# rppm

0th

Percentile

##### Recursively Partitioned Point Process Model

Fits a recursive partition model to point pattern data.

Keywords
models, spatial
##### Usage
rppm(…, rpargs=list())
##### Arguments

Arguments passed to ppm specifying the point pattern data and the explanatory covariates.

rpargs

Optional list of arguments passed to rpart controlling the recursive partitioning procedure.

##### 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.

##### References

Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984) Classification and Regression Trees. Wadsworth.

plot.rppm, predict.rppm, prune.rppm.

• rppm
##### Examples
# NOT RUN {
# 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
# }

Documentation reproduced from package spatstat, version 1.59-0, License: GPL (>= 2)

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