BinaryEPPM-package {BinaryEPPM}R Documentation

Mean and Variance Modeling of Binary Data

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> .

Details

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.

Author(s)

David M Smith, Malcolm J Faddy

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

References

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.

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) 

[Package BinaryEPPM version 2.3 Index]