predict.locppm: Prediction of a Locally Fitted Poisson or Gibbs Point Process...

In baddstats/spatstat.local: Extension to 'spatstat' for Local Composite Likelihood

Prediction of a Locally Fitted Poisson or Gibbs Point Process Model

Description

Computes the fitted intensity of a locally-fitted Poisson point process model, or the fitted intensity, trend or conditional intensity of a locally-fitted Gibbs point process model.

Usage

## S3 method for class 'locppm'
fitted(object, ...,
                        type = c("cif", "trend", "intensity"),
                        new.coef=NULL)

## S3 method for class 'locppm'
predict(object, ...,
                         type = c("cif", "trend", "intensity"),
                         locations=NULL, new.coef=NULL)

Arguments

object	A locally-fitted Poisson or Gibbs point process model (object of class "locppm").
...	Currently ignored.
new.coef	New vector or matrix of values for the model coefficients.
locations	Point pattern of locations where prediction should be computed.
type	Character string (partially matched) specifying the type of predicted value: the conditional intensity "cif" (the default), or the first order trend, or the intensity. For Poisson models all three options are equivalent.

Details

These are methods for the generic functions fitted and predict for the class "locppm" of locally-fitted Gibbs point process models.

The fitted method computes, for each quadrature point v (or in general, at each point v where a local model was fitted), the intensity of the locally-fitted model at v. The result is a numeric vector.

The predict computes the fitted intensity at any specified set of locations, and returns the result as an ssf object.

Value

For fitted.locppm, a numeric vector.

For predict.locppm, an object of class "ssf" as described in ssf.

Author(s)

\adrian

.

References

\localpaper \baddrubaturnbook

See Also

locppm

Examples

  fit <- locppm(cells, sigma=0.1, use.fft=TRUE)
  lam <- predict(fit)

baddstats/spatstat.local documentation built on July 18, 2024, 4:36 p.m.