msdata: simulated artifial data at multiple scales and angles

In TreeHotspots: Hotspot Detection using Classification Trees

Description

data generation see details

Usage

data(msdata)

Format

A data frame with 600 observations on the following 3 variables.

lon

spatial lon coordinate

lat

spatial lat coordinate

clus

binary flag indicating cluster membership

Details

set.seed(123) library(mvtnorm) cov1 = sigma = matrix(2*c(1,-0.9,-0.9,1),ncol=2) Clus1 = rmvnorm(100,mean=c(X=10,Y=10), sigma = cov1) #TestRotation(Clus1, center=colMeans(Clus1)) cov2 = sigma = matrix(0.5*c(1,0.9,0.9,1),ncol=2) Clus2 = rmvnorm(100,mean=c(X=3,Y=3), sigma = cov2) cov3 = sigma = matrix(c(0.1,0,0,0.1),ncol=2) Clus3 = rmvnorm(100,mean=c(X=6,Y=8), sigma = cov3) msdata = rbind.data.frame(Clus1,Clus2,Clus3) #TestRotation(msdata, center=colMeans(msdata[,1:2])) colnames(msdata) = c("lon","lat") rx = range(msdata[,"lon"]) ry = range(msdata[,"lat"]) msdata[,"clus"] = rep(1:3,each=100) N=300 Bckgr = cbind.data.frame(lon= runif(N,rx[1],rx[2]), lat= runif(N,ry[1],ry[2]), clus=0) #alternatively, regular grid: # N1=N2=N0 = round(sqrt(N)) # Bckgr =expand.grid(lon= seq(rx[1],rx[2],length=N1), # lat= seq(ry[1],ry[2],length=N2), # clus=0)

msdata=rbind.data.frame(msdata,Bckgr) msdata$clus = factor(msdata$clus)

Examples

data(msdata)
library(rpart)
library(partykit)
 

  rp = rpart(clus ~ lon+lat,data = msdata, control=rpart.control(minbucket=40,cp=0.01))
 #plot(rp)
 party_rp <- as.party(rp)
 plot(lat ~ lon,data = msdata, col = clus, pch=20,cex=0.6,main="rpart")
PartitionParty(party_rp,vars=c("lon","lat"), verbose=0)

TreeHotspots documentation built on May 2, 2019, 5:17 p.m.