spatstat (version 1.20-2)

effectfun: Compute Fitted Effect of a Spatial Covariate in a Point Process Model

Description

Compute the intensity of a fitted point process model as a function of one of its covariates.

Usage

effectfun(model, covname, ...)

Arguments

model

A fitted point process model (object of class "ppm").

covname

The name of the covariate. A character string.

...

The fixed values of other covariates (in the form name=value) if required.

Value

• A data frame containing a column of values of the covariate and a column of values of the fitted intensity.

  If the covariate named covname is numeric (rather than a factor or logical variable), the return value is also of class "fv" so that it can be plotted immediately.

Details

The object model should be an object of class "ppm" representing a point process model fitted to point pattern data.

The model's trend formula should involve a spatial covariate named covname. This could be "x" or "y" representing one of the Cartesian coordinates. More commonly the covariate is another, external variable that was supplied when fitting the model. The command effectfun computes the fitted intensity of the point process model as a function of the covariate named covname. The return value can be plotted immediately, giving a plot of the fitted intensity against the value of the covariate.

If the model also involves covariates other than covname, then these covariates will be held fixed. Values for these other covariates must be provided as arguments to effectfun in the form name=value.

This command is just a wrapper for the prediction method predict.ppm. For more complicated computations about the fitted intensity, use predict.ppm.

See Also

ppm, predict.ppm, fv.object

Examples

data(copper)
  X <- copper$SouthPoints
  D <- distmap(copper$SouthLines)
  fit <- ppm(X, ~polynom(Z, 7), covariates=list(Z=D))
  plot(effectfun(fit, "Z"))
  fit <- ppm(X, ~x + polynom(Z, 7), covariates=list(Z=D))
  plot(effectfun(fit, "Z", x=20))