Computes the global envelopes corresponding to the Dao-Genton test of goodness-of-fit.
dg.envelope(X, …,
nsim = 19, nsimsub=nsim-1, nrank = 1,
alternative=c("two.sided", "less", "greater"),
leaveout=1, interpolate = FALSE,
savefuns=FALSE, savepatterns=FALSE,
verbose = TRUE)
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"
).
Number of simulated patterns to be generated in the primary experiment.
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.
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.
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
with an upper critical boundary (alternative="greater"
).
Optional integer 0, 1 or 2 indicating how to calculate the deviation between the observed summary function and the nominal reference value, when the reference value must be estimated by simulation. See Details.
Logical value indicating whether to interpolate the distribution of the test statistic by kernel smoothing, as described in Dao and Genton (2014, Section 5).
Logical flag indicating whether to save the simulated function values (from the first stage).
Logical flag indicating whether to save the simulated point patterns (from the first stage).
Logical value determining whether to print progress reports.
An object of class "fv"
.
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.
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.
# NOT RUN {
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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