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"
)

Value

Returns a smerc_cluster object.

Arguments

coords: An $n \times 2$ matrix of centroid coordinates for the regions in the form (x, y) or (longitude, latitude) is using great circle distance.
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.

Author

Joshua French

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>

Examples

Run this code

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 = 0.99
)
plot(out)
summary(out)

# better plotting
if (require("sf", quietly = TRUE)) {
   data(nysf)
   plot(st_geometry(nysf), col = color.clusters(out))
}

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