BinaryEPPM-package {BinaryEPPM} | R Documentation |

Modeling under- and over-dispersed binary data using extended Poisson process models (EPPM) as in the article Faddy and Smith (2012) <doi:10.1002/bimj.201100214> .

The DESCRIPTION file:

Package: | BinaryEPPM |

Type: | Package |

Title: | Mean and Variance Modeling of Binary Data |

Version: | 2.3 |

Imports: | Formula, expm, numDeriv, stats, lmtest, grDevices, graphics |

Date: | 2019-07-30 |

Author: | David M Smith, Malcolm J Faddy |

Maintainer: | David M. Smith <smithdm1@us.ibm.com> |

Depends: | R (>= 3.5.0) |

Description: | Modeling under- and over-dispersed binary data using extended Poisson process models (EPPM) as in the article Faddy and Smith (2012) <doi:10.1002/bimj.201100214> . |

License: | GPL-2 |

Index of help topics:

BBprob Calculation of vector of probabilities for the beta binomial distribution. Berkshires.litters The data are of the number of male piglets born in litters of varying sizes for the Berkshire breed of pigs. BinaryEPPM Fitting of EPPM models to binary data. BinaryEPPM-package Mean and Variance Modeling of Binary Data CBprob Calculation of vector of probabilities for the correlated binomial distribution. EPPMprob Calculation of vector of probabilities for a extended Poisson process model (EPPM). GBprob Calculation of vector of probabilities for the generalized binomial distribution. GasolineYield Data on gasoline yields. Hiroshima.case Individual case data of chromosome aberrations in survivors of Hiroshima. Hiroshima.grouped Data of chromosome aberrations in survivors of Hiroshima grouped into dose ranges and represented as frequency distributions. KupperHaseman.case Kupper and Haseman example data LL.Regression.Binary Function called by optim to calculate the log likelihood from the probabilities and hence perform the fitting of regression models to the binary data. LL.gradient Function used to calculate the first derivatives of the log likelihood with respect to the model parameters. Luningetal.litters Number of trials (implantations) in data of Luning, et al., (1966) Model.BCBinProb Probabilities for beta and correlated binomial distributions given p's and scale-factors. Model.Binary Function for obtaining output from distributional models. Model.GB Probabilities for binomial and generalized binomial distributions given p's and b. Model.JMVGB Probabilities for generalized binomial distributions given p's and scale-factors. Parkes.litters The data are of the number of male piglets born in litters of varying sizes for the Parkes breed of pigs. Titanic.survivors.case Individual case data of Titanic survivors Titanic.survivors.grouped Titanic survivors data in frequency distribution form. Williams.litters Number of implantations, data of Williams (1996). Yorkshires.litters The data are of the number of male piglets born in litters of varying sizes for the Yorkshire breed of pigs. coef.BinaryEPPM Extraction of model coefficients for BinaryEPPM Objects cooks.distance.BinaryEPPM Cook's distance for BinaryEPPM Objects doubexp Double exponential Link Function doubrecip Double reciprocal Link Function fitted.BinaryEPPM Extraction of fitted values from BinaryEPPM Objects foodstamp.case Participation in the federal food stamp program. foodstamp.grouped Participation in the federal food stamp program as a list not a data frame. hatvalues.BinaryEPPM Extraction of hat matrix values from BinaryEPPM Objects logLik.BinaryEPPM Extract Log-Likelihood loglog Log-log Link Function negcomplog Negative complementary log-log Link Function plot.BinaryEPPM Diagnostic Plots for BinaryEPPM Objects powerlogit Power Logit Link Function predict.BinaryEPPM Prediction Method for BinaryEPPM Objects print.BinaryEPPM Printing of BinaryEPPM Objects print.summaryBinaryEPPM Printing of summaryBinaryEPPM Objects residuals.BinaryEPPM Residuals for BinaryEPPM Objects ropespores.case Dilution series for the presence of rope spores. ropespores.grouped Dilution series for the presence of rope spores. summary.BinaryEPPM Summary of BinaryEPPM Objects vcov.BinaryEPPM Variance/Covariance Matrix for Coefficients waldtest.BinaryEPPM Wald Test of Nested Models for BinaryEPPM Objects

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.

David M Smith, Malcolm J Faddy

Maintainer: David M. Smith <smithdm1@us.ibm.com>

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

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)

[Package *BinaryEPPM* version 2.3 Index]