Model Compensator of Nearest Neighbour Function

Given a point process model fitted to a point pattern dataset, this function computes the compensator of the nearest neighbour distance distribution function $G$ based on the fitted model (as well as the usual nonparametric estimates of $G$ based on the data alone). Comparison between the nonparametric and model-compensated $G$ functions serves as a diagnostic for the model.

models, spatial
Gcom(object, r = NULL, breaks = NULL, ...,
     correction = c("border", "Hanisch"),
     conditional = !is.poisson(object),
     trend = ~1, interaction = Poisson(),
     rbord = reach(interaction),
     truecoef = NULL, hi.res = NULL)
Object to be analysed. Either a fitted point process model (object of class "ppm") or a point pattern (object of class "ppp") or quadrature scheme (object of class "quad").
Optional. Vector of values of the argument $r$ at which the function $G(r)$ should be computed. This argument is usually not specified. There is a sensible default.
Optional alternative to r for advanced use.
Edge correction(s) to be employed in calculating the compensator. Options are "border", "Hanisch" and "best".
Optional. Logical value indicating whether to compute the estimates for the conditional case. See Details.
Logical value indicating whether to compute the restriction estimator (restrict=TRUE) or the reweighting estimator (restrict=FALSE, the default). Applies only if conditional=TRUE. See Details.
Optional. Arguments passed to ppm to fit a point process model to the data, if object is a point pattern. See ppm for details.
Extra arguments passed to ppm.
The correction argument to ppm.
Optional. Numeric vector. If present, this will be treated as if it were the true coefficient vector of the point process model, in calculating the diagnostic. Incompatible with hi.res.
Optional. List of parameters passed to quadscheme. If this argument is present, the model will be re-fitted at high resolution as specified by these parameters. The coefficients of the re

This command provides a diagnostic for the goodness-of-fit of a point process model fitted to a point pattern dataset. It computes different estimates of the nearest neighbour distance distribution function $G$ of the dataset, which should be approximately equal if the model is a good fit to the data.

The first argument, object, is usually a fitted point process model (object of class "ppm"), obtained from the model-fitting function ppm.

For convenience, object can also be a point pattern (object of class "ppp"). In that case, a point process model will be fitted to it, by calling ppm using the arguments trend (for the first order trend), interaction (for the interpoint interaction) and rbord (for the erosion distance in the border correction for the pseudolikelihood). See ppm for details of these arguments. The algorithm first extracts the original point pattern dataset (to which the model was fitted) and computes the standard nonparametric estimates of the $G$ function. It then also computes the model-compensated $G$ function. The different functions are returned as columns in a data frame (of class "fv"). The interpretation of the columns is as follows (ignoring edge corrections): [object Object],[object Object],[object Object],[object Object] If the fitted model is a Poisson point process, then the formulae above are exactly what is computed. If the fitted model is not Poisson, the formulae above are modified slightly to handle edge effects.

The modification is determined by the arguments conditional and restrict. The value of conditional defaults to FALSE for Poisson models and TRUE for non-Poisson models. If conditional=FALSE then the formulae above are not modified. If conditional=TRUE, then the algorithm calculates the restriction estimator if restrict=TRUE, and calculates the reweighting estimator if restrict=FALSE. See Appendix E of Baddeley, Rubak and Moller (2011). Thus, by default, the reweighting estimator is computed for non-Poisson models.

The border-corrected and Hanisch-corrected estimates of $G(r)$ are approximately unbiased estimates of the $G$-function, assuming the point process is stationary. The model-compensated functions are unbiased estimates of the mean value of the corresponding nonparametric estimate, assuming the model is true. Thus, if the model is a good fit, the mean value of the difference between the nonparametric and model-compensated estimates is approximately zero.

To compute the difference between the nonparametric and model-compensated functions, use Gres.


  • A function value table (object of class "fv"), essentially a data frame of function values. There is a plot method for this class. See fv.object.


Baddeley, A., Rubak, E. and Moller, J. (2011) Score, pseudo-score and residual diagnostics for spatial point process models. To appear in Statistical Science.

See Also

Related functions: Gest, Gres.

Alternative functions: Kcom, psstA, psstG, psst. Model fitting: ppm.

  • Gcom
    fit0 <- ppm(cells, ~1) # uniform Poisson
    G0 <- Gcom(fit0)
# uniform Poisson is clearly not correct

# Hanisch estimates only
    plot(Gcom(fit0), cbind(han, hcom) ~ r)

    fit1 <- ppm(cells, ~1, Strauss(0.08))
    plot(Gcom(fit1), cbind(han, hcom) ~ r)

# Try adjusting interaction distance

    fit2 <- update(fit1, Strauss(0.10))
    plot(Gcom(fit2), cbind(han, hcom) ~ r)

    G3 <- Gcom(cells, interaction=Strauss(0.12))
    plot(G3, cbind(han, hcom) ~ r)
Documentation reproduced from package spatstat, version 1.25-1, License: GPL (>= 2)

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