glmer.nb: Fitting Negative Binomial GLMMs

Description

Fits a generalized linear mixed-effects model (GLMM) for the negative binomial family, building on glmer, and initializing via theta.ml from MASS.

Usage

glmer.nb(..., interval = log(th) + c(-3, 3),
         tol = 5e-5, verbose = FALSE, nb.control = NULL,
         initCtrl = list(limit = 20, eps = 2*tol, trace = verbose,
                         theta = NULL))

Value

An object of class glmerMod, for which many methods are available (e.g. methods(class="glmerMod")), see

glmer.

Arguments

...: arguments as for glmer(.) such as formula, data, control, etc, but not family!
interval: interval in which to start the optimization. The default is symmetric on log scale around the initially estimated theta.
tol: tolerance for the optimization via optimize.
verbose: logical indicating how much progress information should be printed during the optimization. Use verbose = 2 (or larger) to enable verbose=TRUE in the glmer() calls.
nb.control: optional list, like the output of glmerControl(), used in refit(*, control = control.nb) during the optimization (control, if included in ..., will be used in the initial-stage glmer(...,family=poisson) fit, and passed on to the later optimization stages as well)
initCtrl: (experimental, do not rely on this:) a list with named components as in the default, passed to theta.ml (package MASS) for the initial value of the negative binomial parameter theta. May also include a theta component, in which case the initial estimation step is skipped

Examples

Run this code

set.seed(101)
dd <- expand.grid(f1 = factor(1:3),
                  f2 = LETTERS[1:2], g=1:9, rep=1:15,
          KEEP.OUT.ATTRS=FALSE)
summary(mu <- 5*(-4 + with(dd, as.integer(f1) + 4*as.numeric(f2))))
dd$y <- rnbinom(nrow(dd), mu = mu, size = 0.5)
str(dd)
require("MASS")## and use its glm.nb() - as indeed we have zero random effect:
if (FALSE) {
m.glm <- glm.nb(y ~ f1*f2, data=dd, trace=TRUE)
summary(m.glm)
m.nb <- glmer.nb(y ~ f1*f2 + (1|g), data=dd, verbose=TRUE)
m.nb
## The neg.binomial theta parameter:
getME(m.nb, "glmer.nb.theta")
LL <- logLik(m.nb)
## mixed model has 1 additional parameter (RE variance)
stopifnot(attr(LL,"df")==attr(logLik(m.glm),"df")+1)
plot(m.nb, resid(.) ~ g)# works, as long as data 'dd' is found
}

Run the code above in your browser using DataLab

Get 50% off unlimited learning