cepp.test | R Documentation |
cepp.test
implements the Cluster Evaluation
Permutation Procedure test of Turnbull et al. (1990)
for finding disease clusters.
cepp.test( coords, cases, pop, nstar, ex = sum(cases)/sum(pop) * pop, nsim = 499, alpha = 0.1, longlat = FALSE, simdist = "multinomial" )
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 |
simdist |
A character string indicating whether the
simulated data should come from a |
Returns a smerc_cluster
object.
Joshua French
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>
print.smerc_cluster
,
summary.smerc_cluster
,
plot.smerc_cluster
,
scan.test
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) data(nypoly) library(sp) plot(nypoly, col = color.clusters(out))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.