R: Recursively Partitioned Point Process Model

rppm {spatstat.core} | 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 `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.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz 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`, `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

[Package spatstat.core version 2.3-1 Index]