R: Space-time K-functions

stkhat {splancs} | R Documentation

## Space-time K-functions

### Description

Compute the space-time K-functions

### Usage

    stkhat(pts, times, poly, tlimits, s, tm)

### Arguments

`pts` | A set of points as defined in 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.
`tm` | A vector of times for the analysis

### Value

A list with the following components is returned:

`s, t` | The spatial and temporal scales
`ks` | The spatial K-function
`kt` | The temporal K-function
`kst` | The space-time K-function

For details see Diggle, Chetwynd, Haggkvist, and Morris (1995)

### 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: 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.

### See Also

`stsecal`, `stvmat`, `stmctest`, `stdiagn`

### Examples

    data(burkitt)
    bur1 <- stkhat(burpts, burkitt$t, burbdy, c(400, 5800),
      seq(1,40,2), seq(100, 1500, 100))
    oldpar <- par(mfrow=c(2,1))
    plot(bur1$s, bur1$ks, type="l", xlab="distance", ylab="Estimated K",
      main="spatial K function")
    plot(bur1$t, bur1$kt, type="l", xlab="time", ylab="Estimated K",
      main="temporal K function")
    par(oldpar)

[Package splancs version 2.01-43 Index]