| stmctest {splancs} | R Documentation |
Perform a Monte-Carlo test of space-time clustering.
stmctest(pts, times, poly, tlimits, s, tt, nsim, quiet=FALSE)
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=TRUE 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.
|
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.
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.
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.
stkhat, stsecal, stvmat, stdiagn
example(stkhat) bur1mc <- stmctest(burpts, burkitt$t, burbdy, c(400, 5800), seq(1,40,2), seq(100, 1500, 100), nsim=49, quiet=TRUE)