library(spatstat)

# A process with real-valued marks

data(longleaf)
plot(longleaf)

# Kernel smoothed mark intensity

plot(smooth.ppp(longleaf))

# Histogram of marks

plot(hist(marks(longleaf)))

# Mark correlation function

plot(markcorr(longleaf))

# Envelope of the mark correlation function -- not sure exactly how
# the marks are being resampled... from the docs it seems as if
# they are sampled with replacement

E = envelope(longleaf,markcorr)

# A point process with categorical marks

data(lansing)
plot(lansing)

# cross G function for blackoak and hickory trees and envelope

plot(Gcross(lansing, 'blackoak', 'hickory'))
E = envelope(lansing, Gcross, i='blackoak', j='hickory',
  simulate=expression(rshift(lansing, radius=0.25)))
plot(E)

# Similar, but not exactly the same

plot(Gcross(lansing, 'blackoak', 'hickory'))
E = envelope(lansing, Gcross, i='blackoak', j='hickory')
plot(E)

# Mark correlation for the blackoak and hickory trees

f12 = function(m1, m2) {
return (m1 == 'blackoak') * (m2 == 'hickory')
}

plot(markcorr(lansing, f12))

# Mark correlation for any matches

fany = function(m1, m2) {
return (m1 == m2)
}

plot(markcorr(lansing, fany))

# Random labelling, function taken from p.177 of spatstat doc

Kdif = function(X, ..., i) {
  Kidot = Kdot(X, ..., i = i)
  K = Kest(X, ...)
  dif = eval.fv(Kidot - K)
  return(dif)
}

E = envelope(lansing, Kdif, i='blackoak', simulate=expression(rlabel(lansing)))
plot(E)