KupperHaseman.case

Data of the number of deaths out of number of implants for pregnant female mice
for two groups each of size 10.

 datasets

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>.

David Smith

BinaryEPPM

Mean and Scale-Factor Modeling of Under- And Over-Dispersed
Binary Data

KupperHaseman.case function

A data frame with 20 observations on the following 3 variables.<dl>
 <dt><code>Group</code></dt>
<dd>a factor with levels <code>Control</code> <code>Treated</code></dd> <dt><code>Number.Deaths</code></dt>
<dd>a numeric vector</dd> <dt><code>Number.Implants</code></dt>
<dd>a numeric vector</dd> 
</dl>

Format

Kupper and Haseman example data — KupperHaseman.case

A data frame with 20 observations on the following 3 variables.<dl>
 <dt><code>Group</code></dt>
<dd>a factor with levels <code>Control</code> <code>Treated</code></dd>

 <dt><code>Number.Deaths</code></dt>
<dd>a numeric vector</dd>

 <dt><code>Number.Implants</code></dt>
<dd>a numeric vector</dd>

 
</dl>

KupperHaseman.case: Kupper and Haseman example data

Description

Usage

Arguments

Format

Examples