nlme: glsControl – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

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, msVerbose,
           singular.ok, returnObject = FALSE, apVar, .relStep,
           opt = c("nlminb", "optim"), optimMethod,
           minAbsParApVar, natural, sigma = NULL)

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 of the first outer iteration when `optim` is used. Default is 1e-7.
`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`.
`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`.
`.relStep`	relative step for numerical derivatives calculations. Default is `.Machine$double.eps^(1/3)`.
`opt`	the optimizer to be used, either `"nlminb"` (the current default) or `"optim"` (the previous default).
`optimMethod`	character - the optimization method to be used with the `optim` optimizer. The default is `"BFGS"`. An alternative is `"L-BFGS-B"`.
`minAbsParApVar`	numeric value - minimum absolute parameter value in the approximate variance calculation. The default is `0.05`.
`natural`	logical. Should the natural parameterization be used for the approximate variance calculations? Default is `TRUE`.
`sigma`	optionally a positive number to fix the residual error at. If `NULL`, as by default, or `0`, sigma is estimated.

Value

a list with components for each of the possible arguments.

Author(s)

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

Examples

# 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)

nlme

Linear and Nonlinear Mixed Effects Models

v3.1-152

GPL (>= 2) | file LICENCE

Authors

José Pinheiro [aut] (S version), Douglas Bates [aut] (up to 2007), Saikat DebRoy [ctb] (up to 2002), Deepayan Sarkar [ctb] (up to 2005), EISPACK authors [ctb] (src/rs.f), Siem Heisterkamp [ctb] (Author fixed sigma), Bert Van Willigen [ctb] (Programmer fixed sigma), Johannes Ranke [ctb] (varConstProp()), R-core [aut, cre]

Initial release

2021-02-03