dg.envelope: Global Envelopes for Dao-Genton Test

Description

Computes the global envelopes corresponding to the Dao-Genton test of goodness-of-fit.

Usage

dg.envelope(X, ...,
            nsim = 19, nsimsub=nsim-1, nrank = 1,
            alternative=c("two.sided", "less", "greater"),
            interpolate = FALSE,
            savefuns=FALSE, savepatterns=FALSE,
            verbose = TRUE)

Arguments

Either a point pattern dataset (object of class "ppp", "lpp" or "pp3") or a fitted point process model (object of class "ppm", "kppm" or "slrm").

...

Arguments passed to mad.test or envelope to control the conduct of the test. Useful arguments include fun to determine the summar

nsim

Number of simulated patterns to be generated in the primary experiment.

nsimsub

Number of simulations in each basic test. There will be nsim repetitions of the basic test, each involving nsimsub simulated realisations, so there will be a total of nsim * (nsimsub + 1) simulations.

nrank

Integer. Rank of the envelope value amongst the nsim simulated values. A rank of 1 means that the minimum and maximum simulated values will be used.

alternative

Character string determining whether the envelope corresponds to a two-sided test (alternative="two.sided", the default) or a one-sided test with a lower critical boundary (alternative="less") or a one-sided test

interpolate

Logical value indicating whether to interpolate the distribution of the test statistic by kernel smoothing, as described in Dao and Genton (2014, Section 5).

savefuns

Logical flag indicating whether to save the simulated function values (from the first stage).

savepatterns

Logical flag indicating whether to save the simulated point patterns (from the first stage).

verbose

Logical value determining whether to print progress reports.

Value

An object of class "fv".

Details

Computes global simulation envelopes corresponding to the Dao-Genton (2014) adjusted Monte Carlo goodness-of-fit test. The envelopes are described in Baddeley et al (2015). If X is a point pattern, the null hypothesis is CSR.

If X is a fitted model, the null hypothesis is that model.

References

Dao, N.A. and Genton, M. (2014) A Monte Carlo adjusted goodness-of-fit test for parametric models describing spatial point patterns. Journal of Graphical and Computational Statistics 23, 497--517.

Baddeley, A., Hardegen, A., Lawrence, L., Milne, R.K., Nair, G.M. and Rakshit, S. (2015) Pushing the envelope: extensions of graphical Monte Carlo tests. Submitted for publication.

Examples

Run this code

ns <- if(interactive()) 19 else 4
  E <- dg.envelope(swedishpines, Lest, nsim=ns)
  E
  plot(E)
  Eo <- dg.envelope(swedishpines, Lest, alternative="less", nsim=ns)
  Ei <- dg.envelope(swedishpines, Lest, interpolate=TRUE, nsim=ns)

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