R: Compute Fitted Effect of a Spatial Covariate in a Point...

effectfun {spatstat} | R Documentation

## Compute Fitted Effect of a Spatial Covariate in a Point Process Model

### Description

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

### Usage

      effectfun(model, covname, ..., se.fit=FALSE)

### Arguments

`model` | A fitted point process model (object of class `"ppm"`, `"kppm"`, `"lppm"`, `"dppm"`, `"rppm"` or `"profilepl"`).
`covname` | The name of the covariate. A character string. (Needed only if the model has more than one covariate.)
`...` | The fixed values of other covariates (in the form `name=value`) if required.
`se.fit` | Logical. If `TRUE`, asymptotic standard errors of the estimates will be computed, together with a 95% confidence interval.

### Details

The object `model` should be an object of class `"ppm"`, `"kppm"`, `"lppm"`, `"dppm"`, `"rppm"` or `"profilepl"` 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 trend 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 trend 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`.

If `se.fit=TRUE`, the algorithm also calculates the asymptotic standard error of the fitted trend, and a (pointwise) asymptotic 95% confidence interval for the true trend.

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

### Value

A data frame containing a column of values of the covariate and a column of values of the fitted trend. If `se.fit=TRUE`, there are 3 additional columns containing the standard error and the upper and lower limits of a confidence interval.

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.

### Trend and intensity

For a Poisson point process model, the trend is the same as the intensity of the point process. For a more general Gibbs model, the trend is the first order potential in the model (the first order term in the Gibbs representation). In Poisson or Gibbs models fitted by `ppm`, the trend is the only part of the model that depends on the covariates.

### Determinantal point process models with fixed intensity

The function `dppm` which fits a determinantal point process model allows the user to specify the intensity `lambda`. In such cases the effect function is undefined, and `effectfun` stops with an error message.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au and Rolf Turner r.turner@auckland.ac.nz.

### See Also

`ppm`, `predict.ppm`, `fv.object`

### Examples

      X <- copper$SouthPoints
      D <- distfun(copper$SouthLines)
      fit <- ppm(X ~ polynom(D, 5))
      effectfun(fit)
      plot(effectfun(fit, se.fit=TRUE))

      fitx <- ppm(X ~ x + polynom(D, 5))
      plot(effectfun(fitx, "D", x=20))

[Package spatstat version 1.54-0 Index]