Usage
lmeControl(maxIter, msMaxIter, tolerance, niterEM, msTol,
msScale, msVerbose, returnObject, gradHess, apVar,
.relStep, minAbsParApVar, nlmStepMax, natural, optimizer,
EMverbose, analyticGradient, analyticHessian)
Arguments
maxIter
maximum number of iterations for the lme
optimization algorithm. Default is 50.
msMaxIter
maximum number of iterations
for the nlm
optimization step inside the lme
optimization. Default is 50.
tolerance
tolerance for the convergence criterion in the
lme
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.
msTol
tolerance for the convergence criterion in nlm
,
passed as the rel.tolerance
argument to the function (see
documentation on nlm
). Default is 1e-7.
msScale
scale function passed as the scale
argument to
the nlm
function (see documentation on that function). Default
is lmeScale
.
msVerbose
a logical value passed as the trace
argument to
nlm
(see documentation on that function). 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
.
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
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$double.eps^(1/3)
.
minAbsParApVar
numeric value - minimum absolute parameter value
in the approximate variance calculation. The default is 0.05
.
nlmStepMax
stepmax value to be passed to nlm. See
nlm
for details. Default is 100.0 natural
a logical value indicating whether the pdNatural
parametrization should be used for general positive-definite matrices
(pdLogChol
) in reStruct
, when the approximate covariance
matrix of the estimators is cal
optimizer
the optimizer to be used - either "optim"
, the
default, or "nlm"
EMverbose
a logical value indicating if verbose output should be
produced during the EM iterations. Default is FALSE
.
analyticGradient
a logical value indicating if the analytic
gradient of the objective should be used. This option is for testing
purposes and would not normally be changed from the default. Default
is TRUE
.
analyticHessian
a logical value indicating if the analytic
hessian of the objective should be calculated. This is an
experimental feature and at present the default is FALSE
. In
future we may use the analytic Hessian in the optimization.
synopsis
lmeControl(maxIter = 50, msMaxIter = 50, tolerance =
sqrt((.Machine$double.eps)), niterEM = 25, msTol =
sqrt(.Machine$double.eps), msScale, msVerbose =
FALSE, glmmMaxIter = 20, returnObject = FALSE,
gradHess = TRUE, apVar = TRUE, .relStep =
(.Machine$double.eps)^(1/3), minAbsParApVar = 0.05,
nlmStepMax = NULL, natural = TRUE, optimizer = "nlm",
EMverbose = FALSE, analyticGradient = TRUE,
analyticHessian = FALSE)