Usage
glsControl(maxIter, msMaxIter, tolerance, msTol, msVerbose,
singular.ok, returnObject, 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.