epiR (version 2.0.80)

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

Description

A test for overdispersion of Poisson data.

Usage

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

Value

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

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.

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:
## Lip cancer in Scotland, 1975 to 1980:
data(epi.SClip)
obs <- epi.SClip$cases
pop <- epi.SClip$population

## Calculate the expected number of lip cancer cases per district:
exp <- (sum(obs) / sum(pop)) * pop

## Bohning's test:
epi.bohning(obs, exp, alpha = 0.05)

## 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.