library(sgeostat)
library(akima)
library(spatial)
data(package=spatial)

ppregion(xl=0,xu=1,yl=0,yu=1)
sim1<-Psim(100)
plot(sim1)
title("Poisson Process with n=100 points")
par(mfrow=c(2,2))
sim2<-SSI(25,.08)
plot(sim2)
title("SSI,n=25,delta=.08,packing intensity=.503",cex=.5)
sim3<-SSI(50,.08)
plot(sim3)
title("SSI,n=50,delta=.08,packing intensity=1.006",cex=.5)
sim4<-SSI(75,.08)
plot(sim4)
title("SSI,n=75,delta=.08,packing intensity=1.509",cex=.5)
sim5<-SSI(100,.08)
plot(sim5)
title("SSI,n=100,delta=.08,packing intensity=2.012",cex=.5)

sim6<-Strauss(50,0,.1)
plot(sim6)
title("Strauss,n=50,delta=.1,prob=0",cex=.5)
sim7<-Strauss(50,0.3,.1)
plot(sim7)
title("Strauss,n=50,delta=.1,prob=0.3",cex=.5)
sim8<-Strauss(50,0.7,.1)
plot(sim8)
title("Strauss,n=50,delta=.1,prob=0.7",cex=.5)
sim9<-Strauss(50,1.0,.1)
plot(sim9)
title("Strauss,n=50,delta=.1,prob=1",cex=.5)

source("/home/longa/R/packages/spatial2/spatial2.R")

# sim10<-agg(100,10,.1)
# plot(sim10)
# title("Clustered,n=100,clusters=10,radius=.1",cex=.5)
# sim11<-agg(100,10,.2)
# plot(sim11)
# title("Clustered,n=100,clusters=10,radius=.2",cex=.5)
# sim12<-agg(100,5,.1)
# plot(sim12)
# title("Clustered,n=100,clusters=5,radius=.1",cex=.5)
# sim13<-agg(100,15,.08)
# plot(sim13)
# title("Clustered,n=100,clusters=15,radius=.08",cex=.5)
# 
par(mfrow=c(1,1))
# in the S version you don't need to explicitly set these:
sim1$xu<-1.0
sim1$xl<-0
sim1$yu<-1.0
sim1$yl<-0
temp<-quad(sim1,25,.01,overlap=T,type="square")
temp

par(mfrow=c(2,2))
temp<-quad(sim1,25,.01,overlap=T,type="circle")
temp
fitfreq(temp,"poisson")
fitfreq(temp,"thomas")
# fitfreq(temp1,"neyman")

par(mfrow=c(2,2))
temp1<-quad(sim1,25,.01,overlap=T,type="square")
temp1
fitfreq(temp1,"poisson")
fitfreq(temp1,"thomas")
# fitfreq(temp1,"neyman")

towns <- ppinit("towns.dat")
par(pty="s")
plot(Kfn(towns, 10), type="b", xlab="distance", ylab="L(t)")
lines(Kaver(10, 25, Strauss(69,0.5,3.5)))