smerc (version 1.8.3)

precog.test: PreCoG Scan Test

Description

precog.test is an implementation of the Prefiltered Component-based Greedy Scan Method.

Usage

precog.test(
  coords,
  cases,
  pop,
  w,
  ex = sum(cases)/sum(pop) * pop,
  nsim = 499,
  tol_prob = 0.9,
  alpha = 0.1,
  ubpop = 0.5,
  longlat = FALSE,
  cl = NULL,
  ysim = NULL
)

Value

Returns a smerc_cluster object.

Arguments

coords

An \(n \times 2\) matrix of centroid coordinates for the regions in the form (x, y) or (longitude, latitude) is using great circle distance.

cases

The number of cases observed in each region.

pop

The population size associated with each region.

w

A binary spatial adjacency matrix for the regions.

ex

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

nsim

The number of simulations from which to compute the p-value.

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.

alpha

The significance level to determine whether a cluster is signficant. Default is 0.10.

ubpop

The upperbound of the proportion of the total population to consider for a cluster.

longlat

The default is FALSE, which specifies that Euclidean distance should be used. If longlat is TRUE, then the great circle distance is used to calculate the intercentroid distance.

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.

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.

Author

Joshua French and Mohammad Meysami

See Also

print.smerc_cluster, summary.smerc_cluster, plot.smerc_cluster,

Examples

data(nydf)
data(nyw)
out <- precog.test(coords = nydf[,c("x", "y")],
                   cases = floor(nydf$cases),
                   pop = nydf$pop, w = nyw, nsim = 19,
                   alpha = 0.2)
# better plotting
if (require("sf", quietly = TRUE)) {
   data(nysf)
   plot(st_geometry(nysf), col = color.clusters(out))
}