splancs (version 2.01-4)

stmctest: Monte-Carlo test of space-time clustering

Description

Perform a Monte-Carlo test of space-time clustering.

Usage

stmctest(pts, times, poly, tlimits, s, tt, nsim, quiet=F)

Arguments

pts

A set of points as used by Splancs.

times

A vector of times, the same length as the number of points in pts.

poly

A polygon enclosing the points.

tlimits

A vector of length 2, specifying the upper and lower temporal domain.

s

A vector of spatial distances for the analysis.

tt

A vector of times for the analysis.

nsim

The number of simulations to do.

quiet

If quiet=T then no output is produced, otherwise the function prints the number of simulations completed so far, and also how the test statistic for the data ranks with the simulations.

Value

• A vector of length nsim+1. The first element is the test statistic for the data, and the remaining elements are those for the simulations.

Details

The function uses a sum of residuals as a test statistic, randomly permutes the times of the set of points and recomputes the test statistic for a number of simulations. See Diggle, Chetwynd, Haggkvist and Morris (1995) for details.

References

Diggle, P., Chetwynd, A., Haggkvist, R. and Morris, S. 1995 Second-order analysis of space-time clustering. Statistical Methods in Medical Research, 4, 124-136;Bailey, T. C. and Gatrell, A. C. 1995, Interactive spatial data analysis. Longman, Harlow, pp. 122-125; Rowlingson, B. and Diggle, P. 1993 Splancs: spatial point pattern analysis code in S-Plus. Computers and Geosciences, 19, 627-655; the original sources can be accessed at: http://www.maths.lancs.ac.uk/~rowlings/Splancs/. See also Bivand, R. and Gebhardt, A. 2000 Implementing functions for spatial statistical analysis using the R language. Journal of Geographical Systems, 2, 307-317.

See Also

stkhat, stsecal, stvmat, stdiagn

Examples

example(stkhat)
bur1mc <- stmctest(burpts, burkitt$t, burbdy, c(400, 5800),
  seq(1,40,2), seq(100, 1500, 100), nsim=49, quiet=T)