|Installing and using glmmADMB in R |
- Download glmmADMB.zip.
- On the "Packages" menu in R, choose "Install package(s) from local zip file..."
Using the package in R:
- Download glmmADMB_0.3.tar.gz.
- Consult the
R Installation and Administration manual on how to install the package.
Note that the ADMB-RE executables create temporary files (sometimes large), so you should start R in a
specially dedicated directory.
- library("glmmADMB") to load the package into R
- help("glmm.admb") and see example at the bottom of the help page.
|Source code |
Source code: glmmADMB includes two binaries ("nbmm" and "bvprobit"). On request from
R-users we make the (AD Model Builder) source code for these available here:
nbmm.tpl and bvprobit.tpl. (In order
to compile the tpl-files you need to buy AD Model Builder.)
|ADMB User Forum |
Questions relating to the R-package should be posted to the
ADMB user forum
under the topic "ADMB NBMM for R"|
Zero-inflation and overdispersion currently receive much attention in the statistical literature, e.g:|
For count responses, the situation of excess zeros (relative to what standard models allow) often
occurs in biomedical and sociological applications. Modeling repeated measures of zero-inflated count
data presents special challenges. This is because in addition to the problem of extra zeros,
the correlation between measurements upon the same subject at different occasions needs to be taken into
Min and Agresti (2005),
The R-package glmmADMB provides a GLMM framework (in
the spirit of glmmPQL and GLMM) with:
In addition it is possible to fit data with Bernoulli response (0 or 1):
- Negative binomial or Poisson responses.
- Zero-inflation, e.g. a mixture of a Poisson or negative binomial distribution and a point mass at zero.
glmmADMB is developed using ADMB-RE, but the full unrestricted
R-package is made freely available and does not require ADMB-RE to run with user supplied data.
- Logistic or probit link function
By default glmm.admb() uses the Laplace approximation, which is beleived to be superior to
the PQL method used by other mixed model routines in R. Hence, the likelihood values returned
by glmm.admb() can be used construct the AIC criterion for model comparison, and to perform likelihood
ratio tests. For situations where the Laplace approximation is not accurate enough, importance sampling
is an option of glmm.admb().
Beyond the standard GLMM framework
ADMB-RE provides a full
programming language for random effects modeling. The code for glmmADMB is
nbmm.tpl. Using ADMB-RE it is easy to modify nbmm.tpl to non-standard
situations, such as:
Details and examples of how to build ADMB-RE programs can be found here: user manual.
- zero-inflation with P(zero) depending on covariates.
- Distributions of different types: e.g. response (X,Y) with X Bernoulli and Y Poisson.
- Crossed random effects.