lme4 (version 1.0-5)

lmerControl: Control of Mixed Model Fitting

Description

Construct a control structure for mixed model fitting.

Usage

lmerControl(optimizer = "Nelder_Mead",
    restart_edge = TRUE, sparseX = FALSE,
    check.nobs.vs.rankZ = "warningSmall",
    check.nobs.vs.nlev = "stop",
    check.nlev.gtreq.5 = "ignore",
    check.nlev.gtr.1 = "stop", optCtrl = list())
glmerControl(optimizer = c("bobyqa", "Nelder_Mead"),
    restart_edge = FALSE, sparseX = FALSE,
    check.nobs.vs.rankZ = "warningSmall",
    check.nobs.vs.nlev = "stop",
    check.nlev.gtreq.5 = "ignore",
    check.nlev.gtr.1 = "stop", tolPwrss = 1e-07,
    compDev = TRUE, optCtrl = list())
nlmerControl(optimizer = "Nelder_Mead", tolPwrss = 1e-10,
             optCtrl = list())

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

sparseX

logical - should a sparse model matrix be used for the fixed-effects terms? Defaults to FALSE. 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)?

check.nlev.gtreq.5

character - rules for checking whether all random effects have >= 5 levels. "ignore": skip the test. "warning": warn if test fails. "stop": throw an error if test fails.

check.nlev.gtr.1

character - rules for checking whether all random effects have > 1 level. As for check.nlevel.gtr.5.

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 check.nlevel.gtre

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 check.nlevel.gtreq.5.

optCtrl

a list of additional arguments to be passed to the nonlinear optimizer (see Nelder_Mead, bobyqa

tolPwrss

numeric scalar - the tolerance for declaring convergence in the penalized iteratively weighted residual sum-of-squares step. Defaults to 1e-7.

compDev

logical scalar - should compiled code be used for the deviance evaluation during the optimization of the parameter estimates? Defaults to TRUE.

Value

• a list (of class "merControl") containing (1) general control parameters (e.g. optimizer, restart_edge); (2) a list of data-checking specifications (e.g. check.nobs.vs.rankZ); (3) parameters to be passed to the optimizer, i.e., the optCtrl list, which may contain maxiter.)

Details

If checking options are set via options, [g]lmerControl will use them rather than the default values (but will not override values that are passed as explicit arguments); for example, options(lmerControl=list(check.nobs.vs.rankZ = "ignore")) will suppress warnings that the number of observations is less than the rank of the random effects model matrix Z.

Examples

str(lmerControl())