p 19  


p 33

p 66, 72


p 98  


sp = swedishpines
plot(density(sp, sigma = 10))

sp.den = density(sp, sigma = 10)
plot(sp.den)
plot(sp, add =T, pch = "o", cex = .5)


K.sp = Kest(sp)

#  suggests repelled up to distance about 10 units
# section 21

sp.Poisson = ppm(sp)

plot( envelope( sp.Poisson, nsim = 39) )


sp.Str.fit = ppm( sp, ~1 , Strauss(r=10))
plot( envelope( sp.Str.fit, nsim = 39) )

# envelope( sp.Str.fit, fun = G, nsim = 39) : use a different PP summary function

sp.sim = rmh( sp.Str.fit)


# Section 24, p 160: create ppp data set



lf = data(longleaf) 
plot(lf)

# use data with large marks; write to file
ind = lf$marks>=15
xym = cbind( lf$x[ind], lf$y[ind], lf$marks[ind])
write.table(xym, "biglongleaf.dat", col.names= c("x", "y", "dbh"))
 
# now treat bigleaf.dat as a data set (not in ppp format) to read into R

bl.dat = read.table("biglongleaf.dat", header = T)
bl = data.frame( x = bl.dat[,"x"], y = bl.dat[,"y"], m = bl.dat[,"dbh"])
bigleaf = as.ppp( bl, square(200) )

#  bigleaf = as.ppp( bl, owin( c(0,200), c(0,200) ) )

bigleaf2 = scanpp( "biglongleaf.dat", window = square(200) )

summary(lf)

X = cut( longleaf, breaks = c(0, 30, 80), labels = c("juvenile", "adult") )
X

hist(lf$marks)
hist(bigleaf$marks)

lf.m.hat = smooth.ppp( longleaf)
plot(lf.m.hat)

plot( alltypes( X, "K") )


# mark correlation function

f.prod = function( x,y){ x*y }

M.lf = markcorr( longleaf, f.prod) 
plot( M.lf)

M.lf = markcorr( longleaf, f.prod, kernel = "gaussian", width = 5) 
plot( M.lf)

M.lf = markcorr( longleaf, f.prod, kernel = "epanechnikov") 
plot( M.lf)

K.env = alltypes( lf, Kcross, nsim = 39, enveope = T)
plot(K.env)