echepoi: Echelon spatial scan statistic based on Poisson model

In echelon: The Echelon Analysis and the Detection of Spatial Clusters using Echelon Scan Method

Echelon spatial scan statistic based on Poisson model

Description

The echepoi function detects spatial clusters using the echelon spatial scan statistic with a Poisson model.

Usage

echepoi(echelon.obj, cas, pop = NULL, ex = NULL, K = length(cas)/2, Kmin = 1, n.sim = 99,
        cluster.type = "high", cluster.legend.pos = "bottomleft",
        dendrogram = TRUE, cluster.info = FALSE, coo = NULL, ...)

Arguments

echelon.obj	An object of class echelon. For details, see echelon.
cas	A numeric (integer) vector of case counts. NA values are not allowed.
pop	A numeric (integer) vector for population. NA values are not allowed.
ex	A numeric vector for expected case counts. NA values are not allowed.
K	Maximum cluster size. If K >= 1 (integer), the cluster size is limited to K regions. If 0 < K < 1, the cluster size is limited to K * 100% of the total population.
Kmin	Minimum cluster size.
n.sim	The number of Monte Carlo replications used for significance testing of detected clusters. If set to 0, significance is not assessed.
cluster.type	A character string specifying the cluster type. If "high", the detected clusters have high rates (hotspot). If "low", the detected clusters have low rates (coldspot).
cluster.legend.pos	The location of the legend on the dendrogram. (See legend for details.)
dendrogram	Logical. If TRUE, draws an echelon dendrogram with the detected clusters.
cluster.info	Logical. If TRUE, returns detailed results of the detected clusters.
coo	An array of (x, y) coordinates for the region centroids to plot a cluster map.
...	Related to dendrogram drawing. (See the help for echelon)

Value

clusters	Each detected cluster.
scanned.regions	A region list of all scanning processes.
simulated.LLR	Monte Carlo samples of the log-likelihood ratio.

Note

The function echepoi requires either pop or ex.

Typical values of n.sim are 99, 999, 9999, ...

Author(s)

Fumio Ishioka

References

[1] Kulldorff M. (1997). A spatial scan statistic. Communications in Statistics: Theory and Methods, 26, 1481–1496.

[2] Ishioka F, Kawahara J, Mizuta M, Minato S, and Kurihara K. (2019) Evaluation of hotspot cluster detection using spatial scan statistic based on exact counting. Japanese Journal of Statistics and Data Science, 2, 241–262.

See Also

echelon for the echelon analysis.

echebin for cluster detection based on echelons using Binomial model.

Examples

##Hotspot detection for SIDS data of North Carolina using echelon scan

#Mortality rate per 1,000 live births from 1974 to 1984
library(spData)
data("nc.sids")
SIDS.cas <- nc.sids$SID74 + nc.sids$SID79
SIDS.pop <- nc.sids$BIR74 + nc.sids$BIR79
SIDS.rate <- SIDS.cas * 1000 / SIDS.pop

#Hotspot detection based on Poisson model
SIDS.echelon <- echelon(x = SIDS.rate, nb = ncCR85.nb, name = row.names(nc.sids))
echepoi(SIDS.echelon, cas = SIDS.cas, pop = SIDS.pop, K = 20,
  main = "Hgih rate clusters", ens = FALSE)
text(SIDS.echelon$coord, labels = SIDS.echelon$regions.name,
  adj = -0.1, cex = 0.7)

#Detected clusters and neighbors map
#XY coordinates of each polygon centroid point
NC.coo <- cbind(nc.sids$lon, nc.sids$lat)
echepoi(SIDS.echelon, cas = SIDS.cas, pop = SIDS.pop, K = 20,
  coo = NC.coo, dendrogram = FALSE)


##Detected clusters map
#Here is an example using the sf class "sf"
SIDS.clusters <- echepoi(SIDS.echelon, cas = SIDS.cas,
  pop = SIDS.pop, K = 20, dendrogram = FALSE)
MLC <- SIDS.clusters$clusters[[1]]
Secondary <- SIDS.clusters$clusters[[2]]
cluster.col <- rep(0,times=length(SIDS.rate))
cluster.col[MLC$regionsID] <- 2
cluster.col[Secondary$regionsID] <- 3

library(sf)
nc <- st_read(system.file("shape/nc.shp", package = "sf"))
plot(nc$geometry, col = cluster.col,
main = "Detected high rate clusters")
text(st_coordinates(st_centroid(st_geometry(nc))),
  labels = nc$CRESS_ID, cex =0.75)
legend("bottomleft",
  c(paste("1- p-value:", MLC$p),
  paste("2- p-value:", Secondary$p)),
  text.col = c(2,3))

echelon documentation built on April 3, 2025, 11:45 p.m.