in the fit. Default is TRUE.
natUnconstrained
a logical value, or a named list of logical values, indicating whether an unconstrained parameterization should be used for the natural parameters of the model structures. If given as a single logical value, it is used for all model structures ( pdMat, corStruct, and varFunc objects) used in the fit. If given as a list, it must have names reStruct, corStruct, and varStruct corresponding to the model structures used in the fit. Default is TRUE.
sigma
a numeric value indicating the value at which the within-group standard error should be kept fixed during the optmization of the objective function. Defaults to NULL, in which case the within-group standard error is estimated together with the other model parameters. Must be a non-negative numeric value - setting it to zero has the same effect as the default ( NULL).

VALUE:

a list with components for each of the possible arguments.

SEE ALSO:

, ,

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)