```
# NOT RUN {
# (1) cutting based on numeric marks of point pattern
trees <- longleaf
# Longleaf Pines data
# the marks are positive real numbers indicating tree diameters.
# }
# NOT RUN {
# }
# NOT RUN {
plot(trees)
# }
# NOT RUN {
# cut the range of tree diameters into three intervals
long3 <- cut(trees, breaks=3)
# }
# NOT RUN {
plot(long3)
# }
# NOT RUN {
# adult trees defined to have diameter at least 30 cm
long2 <- cut(trees, breaks=c(0,30,100), labels=c("Sapling", "Adult"))
plot(long2)
plot(long2, cols=c("green","blue"))
# (2) cutting based on another numeric vector
# Divide Swedish Pines data into 3 classes
# according to nearest neighbour distance
swedishpines
plot(cut(swedishpines, nndist(swedishpines), breaks=3))
# (3) cutting based on tessellation
# Divide Swedish Pines study region into a 4 x 4 grid of rectangles
# and classify points accordingly
tes <- tess(xgrid=seq(0,96,length=5),ygrid=seq(0,100,length=5))
plot(cut(swedishpines, tes))
plot(tes, lty=2, add=TRUE)
# (4) inside/outside a given region
with(murchison, cut(gold, greenstone))
# (5) multivariate marks
finpines
cut(finpines, "height", breaks=4)
# }
```

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