surveillance: stK – R documentation

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stK

Diggle et al (1995) K-function test for space-time clustering

Description

The function stKtest wraps functions in package splancs to perform the K-function based Monte Carlo permutation test for space-time clustering (Diggle et al, 1995) for "epidataCS". The implementation is due to Meyer et al. (2016).

Usage

stKtest(object, eps.s = NULL, eps.t = NULL, B = 199,
        cores = 1, seed = NULL, poly = object$W)

## S3 method for class 'stKtest'
plot(x, which = c("D", "R", "MC"),
     args.D = list(), args.D0 = args.D, args.R = list(), args.MC = list(),
     mfrow = sort(n2mfrow(length(which))), ...)

Arguments

`object`	an object of class `"epidataCS"`.
`eps.s, eps.t`	numeric vectors defining the spatial and temporal grids of critical distances over which to evaluate the test. The default (`NULL`) uses equidistant values from 0 to the smallest `eps.s`/`eps.t` value in `object$events`, but not larger than half the observed spatial/temporal domain.
`B`	the number of permutations.
`cores`	the number of parallel processes over which to distribute the requested number of permutations.
`seed`	argument for `set.seed` to initialize the random number generator such that results become reproducible (also if `cores > 1`, see `plapply`).
`poly`	the polygonal observation region of the events (as an object handled by `xylist`). The default `object$W` might not work since package splancs does not support multi-polygons. In this case, the `poly` argument can be used to specify a substitute.
`x`	an `"stKtest"`.
`which`	a character vector indicating which diagnostic plots to produce. The full set is `c("D", "D0", "R", "MC")`. The special value `which = "stdiagn"` means to call the associated splancs function `stdiagn`.
`args.D,args.D0,args.R,args.MC`	argument lists for the plot functions `persp` (for `"D"` and `"D0"`), `plot.default` (`"R"`), and `truehist` (`"MC"`), respectively, to modify the default settings. Ignored if `which = "stdiagn"`.
`mfrow`	`par`-setting to layout the plots. Ignored for `which = "stdiagn"` and if set to `NULL`.
`...`	ignored (argument of the generic).

Value

an object of class "stKtest" (inheriting from "htest"), which is a list with the following components:

`method`	a character string indicating the type of test performed.
`data.name`	a character string naming the supplied `object`.
`statistic`	the sum U of the standardized residuals R(s,t).
`parameter`	the number `B` of permutations.
`p.value`	the p-value for the test.
`pts`	the coordinate matrix of the event locations (for `stdiagn`.
`stK`	the estimated K-function as returned by `stkhat`.
`seD`	the standard error of the estimated D(s,t) as returned by `stsecal`.
`mctest`	the observed and permutation values of the test statistic as returned by `stmctest`.

The plot-method invisibly returns NULL.

Author(s)

Sebastian Meyer

References

Diggle, P. J.; Chetwynd, A. G.; Häggkvist, R. and Morris, S. E. (1995): Second-order analysis of space-time clustering Statistical Methods in Medical Research, 4, 124-136.

Meyer, S., Warnke, I., Rössler, W. and Held, L. (2016): Model-based testing for space-time interaction using point processes: An application to psychiatric hospital admissions in an urban area. Spatial and Spatio-temporal Epidemiology, 17, 15-25. doi: 10.1016/j.sste.2016.03.002. Eprint: https://arxiv.org/abs/1512.09052.

Examples

if (requireNamespace("splancs")) {
    data("imdepi")
    imdepiB <- subset(imdepi, type == "B")
    mainpoly <- coordinates(imdepiB$W@polygons[[1]]@Polygons[[5]])
    if (surveillance.options("allExamples")) {
        SGRID <- c(0, 10, 25, 50, 75, 100, 150, 200)
        TGRID <- c(0, 7, 14, 21, 28)
        B <- 99
        CORES <- 2
    } else { # dummy settings for fast CRAN checks
        SGRID <- c(0, 50)
        TGRID <- c(0, 30)
        B <- 9
        CORES <- 1
    }
    imdBstKtest <- stKtest(imdepiB, eps.s = SGRID, eps.t = TGRID, B = B,
                           cores = CORES, seed = 1, poly = list(mainpoly))
    print(imdBstKtest)
    plot(imdBstKtest)
}

surveillance

Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

v1.19.1

GPL-2

Authors

Michael H<f6>hle [aut, ths] (<https://orcid.org/0000-0002-0423-6702>), Sebastian Meyer [aut, cre] (<https://orcid.org/0000-0002-1791-9449>), Michaela Paul [aut], Leonhard Held [ctb, ths], Howard Burkom [ctb], Thais Correa [ctb], Mathias Hofmann [ctb], Christian Lang [ctb], Juliane Manitz [ctb], Andrea Riebler [ctb], Daniel Saban<e9>s Bov<e9> [ctb], Ma<eb>lle Salmon [ctb], Dirk Schumacher [ctb], Stefan Steiner [ctb], Mikko Virtanen [ctb], Wei Wei [ctb], Valentin Wimmer [ctb], R Core Team [ctb] (A few code segments are modified versions of code from base R)

Initial release

2021-03-30

stK