BinaryEPPM (version 3.0)

BinaryEPPM-package: tools:::Rd_package_title("BinaryEPPM")

Description

Under- and over-dispersed binary data are modeled using an extended Poisson process model (EPPM) appropriate for binary data. A feature of the model is that the under-dispersion relative to the binomial distribution only needs to be greater than zero, but the over-dispersion is restricted compared to other distributional models such as the beta and correlated binomials. Because of this, the examples focus on under-dispersed data and how, in combination with the beta or correlated distributions, flexible models can be fitted to data displaying both under- and over-dispersion. Using Generalized Linear Model (GLM) terminology, the functions utilize linear predictors for the probability of success and scale-factor with various link functions for p, and log link for scale-factor, to fit a variety of models relevant to areas such as bioassay. Details of the EPPM are in Faddy and Smith (2012) and Smith and Faddy (2019). Two important changes from version 2.3 are the change to scale-factor rather than variance modeling, and the inclusion of a vignette.

Author

tools:::Rd_package_author("BinaryEPPM")

Maintainer: tools:::Rd_package_maintainer("BinaryEPPM")

Details

tools:::Rd_package_indices("BinaryEPPM")

References

Cribari-Neto F, Zeileis A. (2010). Beta Regression in R. Journal of Statistical Software, 34(2), 1-24. tools:::Rd_expr_doi("10.18637/jss.v034.i02").

Faddy M, Smith D. (2012). Extended Poisson Process Modeling and Analysis of Grouped Binary Data. Biometrical Journal, 54, 426-435. tools:::Rd_expr_doi("10.1002/bimj.201100214").

Grun B, Kosmidis I, Zeileis A. (2012). Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned. Journal of Statistical Software, 48(11), 1-25. tools:::Rd_expr_doi("10.18637/jss.v048.i11").

Smith D, Faddy M. (2019). Mean and Variance Modeling of Under-Dispersed and Over-Dispersed Grouped Binary Data. Journal of Statistical Software, 90(8), 1-20. tools:::Rd_expr_doi("10.18637/jss.v090.i08").

Zeileis A, Croissant Y. (2010). Extended Model Formulas in R: Multiple Parts and Multiple Responses. Journal of Statistical Software, 34(1), 1-13. tools:::Rd_expr_doi("10.18637/jss.v034.i01").

See Also

CountsEPPM betareg

Examples

data("ropespores.case")
output.fn <- BinaryEPPM(data = ropespores.case,
                  number.spores / number.tested ~ 1 + offset(logdilution),
                  model.type = 'p only', model.name = 'binomial')                 
summary(output.fn)