stmctest | R Documentation |
Perform a Monte-Carlo test of space-time clustering.
stmctest(pts, times, poly, tlimits, s, tt, nsim, quiet=FALSE, returnSims=FALSE)
pts |
A set of points as used by Splancs. |
times |
A vector of times, the same length as the number of points in |
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 |
returnSims |
default FALSE, if TRUE, return the |
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 list with components:
t0 |
The observed value of the statistic |
t |
A single column matrix with |
The example of using returned simulated values is included only to show how the values might be used, not to indicate that this constitutes a way of examining which observed values of the space-time measure are exceptional.
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: https://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$t, burbdy, c(400, 5800),
seq(1,40,2), seq(100, 1500, 100), nsim=49, quiet=TRUE, returnSims=TRUE)
plot(density(bur1mc$t), xlim=range(c(bur1mc$t0, bur1mc$t)))
abline(v=bur1mc$t0)
r0 <- attr(bur1mc, "obs")$kst-outer(attr(bur1mc, "obs")$ks, attr(bur1mc, "obs")$kt)
rsimlist <- lapply(attr(bur1mc, "sims"), function(x) x$kst - outer(x$ks, x$kt))
rarray <- array(do.call("cbind", rsimlist), dim=c(20, 15, 49))
rmin <- apply(rarray, c(1,2), min)
rmax <- apply(rarray, c(1,2), max)
r0 < rmin
r0 > rmax
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