BinaryEPPM (version 2.3)

BinaryEPPM-package: BinaryEPPM

Description

BinaryEPPM

Details

The DESCRIPTION file: BinaryEPPM BinaryEPPM 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 regression models. Smith and Faddy (2019) gives further details about the package as well as examples of its use.

References

Cribari-Neto F, Zeileis A. (2010). Beta Regression in R. Journal of Statistical Software, 34(2), 1-24. 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. 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. 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. 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. 10.18637/jss.v034.i01.

See Also

CountsEPPM betareg

Examples

# NOT RUN {
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) 
# }