flex.sim: Perform 'flex.test' on simualated data
In smerc: Statistical Methods for Regional Counts

Description Usage Arguments Value Examples

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

flex.sim(
  nsim = 1,
  zones,
  ty,
  ex,
  type = "poisson",
  ein = NULL,
  eout = NULL,
  tpop = NULL,
  popin = NULL,
  popout = NULL,
  cl = NULL
)

`nsim`	A positive integer indicating the number of simulations to perform.
`zones`	A list of zones to compute the test statistic over for each simulated data set.
`ty`	The total number of cases in the study area.
`ex`	The expected number of cases for each region. The default is calculated under the constant risk hypothesis.
`type`	The type of scan statistic to compute. The default is `"poisson"`. The other choice is `"binomial"`.
`ein`	The expected number of cases in the zone. Conventionally, this is the estimated overall disease risk across the study area, multiplied by the total population size of the zone.
`eout`	The expected number of cases outside the zone. This should be `ty - ein` and is computed automatically if not provided.
`tpop`	The total population in the study area.
`popin`	The total population in the zone.
`popout`	The population outside the zone. This should be `tpop - popin` and is computed automatically if not provided.
`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).

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

data(nydf)
data(nyw)
coords = with(nydf, cbind(longitude, latitude))
zones = flex.zones(coords, w = nyw, k = 3, longlat = TRUE)
cases = floor(nydf$cases)
ty = sum(cases)
ex = ty/sum(nydf$pop) * nydf$pop
ein = zones.sum(zones, ex)
tsim = flex.sim(nsim = 2, zones, ty, ex, ein = ein, eout = ty - ein)