Berkshires.litters

<p>The data are arranged as a list of binomial frequency distributions where the 
listing is by litter size which is included both as a variate (vsize) and as 
a factor (fsize)</p>

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

Berkshires.litters function

<p>The format is:
List of 3
 $ fsize         : Factor w/ 7 levels " size 5"," size 6",..: 1 2 3 4 5 6 7
 $ vsize         : int [1:7] 5 6 7 8 9 10 11
 $ number.success:List of 7
  ..$ : num [1:6] 8 29 72 65 40 3
  ..$ : num [1:7] 5 22 89 129 74 35 4
  ..$ : num [1:8] 1 25 62 131 136 89 26 5
  ..$ : num [1:9] 1 15 79 179 219 149 71 33 4
  ..$ : num [1:10] 2 6 47 117 172 181 117 40 9 2
  ..$ : num [1:11] 2 1 23 65 131 145 120 61 20 3 ...
  ..$ : num [1:12] 0 3 9 22 53 94 72 54 20 4 ...</p>

Berkshires.litters: The data are of the number of male piglets born in litters of varying sizes for the Berkshire breed of pigs.

Description

Usage

Arguments

Format

Examples