Mean and Scale-Factor Modeling of Under- And Over-Dispersed Binary Data
Calculation of vector of probabilities for the beta binomial distribut...
tools:::Rd_package_title("BinaryEPPM")
Fitting of EPPM models to binary data.
Calculation of vector of probabilities for the correlated binomial dis...
Extraction of hat matrix values from BinaryEPPM Objects
Function used to calculate the first derivatives of the log likelihood...
Function called by optim to calculate the log likelihood from the prob...
Extract Log-Likelihood
Log-log Link Function
Probabilities for beta and correlated binomial distributions given p's...
Extraction of model coefficients for BinaryEPPM Objects
Cook's distance for BinaryEPPM Objects
Double exponential Link Function
Double reciprocal Link Function
Calculation of vector of probabilities for a extended Poisson process ...
Extraction of fitted values from BinaryEPPM Objects
Calculation of vector of probabilities for the EPPM binomial distribut...
Function for obtaining output from distributional models.
Probabilities for binomial and EPPM extended binomial distributions gi...
Probabilities for EPPM extended binomial distributions given p's and s...
Negative complementary log-log Link Function
Diagnostic Plots for BinaryEPPM Objects
Power Logit Link Function
Prediction Method for BinaryEPPM Objects
Printing of BinaryEPPM Objects
Printing of summaryBinaryEPPM Objects
Residuals for BinaryEPPM Objects
Summary of BinaryEPPM Objects
Variance/Covariance Matrix for Coefficients
Wald Test of Nested Models for BinaryEPPM Objects
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) <doi:10.1002/bimj.201100214> and Smith and Faddy (2019) <doi:10.18637/jss.v090.i08>.