mbest (version 0.6.1)

mhglm.control: Auxiliary for Controlling Moment Heirarchical GLM Fitting

Description

Auxiliary function for mhglm fitting. Typically only used internally by mhglm.fit, but may be used to construct a control argument to either function.

Usage

mhglm.control(standardize = TRUE, steps = 1, parallel = FALSE, diagcov = FALSE,
        fit.method = "firthglm.fit", fixef.rank.warn = FALSE, cov.rank.warn = FALSE,
        cov.psd.warn = TRUE, fit.control = list(...), ...)
mhglm_ml.control(standardize = FALSE, steps = 1, parallel = FALSE, diagcov = FALSE,
              fit.method = "firthglm.fit", fixef.rank.warn = FALSE, cov.rank.warn = FALSE,
              cov.psd.warn = FALSE, fit.control = list(...), ...)

Value

A list with components named as the arguments.

Arguments

standardize

logitcal indicating if predictors should be standardized before moment-based fitted

steps

number of refinement steps

parallel

fit the group-specific estimates in parallel rather than sequentially

diagcov

estimate random effect covairance matrix with diagonal approximation

fit.method

method for obtaining group-specific effect estimates

fixef.rank.warn

if TRUE, print warnings when fixef is unidentifiable

cov.rank.warn

if TRUE, print warnings when covariance matrix is unidentifiable

cov.psd.warn

if TRUE, print warnings when moment based estimates of covariance matrix is not positive semi-definite

fit.control

control parameters for fit.method

...

arguments to be used to form the fit.control argument if it is not supplied directly.

Details

Setting standardize = TRUE ensures that the procedure is equivariant, and generally leads to better estimation performance. Right now standardize = TRUE is not allowed for mhglm_ml.

The steps argument gives the number of refinement steps for the moment based parameters. In each step, the previous fixed effect and random effect covariance matrix estimates are used to weight the subpopulation-specific effect estimates. In principle, higher values of steps could lead to more accurate estimates, but in simulations, the differences are negligible.

See Also

mhglm.fit, the fitting procedure used by mhglm.

firthglm.fit, the default subpopulation-specific fitting method.

Examples

if(requireNamespace("lme4")) { # for cbpp data

 data("cbpp", package = "lme4")
# The default fitting method uses Firth's bias-corrected estimates
(gm.firth <- mhglm(cbind(incidence, size - incidence) ~ period + (1 | herd),
                   data = cbpp, family = binomial,
                   control=mhglm.control(fit.method="firthglm.fit")))

# Using maximum likelihood estimates is less reliable
(gm.ml <- mhglm(cbind(incidence, size - incidence) ~ period + (1 | herd),
                data = cbpp, family = binomial,
                control=mhglm.control(fit.method="glm.fit")))
}