bits.envelope  R Documentation 
Computes the global envelopes corresponding to the balanced independent twostage Monte Carlo test of goodnessoffit.
bits.envelope(X, ..., nsim = 19, nrank = 1, alternative=c("two.sided", "less", "greater"), leaveout=1, interpolate = FALSE, savefuns=FALSE, savepatterns=FALSE, verbose = TRUE)
X 
Either a point pattern dataset (object of class 
... 
Arguments passed to

nsim 
Number of simulated patterns to be generated in each stage.
Number of simulations in each basic test. There will be 
nrank 
Integer. Rank of the envelope value amongst the 
alternative 
Character string determining whether the envelope corresponds
to a twosided test ( 
leaveout 
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. 
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. 
Computes global simulation envelopes corresponding to the balanced independent twostage Monte Carlo test of goodnessoffit described by Baddeley et al (2017). The envelopes are described in Baddeley et al (2019).
If X
is a point pattern, the null hypothesis is CSR.
If X
is a fitted model, the null hypothesis is that model.
This command is similar to dg.envelope
which corresponds
to the DaoGenton test of goodnessoffit.
It was shown in Baddeley et al (2017) that
the DaoGenton test is biased when the significance level is very small
(small pvalues are not reliable) and
we recommend bits.envelope
in this case.
An object of class "fv"
.
Adrian Baddeley, Andrew Hardegen, Tom Lawrence, Robin Milne, Gopalan Nair and Suman Rakshit. Implemented by \adrian.
Dao, N.A. and Genton, M. (2014) A Monte Carlo adjusted goodnessoffit test for parametric models describing spatial point patterns. Journal of Graphical and Computational Statistics 23, 497–517.
Baddeley, A., Hardegen, A., Lawrence, T., Milne, R.K., Nair, G. and Rakshit, S. (2017) On twostage Monte Carlo tests of composite hypotheses. Computational Statistics and Data Analysis 114, 75–87.
Baddeley, A., Hardegen, A., Lawrence, L., Milne, R.K., Nair, G.M. and Rakshit, S. (2019) Pushing the envelope: extensions of graphical Monte Carlo tests. In preparation.
dg.envelope
,
bits.test
,
mad.test
,
envelope
ns < if(interactive()) 19 else 4 E < bits.envelope(swedishpines, Lest, nsim=ns) E plot(E) Eo < bits.envelope(swedishpines, Lest, alternative="less", nsim=ns) Ei < bits.envelope(swedishpines, Lest, interpolate=TRUE, nsim=ns)
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