R/e.cluster.map.r

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

###################
### Clsuter map ###
###################

e.cluster.map <- function(x,c_separates,locs,coo,rin,p_rank,cluster_reg,n.sim,cluster.type){

	dev.new()
	par(mai=c(0.1,0.1,0.5,0.1))

	if(cluster.type == "high") plot(coo,ylab="",xlab="",axes=F,main="High rate clusters")
	if(cluster.type == "low") plot(coo,ylab="",xlab="",axes=F,main="Low rate clusters")

	cluster.col <- c(2:6)
	for(i in 1:length(coo[,1])){
		for(j in 1:length(rin[i,])){
			if(!is.na(rin[i,j])){
				temp <- as.integer(rin[i,j])
				lines(x=c(coo[i,1],coo[temp,1]),y=c(coo[i,2],coo[temp,2]),lwd=0.5,lty=3)
				for(k in 1:nrow(cluster_reg)){
					if(length(which(!is.na(match(c(i,temp),cluster_reg[k,])))) == 2) lines(x=c(coo[i,1],coo[temp,1]),y=c(coo[i,2],coo[temp,2]),lwd=2,col=cluster.col[k])
				}
			}
			else break
	}}

	if(nrow(cluster_reg) <= 5) l <- nrow(cluster_reg)
	else l <- 5
	leg.txt <- NULL
	leg.col <- NULL
	for(i in 1:l){
		temp <- cluster_reg[i,][!is.na(cluster_reg[i,])]
		points(coo[temp,1],coo[temp,2],pch=21,bg="white",col=cluster.col[i],cex=2)
		text(coo[temp,1],coo[temp,2],labels=i,col=cluster.col[i],cex=1.5)
		if(!is.null(p_rank)) leg.txt <- c(leg.txt,paste(i,"- p-value:",p_rank[i]/(n.sim+1)))
		else leg.txt <- c(leg.txt,paste(i,"- p-value:---"))
		leg.col <- c(leg.col,cluster.col[i])
	}
	legend("bottomleft",legend=leg.txt,text.col=leg.col)

	dev.set(dev.prev())
}