echelon (version 0.3.1)

echebin: Echelon spatial scan statistic based on Binomial model

Description

The echebin function detects spatial clusters using the echelon spatial scan statistic with a Binomial model.

Usage

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

Value

clusters

Each detected cluster.

scanned.regions

A region list of all scanning processes.

simulated.LLR

Monte Carlo samples of the log-likelihood ratio.

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.

ctl

A numeric (integer) vector of control 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)

Author

Fumio Ishioka

References

[1] Kulldorff M, Nagarwalla N. (1995). Spatial disease clusters: Detection and inference. Statistics in Medicine, 14, 799--810.

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

See Also

echelon for the echelon analysis.

echepoi for cluster detection based on echelons using Poisson model.

Examples

##Hotspot detection for non-white birth of North Carolina using echelon scan

#Non-white birth from 1974 to 1984 (case data)
library(spData)
data("nc.sids")
nwb <- nc.sids$NWBIR74 + nc.sids$NWBIR79

#White birth from 1974 to 1984 (control data)
wb <- (nc.sids$BIR74 - nc.sids$NWBIR74) + (nc.sids$BIR79 - nc.sids$NWBIR79)

#Hotspot detection based on Binomial model
nwb.echelon <- echelon(x = nwb/wb, nb = ncCR85.nb, name = row.names(nc.sids))
echebin(nwb.echelon, cas = nwb, ctl = wb, K = 20,
  main = "Hgih rate clusters", ens = FALSE)
text(nwb.echelon$coord, labels = nwb.echelon$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)
echebin(nwb.echelon, cas = nwb, ctl = wb, K = 20,
  coo = NC.coo, dendrogram = FALSE)

# \donttest{
##Detected clusters map
#Here is an example using the sf class "sf"
nwb.clusters <- echebin(nwb.echelon, cas = nwb,
   ctl = wb, K = 20, dendrogram = FALSE)
MLC <- nwb.clusters$clusters[[1]]
Secondary <- nwb.clusters$clusters[[2]]
cluster.col <- rep(0,times=length(nwb))
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))
# }