Performs the Dao and Genton (2014) adjusted goodness-of-fit test of spatial pattern.
dg.test(X, ...,
exponent = 2, nsim=19, nsimsub=nsim-1,
alternative=c("two.sided", "less", "greater"),
reuse = TRUE, leaveout=1, interpolate = FALSE,
savefuns=FALSE, savepatterns=FALSE,
verbose = TRUE)A hypothesis test (object of class "htest"
which can be printed to show the outcome of the test.
Either a point pattern dataset (object of class "ppp",
"lpp" or "pp3") or a fitted point process model
(object of class "ppm", "kppm", "lppm"
or "slrm").
Arguments passed to dclf.test or
mad.test or envelope to
control the conduct of the test.
Useful arguments include fun to determine the summary
function, rinterval to determine the range of
\(r\) values used in the test,
and use.theory described under Details.
Exponent used in the test statistic. Use exponent=2
for the Diggle-Cressie-Loosmore-Ford test, and exponent=Inf
for the Maximum Absolute Deviation test.
Number of repetitions of the basic test.
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.
Character string specifying the alternative hypothesis.
The default (alternative="two.sided") is that the
true value of the summary function is not equal to the theoretical
value postulated under the null hypothesis.
If alternative="less" the alternative hypothesis is that the
true value of the summary function is lower than the theoretical value.
Logical value indicating whether to re-use the first stage simulations at the second stage, as described by Dao and Genton (2014).
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 indicating whether to print progress reports.
Adrian Baddeley, Andrew Hardegen, Tom Lawrence, Robin Milne, Gopalan Nair and Suman Rakshit. Implemented by Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.
Performs the Dao-Genton (2014) adjusted Monte Carlo goodness-of-fit test, in the equivalent form described by Baddeley et al (2014).
If X is a point pattern, the null hypothesis is CSR.
If X is a fitted model, the null hypothesis is that model.
The argument use.theory passed to envelope
determines whether to compare the summary function for the data
to its theoretical value for CSR (use.theory=TRUE)
or to the sample mean of simulations from CSR
(use.theory=FALSE).
The argument leaveout specifies how to calculate the
discrepancy between the summary function for the data and the
nominal reference value, when the reference value must be estimated
by simulation. The values leaveout=0 and
leaveout=1 are both algebraically equivalent (Baddeley et al, 2014,
Appendix) to computing the difference observed - reference
where the reference is the mean of simulated values.
The value leaveout=2 gives the leave-two-out discrepancy
proposed by Dao and Genton (2014).
The Dao-Genton test is biased when the significance level is very small
(small \(p\)-values are not reliable) and
we recommend bits.test in this case.
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., Diggle, P.J., Hardegen, A., Lawrence, T., Milne, R.K. and Nair, G. (2014) On tests of spatial pattern based on simulation envelopes. Ecological Monographs 84 (3) 477--489.
Baddeley, A., Hardegen, A., Lawrence, L., Milne, R.K., Nair, G.M. and Rakshit, S. (2017) On two-stage Monte Carlo tests of composite hypotheses. Computational Statistics and Data Analysis 114, 75--87.
bits.test,
dclf.test,
mad.test
ns <- if(interactive()) 19 else 4
dg.test(cells, nsim=ns)
dg.test(cells, alternative="less", nsim=ns)
dg.test(cells, nsim=ns, interpolate=TRUE)
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