dc.sim {smerc}

Perform dc.test on simulated data

Description

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

Usage

dc.sim(nsim = 1, nn, ty, ex, w, pop, max_pop, cl = NULL)

Arguments

nsim

A positive integer indicating the number of simulations to perform.

nn

A list of distance-based nearest neighbors, preferably from the nndist function.

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.

w

A binary spatial adjacency matrix for the regions.

pop

The population size associated with each region.

max_pop

The population upperbound (in total population) for a candidate zone.

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))
cases <- floor(nydf$cases)
pop <- nydf$pop
ty <- sum(cases)
ex <- ty / sum(pop) * pop
d <- gedist(coords, longlat = TRUE)
nn <- nndist(d, ubd = 0.05)
max_pop <- sum(pop) * 0.25
tsim <- dc.sim(1, nn, ty, ex, nyw,
  pop = pop,
  max_pop = max_pop
)