rppm: Recursively Partitioned Point Process Model

In spatstat.model: Parametric Statistical Modelling and Inference for the 'spatstat' Family

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)

\spatstatAuthors

.

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

spatstat.model documentation built on Sept. 30, 2024, 9:26 a.m.