Parkes.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

Parkes.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] 2 20 41 35 14 4
  ..$ : num [1:7] 3 16 53 78 53 18 0
  ..$ : num [1:8] 0 21 63 117 104 46 21 2
  ..$ : num [1:9] 1 8 37 81 162 77 30 5 1
  ..$ : num [1:10] 0 2 23 72 101 83 46 12 7 0
  ..$ : num [1:11] 0 7 8 19 79 82 48 24 10 0 ...
  ..$ : num [1:12] 0 1 3 15 15 33 13 12 8 1 ...</p>

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

Description

Usage

Arguments

Format

Examples