uls.sim: Perform 'uls.test' on simulated data

In smerc: Statistical Methods for Regional Counts

Perform uls.test on simulated data

Description

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

Usage

uls.sim(
  nsim = 1,
  ty,
  ex,
  w,
  pop,
  ubpop,
  type = "poisson",
  check.unique = FALSE,
  cl = NULL
)

Arguments

nsim	A positive integer indicating the number of simulations to perform.
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.
ubpop	The upperbound of the proportion of the total population to consider for a cluster.
type	The type of scan statistic to compute. The default is "poisson". The other choice is "binomial".
check.unique	A logical value indicating whether a check for unique values should be determined. The default is FALSE. This is unlikely to make a practical different for most real data sets.
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
tsim <- uls.sim(1, ty, ex, nyw, pop = pop, ubpop = 0.5)

smerc documentation built on Oct. 25, 2024, 1:07 a.m.