glmer.nb {lme4}R Documentation

Fitting Negative Binomial GLMMs


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


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))



arguments as for glmer(.) such as formula, data, control, etc, but not family!


interval in which to start the optimization. The default is symmetric on log scale around the initially estimated theta.


tolerance for the optimization via optimize.


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.


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)


(experimental, do not rely on this:) a list with named components as in the default, passed to (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


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


For historical reasons, the shape parameter of the negative binomial and the random effects parameters in our (G)LMM models are both called theta (\theta), but are unrelated here.

The negative binomial \theta can be extracted from a fit g <- glmer.nb() by getME(g, "glmer.nb.theta").

Parts of glmer.nb() are still experimental and methods are still missing or suboptimal. In particular, there is no inference available for the dispersion parameter \theta, yet.

To fit a negative binomial model with known overdispersion parameter (e.g. as part of a model comparison exercise, use glmer with the negative.binomial family from the MASS package, e.g. glmer(...,family=MASS::negative.binomial(theta=1.75)).

See Also

glmer; from package MASS, negative.binomial (which we re-export currently) and, the latter for initialization of optimization.

The ‘Details’ of pnbinom for the definition of the negative binomial distribution.


dd <- expand.grid(f1 = factor(1:3),
                  f2 = LETTERS[1:2], g=1:9, rep=1:15,
summary(mu <- 5*(-4 + with(dd, as.integer(f1) + 4*as.numeric(f2))))
dd$y <- rnbinom(nrow(dd), mu = mu, size = 0.5)
require("MASS")## and use its glm.nb() - as indeed we have zero random effect:
## Not run: 
m.glm <- glm.nb(y ~ f1*f2, data=dd, trace=TRUE)
m.nb <- glmer.nb(y ~ f1*f2 + (1|g), data=dd, verbose=TRUE)
## The neg.binomial theta parameter:
getME(m.nb, "glmer.nb.theta")
LL <- logLik(m.nb)
## mixed model has 1 additional parameter (RE variance)
plot(m.nb, resid(.) ~ g)# works, as long as data 'dd' is found

## End(Not run)

[Package lme4 version 1.1-35.5 Index]