smerc (version 1.4.2)

rflex.midp: Compute middle p-value

Description

Computes P(Y > cases) + P(Y = cases)/2 when Y ~ Poisson(ex) or Y ~ Binomial(n = pop, p = ex/pop). This is middle p-value computed by Tango and Takahashi (2012).

Usage

rflex.midp(cases, ex, type = "poisson", pop = NULL)

Arguments

cases

The number of cases observed in each region.

ex

The expected number of cases for each region. The default is calculated under the constant risk hypothesis.

type

The type of scan statistic to compute. The default is "poisson". The other choice is "binomial".

pop

The population size associated with each region.

Value

A vector of middle p-values

References

Tango, T. and Takahashi, K. (2012), A flexible spatial scan statistic with a restricted likelihood ratio for detecting disease clusters. Statist. Med., 31: 4207-4218. <doi:10.1002/sim.5478>

Examples

# NOT RUN {
data(nydf)
cases = floor(nydf$cases)
pop = nydf$pop
ex = pop * sum(cases)/sum(pop)
# zones for poisson model
pp = rflex.midp(cases, ex)
# zones for binomial model
bp = rflex.midp(cases, ex, type = "binomial", pop = pop)
# }