elliptic.sim.adj {smerc}

Perform elliptic.test on simulated data

Description

elliptic.sim efficiently performs elliptic.test on a simulated data set. The function is meant to be used internally by the elliptic.test function, but is informative for better understanding the implementation of the test.

Usage

elliptic.sim.adj(
  nsim = 1,
  ex,
  nn,
  ty,
  logein,
  logeout,
  a,
  pen,
  min.cases = 2,
  cl = NULL
)

Arguments

nsim

A positive integer indicating the number of simulations to perform.

ex

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

nn

A list of nearest neighbors produced by elliptic.nn.

ty

The total number of cases in the study area.

logein

The log of the expected number of cases in each candidate zone.

logeout

The log of the expected number of cases outside of each candidate zone.

a

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

pen

The eccentricity penalty for each candidate zone.

min.cases

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

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.

Value

A vector with the maximum test statistic for each simulated data set.

Examples

data(nydf)
data(nyw)
coords <- with(nydf, cbind(longitude, latitude))
pop <- nydf$pop
enn <- elliptic.nn(coords, pop, ubpop = 0.5)
cases <- floor(nydf$cases)
ty <- sum(cases)
ex <- ty / sum(pop) * pop
yin <- nn.cumsum(enn$nn, cases)
ein <- nn.cumsum(enn$nn, ex)
logein <- log(ein)
logeout <- log(ty - ein)
pen <- elliptic.penalty(0.5, enn$shape_all)
tsim <- elliptic.sim.adj(
  nsim = 3, ex = ex,
  nn = enn$nn, ty = ty,
  logein = logein, logeout = logeout,
  a = 0.5, pen = pen
)