epi.bohning: Bohning's test for overdispersion of Poisson data

In epiR: Tools for the Analysis of Epidemiological Data

Bohning's test for overdispersion of Poisson data

Description

A test for overdispersion of Poisson data.

Usage

epi.bohning(obs, exp, alpha = 0.05)

Arguments

obs	the observed number of cases in each area.
exp	the expected number of cases in each area.
alpha	alpha level to be used for the test of significance. Must be a single number between 0 and 1.

Value

A data frame with two elements: test.statistic, Bohning's test statistic and p.value the associated P-value.

References

Bohning D (2000). Computer-assisted Analysis of Mixtures and Applications. Chapman and Hall, Boca Raton.

Ugarte MD, Ibanez B, Militino AF (2006). Modelling risks in disease mapping. Statistical Methods in Medical Research 15: 21 - 35.

Examples

## EXAMPLE 1:
data(epi.SClip)
obs <- epi.SClip$cases
pop <- epi.SClip$population
exp <- (sum(obs) / sum(pop)) * pop

epi.bohning(obs, exp, alpha = 0.05)

## Bohning's test was used to determine if there was statistically significant
## overdispersion in lip cancer cases across 56 Scottish districts for the 
## period 1975 to 1980. 

## The test statistic was 53.33. The associated P value was <0.01. We reject 
## the null hypothesis of no over dispersion and accept the null hypothesis
## concluding that the lip cancer data are over dispersed.

epiR documentation built on Nov. 20, 2023, 9:06 a.m.