Learn R
nlme (version 3.1-1)

glsControl: Control Values for gls 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 gls function.

Usage

glsControl(maxIter, msMaxIter, tolerance, msTol, msScale, msVerbose,
           singular.ok, qrTol, returnObject, apVar, nlmStepMax, .relStep)

Arguments

maxIter
maximum number of iterations for the gls optimization algorithm. Default is 50.
msMaxIter
maximum number of iterations for the optimization step inside the gls optimization. Default is 50.
tolerance
tolerance for the convergence criterion in the gls algorithm. Default is 1e-6.
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.
msVerbose
a logical value passed as the trace argument to ms (see documentation on that function). Default is FALSE.
singular.ok
a logical value indicating whether non-estimable coefficients (resulting from linear dependencies among the columns of the regression matrix) should be allowed. Default is FALSE.
qrTol
a tolerance for detecting linear dependencies among the columns of the regression matrix in its QR decomposition. Default is .Machine$single.eps.
returnObject
a logical value indicating whether the fitted object should be returned when the maximum number of iterations is reached without convergence of the algorithm. Default is FALSE.
apVar
a logical value indicating whether the approximate covariance matrix of the variance-covariance parameters should be calculated. Default is TRUE.
nlmStepMax
stepmax value to be passed to nlm. See nlm for details. Default is 100.0
.relStep
relative step for numerical derivatives calculations. Default is .Machine$double.eps^(1/3).

Value

  • a list with components for each of the possible arguments.

See Also

gls, lmeScale

Examples

Run this code
# decrease the maximum number iterations in the optimization call and
# request that information on the evolution of the ms iterations be printed
glsControl(msMaxIter = 20, msVerbose = TRUE)

Run the code above in your browser using DataCamp Workspace