clf.test

0th

Percentile

Cressie-Loosmore-Ford and Maximum Absolute Deviation Tests

Perform the Cressie (1991)/Loosmore and Ford (2006) test or the Maximum Absolute Deviation test for a spatial point pattern.

Keywords
htest, spatial
Usage
clf.test(X, ..., rinterval = NULL, use.theo=FALSE)
mad.test(X, ..., rinterval = NULL, use.theo=FALSE)
Arguments
X
Either a point pattern (object of class "ppp", "lpp" or other class), a fitted point process model (object of class "ppm", "kppm" or other class) or an envelope object (class "envelope"
...
Arguments passed to envelope. Useful arguments include fun to determine the summary function, nsim to specify the number of Monte Carlo simulations, and verbose=
rinterval
Interval of values of the summary function argument r over which the maximum absolute deviation, or the integral, will be computed for the test. A numeric vector of length 2.
use.theo
Logical value determining whether to compare the summary function for the data to its theoretical value for CSR (use.theo=TRUE) or to the sample mean of simulations from CSR (use.theo=FALSE).
Details

These functions perform hypothesis tests for goodness-of-fit of a point pattern dataset to a point process model, based on Monte Carlo simulation from the model.

clf.test performs the test advocated by Loosmore and Ford (2006) which is also described in Cressie (1991, page 667, equation (8.5.42)).

mad.test performs the global or Maximum Absolute Deviation test described by Ripley (1977, 1981).

The type of test depends on the type of argument X.

• IfXis some kind of point pattern, then a test of Complete Spatial Randomness (CSR) will be performed. That is, the null hypothesis is that the point pattern is completely random.
• IfXis a fitted point process model, then a test of goodness-of-fit for the fitted model will be performed. The model object contains the data point pattern to which it was originally fitted. The null hypothesis is that the data point pattern is a realisation of the model.
• IfXis an envelope object generated byenvelope, then it should have been generated withsavefuns=TRUEorsavepatterns=TRUEso that it contains simulation results. These simulations will be treated as realisations from the null hypothesis.
In all cases, the algorithm will first call envelope to generate or extract the simulated summary functions. The number of simulations that will be generated or extracted, is determined by the argument nsim, and defaults to 99. The summary function that will be computed is determined by the argument fun (or the first unnamed argument in the list ...) and defaults to Kest (except when X is an envelope object generated with savefuns=TRUE, when these functions will be taken).

The choice of summary function fun affects the power of the test. It is normally recommended to apply a variance-stabilising transformation (Ripley, 1981). If you are using the $K$ function, the normal practice is to replace this by the $L$ function (Besag, 1977) computed by Lest. If you are using the $F$ or $G$ functions, the recommended practice is to apply Fisher's variance-stabilising transformation $\sin^{-1}\sqrt x$ using the argument transform. See the Examples.

Value

• An object of class "htest". Printing this object gives a report on the result of the test. The $p$-value is contained in the component p.value.

References

Besag, J. (1977) Discussion of Dr Ripley's paper. Journal of the Royal Statistical Society, Series B, 39, 193--195. Cressie, N.A.C. (1991) Statistics for spatial data. John Wiley and Sons, 1991.

Loosmore, N.B. and Ford, E.D. (2006) Statistical inference using the G or K point pattern spatial statistics. Ecology 87, 1925--1931.

Ripley, B.D. (1977) Modelling spatial patterns (with discussion). Journal of the Royal Statistical Society, Series B, 39, 172 -- 212.

Ripley, B.D. (1981) Spatial statistics. John Wiley and Sons.

envelope

Aliases
• clf.test
clf.test(cells, Lest)
clf.test(cells, Gest, transform=expression(asin(sqrt(.))), verbose=FALSE)