spatstat.explore (version 3.5-2)

dg.test: Dao-Genton Adjusted Goodness-Of-Fit Test

Description

Performs the Dao and Genton (2014) adjusted goodness-of-fit test of spatial pattern.

Usage

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)

Value

A hypothesis test (object of class "htest"

which can be printed to show the outcome of the test.

Details

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.

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., 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.

See Also

bits.test, dclf.test, mad.test

Examples

 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)