R: Prune a Recursively Partitioned Point Process Model

prune.rppm {spatstat.core} | R Documentation

## Prune a Recursively Partitioned Point Process Model

### Description

Given a model which has been fitted to point pattern data by recursive partitioning, apply pruning to reduce the complexity of the partition tree.

### Usage

    ## S3 method for class 'rppm'
    prune(tree, ...)

### Arguments

`tree` | Fitted point process model of class `"rppm"` produced by the function `rppm`.
`...` | Arguments passed to `prune.rpart` to control the pruning procedure.

### Details

This is a method for the generic function `prune` for the class `"rppm"`. An object of this class is a point process model, fitted to point pattern data by recursive partitioning, by the function `rppm`.

The recursive partition tree will be pruned using `prune.rpart`. The result is another object of class `"rppm"`.

### Value

Object of class `"rppm"`.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk

### See Also

`rppm`, `plot.rppm`, `predict.rppm`.

### Examples

      # Murchison gold data
      mur <- solapply(murchison, rescale, s=1000, unitname="km")
      mur$dfault <- distfun(mur$faults)
      fit <- rppm(gold ~ dfault + greenstone, data=mur)
      fit
      prune(fit, cp=0.1)

[Package spatstat.core version 1.65-5 Index]