lmeControl {lme4} | R Documentation |
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 in the lme
function.
lmeControl(maxIter, msMaxIter, tolerance, niterEM, msTol, msScale, msVerbose, returnObject, gradHess, apVar, .relStep, minAbsParApVar, nlmStepMax, natural, optimizer, EMverbose, analyticGradient, analyticHessian)
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 ) and the variance
function structure (varFunc ) have no "varying" parameters and
the pdMat classes used in the random effects structure are
pdLogChol (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) . |
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 calculated. Default is TRUE . |
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. |
a list with a component for each of the possible arguments.
# decrease the maximum number iterations in the ms call and # request that information on the evolution of the ms iterations be printed str(lmeControl(msMaxIter = 20, msVerbose = TRUE))