| rppm {spatstat.model} | R Documentation |
Recursively Partitioned Point Process Model
Description
Fits a recursive partition model to point pattern data.
Usage
rppm(..., rpargs=list())
Arguments
... |
Arguments passed to |
rpargs |
Optional list of arguments passed to |
Details
This function attempts to find a simple rule for predicting low and high intensity regions of points in a point pattern, using explanatory covariates.
The arguments ... specify the point pattern data
and explanatory covariates in the same way as they would be
in the function ppm.
The recursive partitioning algorithm rpart
is then used to find a partitioning rule.
Value
An object of class "rppm". There are methods
for print, plot, fitted, predict and
prune for this class.
Author(s)
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner rolfturner@posteo.net and Ege Rubak rubak@math.aau.dk.
References
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984) Classification and Regression Trees. Wadsworth.
See Also
plot.rppm,
predict.rppm,
update.rppm,
prune.rppm.
Examples
# New Zealand trees data: trees planted along border
# Use covariates 'x', 'y'
nzfit <- rppm(nztrees ~ x + y)
nzfit
prune(nzfit, cp=0.035)
# Murchison gold data: numeric and logical covariates
mur <- solapply(murchison, rescale, s=1000, unitname="km")
mur$dfault <- distfun(mur$faults)
#
mfit <- rppm(gold ~ dfault + greenstone, data=mur)
mfit
# Gorillas data: factor covariates
# (symbol '.' indicates 'all variables')
gfit <- rppm(unmark(gorillas) ~ . , data=gorillas.extra)
gfit