spatstat (version 1.63-0)

# rppm: Recursively Partitioned Point Process Model

## Description

Fits a recursive partition model to point pattern data.

## 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.

## Value

An object of class `"rppm"`. There are methods for `print`, `plot`, `fitted`, `predict` and `prune` for this class.

## 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.

## 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`, `prune.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
# }
```