R: Prediction from a Fitted Determinantal Point Process Model

predict.dppm {spatstat.core} | R Documentation

## Prediction from a Fitted Determinantal Point Process Model

### Description

Given a fitted determinantal point process model, these functions compute the fitted intensity.

### Usage

      ## S3 method for class 'dppm'
    fitted(object, ...)

      ## S3 method for class 'dppm'
    predict(object, ...)

### Arguments

`object` | Fitted determinantal point process model. An object of class `"dppm"`.
`...` | Arguments passed to `fitted.ppm` or `predict.ppm` respectively.

### Details

These functions are methods for the generic functions `fitted` and `predict`. The argument `object` should be a determinantal point process model (object of class `"dppm"`) obtained using the function `dppm`.

The intensity of the fitted model is computed, using `fitted.ppm` or `predict.ppm` respectively.

### Value

The value of `fitted.dppm` is a numeric vector giving the fitted values at the quadrature points.

The value of `predict.dppm` is usually a pixel image (object of class `"im"`), but see `predict.ppm` for details.

### 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

`dppm`, `plot.dppm`, `fitted.ppm`, `predict.ppm`

### Examples

      if(interactive()) {
        fit <- dppm(swedishpines ~ x + y, dppGauss())
      } else {
        fit <- dppm(redwood ~ x, dppGauss())
      }
      predict(fit)

[Package spatstat.core version 2.3-0 Index]