Performs a Balanced Independent Two-Stage Monte Carlo test of goodness-of-fit for spatial pattern.
bits.test(X, ...,
exponent = 2, nsim=19,
alternative=c("two.sided", "less", "greater"),
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.
Performs the Balanced Independent Two-Stage Monte Carlo test proposed by Baddeley et al (2017), an improvement of the Dao-Genton (2014) test.
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).
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.
Simulation envelopes: bits.envelope.
Other tests:
dg.test,
dclf.test,
mad.test.
ns <- if(interactive()) 19 else 4
bits.test(cells, nsim=ns)
bits.test(cells, alternative="less", nsim=ns)
bits.test(cells, nsim=ns, interpolate=TRUE)
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