smerc (version 1.8.3)

elliptic.test: Elliptical Spatial Scan Test

Description

elliptic.test performs the elliptical scan test of Kulldorf et al. (2006).

Usage

elliptic.test(
  coords,
  cases,
  pop,
  ex = sum(cases)/sum(pop) * pop,
  nsim = 499,
  alpha = 0.1,
  ubpop = 0.5,
  shape = c(1, 1.5, 2, 3, 4, 5),
  nangle = c(1, 4, 6, 9, 12, 15),
  a = 0.5,
  cl = NULL,
  type = "poisson",
  min.cases = 2
)

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.

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.

ubpop

The upperbound of the proportion of the total population to consider for a cluster.

shape

The ratios of the major and minor axes of the desired ellipses.

nangle

The number of angles (between 0 and 180) to consider for each shape.

a

The penalty for the spatial scan statistic. The default is 0.5.

cl

A cluster object created by makeCluster, or an integer to indicate number of child-processes (integer values are ignored on Windows) for parallel evaluations (see Details on performance). It can also be "future" to use a future backend (see Details), NULL (default) refers to sequential evaluation.

type

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

min.cases

The minimum number of cases required for a cluster. The default is 2.

Author

Joshua French

Details

The test is performed using the spatial scan test based on the Poisson test statistic and a fixed number of cases. Candidate zones are elliptical and extend from the observed data locations. The clusters returned are non-overlapping, ordered from most significant to least significant. The first cluster is the most likely to be a cluster. If no significant clusters are found, then the most likely cluster is returned (along with a warning).

References

Kulldorff, M. (1997) A spatial scan statistic. Communications in Statistics - Theory and Methods, 26(6): 1481-1496, <doi:10.1080/03610929708831995>

Kulldorff, M., Huang, L., Pickle, L. and Duczmal, L. (2006) An elliptic spatial scan statistic. Statististics in Medicine, 25:3929-3943. <doi:10.1002/sim.2490>

See Also

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

Examples

data(nydf)
coords <- nydf[, c("x", "y")]
if (FALSE) {
# run only a small number of sims to make example fast
out <- elliptic.test(
  coords = coords,
  cases = floor(nydf$cases),
  pop = nydf$pop, ubpop = 0.1,
  nsim = 19,
  alpha = 0.12)
}