This package provides Bayesian variable selection for regression models of under-reported count data as well as for (overdispersed) Poisson, negative binomial and binomial logit regression models using spike and slab priors. For posterior inference, MCMC sampling schemes are used that rely on data augmentation and/or auxiliary mixture sampling techniques. Details can be found in Dvorzak and Wagner (2016).
The main function is
pogitBvs which provides Bayesian variable
selection for a Poisson-Logistic (Pogit) model to account for potential
under-reporting of count data. The Pogit model, introduced by Winkelmann
and Zimmermann (1993), is specified by combining a Poisson model for the data
generating process of counts and a logit model for the fallible reporting
process, where the outcomes of both processes may depend on a set of
By augmenting the observed data with the unobserved counts, the model
can be factorized into a Poisson and a binomial logit model part. Hence,
the MCMC sampling algorithm for this two-part model is based on
data augmentation and sampling schemes for a Poisson and a binomial
Though part of the main function, the functions
logitBvs can be used separately to perform
Bayesian variable selection for Poisson or binomial logit regression models.
An alternative to
poissonBvs is provided by the function
negbinBvs to deal with overdispersion of count data.
The sampling algorithms are based on auxiliary mixture sampling
All functions return an object of class "
pogit" with methods
plot.pogit to summarize and display the results.
Michaela Dvorzak <[email protected]>, Helga Wagner
Maintainer: Michaela Dvorzak <[email protected]>
Dvorzak, M. and Wagner, H. (2016). Sparse Bayesian modelling of underreported count data. Statistical Modelling, 16(1), 24 - 46, http://dx.doi.org/10.1177/1471082x15588398.
Winkelmann, R. and Zimmermann, K. F. (1993). Poisson-Logistic regression. Department of Economics, University of Munich, Working Paper No. 93 - 18.
## see examples for pogitBvs, logitBvs, poissonBvs and negbinBvs
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