smerc (version 1.3)

cepp.test: Cluster Evalation Permutation Procedure Test

Description

cepp.test implements the Cluster Evaluation Permutation Procedure test of Turnbull et al. (1990) for finding disease clusters.

Usage

cepp.test(
  coords,
  cases,
  pop,
  nstar,
  ex = sum(cases)/sum(pop) * pop,
  nsim = 499,
  alpha = 0.1,
  longlat = FALSE,
  simdist = "multinomial"
)

Arguments

coords

An \(n \times 2\) matrix of centroid coordinates for the regions.

cases

The number of cases observed in each region.

pop

The population size associated with each region.

nstar

The size of the at-risk population in each window.

ex

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

nsim

The number of simulations from which to compute the p-value.

alpha

The significance level to determine whether a cluster is signficant. Default is 0.10.

longlat

The default is FALSE, which specifies that Euclidean distance should be used. If longlat is TRUE, then the great circle distance is used to calculate the intercentroid distance.

simdist

A character string indicating whether the simulated data should come from a "multinomial" or "poisson" distribution. The default is "multinomial", which fixes the total number of cases observed in each simulated data set.

Value

Returns a smerc_cluster object.

References

Bruce W. Turnbull, Eric J. Iwano, William S. Burnett, Holly L. Howe, Larry C. Clark (1990). Monitoring for Clusters of Disease: Application to Leukemia Incidence in Upstate New York, American Journal of Epidemiology, 132(supp1):136-143. <doi:10.1093/oxfordjournals.aje.a115775>

See Also

print.smerc_cluster, summary.smerc_cluster, plot.smerc_cluster, scan.test

Examples

# NOT RUN {
data(nydf)
data(nyw)
coords = with(nydf, cbind(x, y))
cases = nydf$cases
pop = nydf$pop
out = cepp.test(coords = coords, cases = cases, pop = pop,
                nstar = 1000, alpha = 0.1)
plot(out)

data(nypoly)
library(sp)
plot(nypoly, col = color.clusters(out))
# }