eda.od {ZIGP}R Documentation

Exploratory data analysis tool for overdispersion level

Description

'eda.od' performs an impact study on the influence of a covariate on the overdispersion design (where the shifted log-link is assumed). Thereby, a discretation using scoring classes will be applied and the overdispersion function be calculated for each scoring class (see Czado et. al (2007)).

Usage

eda.od(x, y, Offset=rep(1,length(y)), numberclasses=5)

Arguments

x Covariate considered
y Response considered
Offset Exposure for individual observation lengths. Defaults to a vector of 1. The offset MUST NOT be in 'log' scale.
numberclasses Number of classes for discretization. Defaults to 5.

Details

As covariate x, discrete or continuous variables may be considered. Categorical covariates only make sense if they have only two levels.

References

Czado, C., Erhardt, V., Min, A., Wagner, S. (2007) Zero-inflated generalized Poisson models with regression effects on the mean, dispersion and zero-inflation level applied to patent outsourcing rates. Statistical Modelling 7 (2), 125-153.

Examples

data(Seatbelts)
DriversKilled <- as.vector(Seatbelts[,1])           # will be response
kms <- as.vector(Seatbelts[,5]/mean(Seatbelts[,5])) # will be exposure
PetrolPrice <- as.vector(Seatbelts[,6])             # will be covariate 1
law <- as.vector(Seatbelts[,8])                     # will be covariate 2

eda.od(x=PetrolPrice, y=DriversKilled, Offset=kms)
eda.od(x=PetrolPrice, y=DriversKilled, Offset=kms, numberclasses=20)
eda.od(x=law, y=DriversKilled, Offset=kms)

[Package ZIGP version 3.3 Index]