precog.sim: Perform 'precog.test' on simulated data.
In jfrench/smerc: Statistical Methods for Regional Counts

precog.sim

R Documentation

Perform `precog.test` on simulated data.

Description

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

Usage

precog.sim(
  nsim = 1,
  zones,
  ty,
  ex,
  w,
  pop,
  max_pop,
  logein,
  logeout,
  d,
  cl = NULL,
  tol_prob = 0.9,
  ysim = NULL
)

Arguments

`nsim`	The number of simulations from which to compute the p-value.
`zones`	A list with of candidate zones that includes each regions and its adjacent neighbors.
`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 maximum population size allowable for a cluster.
`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.
`d`	A precomputed distance matrix based on `coords`
`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.
`tol_prob`	A single numeric value between 0 and 1 that describes the quantile of the tolerance envelopes used to prefilter regions from the candidate zones.
`ysim`	A matrix of size `nsim\times n`, where `n` is the number of regions in the study area. This is a matrix of `nsim` realizations of the case counts for each region in the study area under the null hypothesis. This argument is only not meant to be used by the user.