residuals.ppm

Residuals for Fitted Point Process Model

Given a point process model fitted to a point pattern, compute residuals.

Usage

residuals.ppm(object, type="raw", ...)

Details

This function computes several kinds of residuals for the fit of a point process model to a spatial point pattern dataset (Baddeley et al, 2005). Use diagnose.ppm to produce diagnostic plots based on these residuals.

The argument object must be a fitted point process model (object of class "ppm"). Such objects are produced by the maximum pseudolikelihood fitting algorithm ppm). This fitted model object contains complete information about the original data pattern.

Residuals are attached both to the data points and to some other points in the window of observation (namely, to the dummy points of the quadrature scheme used to fit the model). If the fitted model is correct, then the sum of the residuals over all (data and dummy) points in a spatial region $B$ has mean zero. For further explanation, see Baddeley et al (2005). The type of residual is chosen by the argument type. Current options are

[object Object],[object Object],[object Object]

Use diagnose.ppm to produce diagnostic plots based on these residuals.

synopsis

residuals.ppm(object, type="raw", ..., fittedvalues=fitted.ppm(object))

References

Baddeley, A., Turner, R., Moller, J. and Hazelton, M. (2005) Residual analysis for spatial point processes. Journal of the Royal Statistical Society, series B. to appear.

See Also

diagnose.ppm, ppm.object, ppm

Aliases

• residuals.ppm

Examples

data(cells)
    fit <- ppm(cells, ~x, Strauss(r=0.15), rbord=0.15)

    rp <- residuals.ppm(fit, type="pe")
    sum(rp) # should be close to 0

    # extract quadrature points to which the residuals correspond
    quadpoints <- union.quad(quad.ppm(fit))

    # plot residuals as marks attached to the quadrature points 
    quadmarked <- setmarks(quadpoints, rp)
    plot(quadmarked)