Usage
lmerControl(optimizer = "bobyqa", restart_edge = TRUE,
boundary.tol = 1e-5,
calc.derivs=TRUE,
use.last.params=FALSE,
sparseX = FALSE,
## input checking options
check.nobs.vs.rankZ = "ignore",
check.nobs.vs.nlev = "stop",
check.nlev.gtreq.5 = "ignore",
check.nlev.gtr.1 = "stop",
check.nobs.vs.nRE="stop",
check.rankX = c("message+drop.cols", "silent.drop.cols", "warn+drop.cols",
"stop.deficient", "ignore"),
check.scaleX = c("warning","stop","silent.rescale",
"message+rescale","warn+rescale","ignore"),
check.formula.LHS = "stop",
## convergence checking options
check.conv.grad = .makeCC("warning", tol = 2e-3, relTol = NULL),
check.conv.singular = .makeCC(action = "ignore", tol = 1e-4),
check.conv.hess = .makeCC(action = "warning", tol = 1e-6),
## optimizer args
optCtrl = list())glmerControl(optimizer = c("bobyqa", "Nelder_Mead"),
restart_edge = FALSE,
boundary.tol = 1e-5,
calc.derivs=TRUE,
use.last.params=FALSE,
sparseX = FALSE,
tolPwrss=1e-7,
compDev=TRUE,
nAGQ0initStep=TRUE,
## input checking options
check.nobs.vs.rankZ = "ignore",
check.nobs.vs.nlev = "stop",
check.nlev.gtreq.5 = "ignore",
check.nlev.gtr.1 = "stop",
check.nobs.vs.nRE="stop",
check.rankX = c("message+drop.cols", "silent.drop.cols", "warn+drop.cols",
"stop.deficient", "ignore"),
check.scaleX = "warning",
check.formula.LHS = "stop",
check.response.not.const = "stop",
## convergence checking options
check.conv.grad = .makeCC("warning", tol = 1e-3, relTol = NULL),
check.conv.singular = .makeCC(action = "ignore", tol = 1e-4),
check.conv.hess = .makeCC(action = "warning", tol = 1e-6),
## optimizer args
optCtrl = list())
nlmerControl(optimizer = "Nelder_Mead", tolPwrss = 1e-10,
optCtrl = list())
.makeCC(action, tol, relTol, ...)
Arguments
optimizer
character - name of optimizing
function(s). A character vector or list of functions: length 1 for
lmer
or glmer
, possibly length 2 for glmer
).
The built-in optimizers are
calc.derivs
logical - compute gradient and Hessian of nonlinear
optimization solution?
use.last.params
logical - should the last value of the
parameters evaluated (TRUE
), rather than the value of the
parameters corresponding to the minimum deviance, be returned?
This is a "backward bug-compatibility" option; use TRUE
sparseX
logical - should a sparse model matrix be
used for the fixed-effects terms?
Currently inactive.
restart_edge
logical - should the optimizer
attempt a restart when it finds a solution at the
boundary (i.e. zero random-effect variances or perfect
+/-1 correlations)? (Currently only implemented for
lmerControl
.)
boundary.tol
numeric - within what distance of
a boundary should the boundary be checked for a better fit?
(Set to zero to disable boundary checking.)
tolPwrss
numeric scalar - the tolerance for declaring
convergence in the penalized iteratively weighted residual
sum-of-squares step.
compDev
logical scalar - should compiled code be
used for the deviance evaluation during the optimization
of the parameter estimates?
nAGQ0initStep
do one initial run with nAGQ = 0
.
check.nlev.gtreq.5
character - rules for
checking whether all random effects have >= 5 levels.
See action
.
check.nlev.gtr.1
character - rules for checking
whether all random effects have > 1 level. See action
.
check.nobs.vs.rankZ
character - rules for
checking whether the number of observations is greater
than (or greater than or equal to) the rank of the random
effects design matrix (Z), usually necessary for
identifiable variances. As for action
, wi
check.nobs.vs.nlev
character - rules for checking whether the
number of observations is less than (or less than or equal to) the
number of levels of every grouping factor, usually necessary for
identifiable variances. As for action
.
nobs<
check.nobs.vs.nRE
character - rules for
checking whether the number of observations is greater
than (or greater than or equal to) the number of random-effects
levels for each term, usually necessary for identifiable variances.
As for check.nobs.vs.nle
check.conv.grad
rules for checking the gradient of the deviance
function for convergence. A list as returned
by .makeCC
, or a character string with only the action.
check.conv.singular
rules for checking for a singular fit,
i.e. one where some parameters are on the boundary of the feasible
space (for example, random effects variances equal to 0 or
correlations between random effects equal to +/- 1.0);
as for check.
check.conv.hess
rules for checking the Hessian of the deviance
function for convergence.; as for check.conv.grad
above.
check.rankX
character - specifying if rankMatrix(X)
should be compared with ncol(X)
and if columns from the design
matrix should possibly be dropped to ensure that it has full rank.
So check.scaleX
character - check for problematic scaling of
columns of fixed-effect model matrix, e.g. parameters measured on
very different scales.
check.formula.LHS
check whether specified formula has
a left-hand side. Primarily for internal use within
simulate.merMod
;
use at your own risk as it may allow the generation
of unstable merMod
objects
check.response.not.const
character - check that the
response is not constant.
action
character - generic choices for the severity level
of any test. "ignore": skip the test. "warning": warn if test fails.
"stop": throw an error if test fails.
tol
numeric - tolerance for check
relTol
numeric - tolerance for checking relative variation
...
other elements to include in check specification