Find.Clusters.SI.ST: Find.Clusters.SI.ST

In mkamenet3/coefclust: Clustered spatio-temporal varying coefficient regression modeling

Description

Find multiple cylindrical spatio-temporal clusters sequentially via simultaneous detection. Find and test the cluster in the simple linear regression for given potential centroids.

Usage

Find.Clusters.SI.ST(yList, XList, long, lat, MR, M, overlap, alpha)

Arguments

yList	The input data (as a list of vectors).
XList	The input data (as a list of matrices).
long	longitude.
lat	latitude.
MR	Maximum radius.
M	Number of simulations.
overlap	Boolean which is TRUE for overlapping clusters / FALSE for non-overlapping clusters.
alpha	Significance level

Value

List of clusters, coefficients, and indicator of cluster membership.

Examples

data("SE_FakeData_SpTm")
long <- SE_FakeData_SpTm$long; lat <- SE_FakeData_SpTm$lat
yList <-list()
yList[[1]] <- SE_FakeData_SpTm$y1
yList[[2]] <- SE_FakeData_SpTm$y2
yList[[3]] <- SE_FakeData_SpTm$y3
XList <-list()
XList[[1]] <- cbind(rep(1,length(long)), SE_FakeData_SpTm$x1)
XList[[2]] <- cbind(rep(1,length(long)), SE_FakeData_SpTm$x2)
XList[[3]] <- cbind(rep(1,length(long)), SE_FakeData_SpTm$x3)
MR <- 300; M <- 2
Clusters_simST <- Find.Clusters.SI.ST(yList, XList, long, lat,
MR, M, overlap=FALSE, alpha=0.05)

mkamenet3/coefclust documentation built on June 28, 2020, 10:41 a.m.