lme4: glmer.nb – R documentation

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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))

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

Value

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

Note

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

The negative binomial θ 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 θ, 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)).

Examples

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:
## Not run: 
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

## End(Not run)

lme4

Linear Mixed-Effects Models using 'Eigen' and S4

v1.1-26

GPL (>= 2)

Authors

Douglas Bates [aut] (<https://orcid.org/0000-0001-8316-9503>), Martin Maechler [aut] (<https://orcid.org/0000-0002-8685-9910>), Ben Bolker [aut, cre] (<https://orcid.org/0000-0002-2127-0443>), Steven Walker [aut] (<https://orcid.org/0000-0002-4394-9078>), Rune Haubo Bojesen Christensen [ctb] (<https://orcid.org/0000-0002-4494-3399>), Henrik Singmann [ctb] (<https://orcid.org/0000-0002-4842-3657>), Bin Dai [ctb], Fabian Scheipl [ctb] (<https://orcid.org/0000-0001-8172-3603>), Gabor Grothendieck [ctb], Peter Green [ctb] (<https://orcid.org/0000-0002-0238-9852>), John Fox [ctb], Alexander Bauer [ctb], Pavel N. Krivitsky [ctb, cph] (<https://orcid.org/0000-0002-9101-3362>, shared copyright on simulate.formula)

Initial release