The function knox of the "Surveillance" package performs Knox test for
space-time interaction. The output is supposed to give the numbers of
events that occur in a specific distance in space and time define by the
function arguments. This function also perform a Monte Carlo permutation
test, which give the estimated value (based on a random distribution) which
are supposed to be compared to the observed value. This comparison gives a
ratio which means the chance that event occur in a specific distance in
time and space.
Here is the example of the function and it's output:
library(surveillance)
data("imdepi")
imdepiB <- subset(imdepi, type == "B")
## Obtain the p-value via a Monte Carlo permutation test,
## where the permutations can be computed in parallel
## (using forking on Unix-alikes and a cluster on Windows, see
?plapply)
knoxtest <- knox(
dt = dist(imdepiB$events$time), eps.t = 30,
ds = dist(coordinates(imdepiB$events)), eps.s = 50,
simulate.p.value = TRUE, B = 19,
.parallel = 2, .seed = 1, .verbose = FALSE
)
knoxtest
Output:
Knox test with Poisson approximation
data: dt = dist(imdepiB$events$time) and ds =
dist(coordinates(imdepiB$events))
number of close pairs = 204, lambda = 181.57, p-value = 0.04649
alternative hypothesis: true number is greater than 181.5686
contingency table:
ds
dt <= 50 > 50
<= 30 204 1295
> 30 6613 48168
The "imdepi" dataset include 636 events, so, the output isn't a count of the event occuring in a specific distance in space and time. Also, even if I change the value of "B", which correspond to the number of permutation for the Monte Carlo approach, the results stays the same. *What the numbers of output contingency table mean?How the Monte Carlo iteration influence the results?* Here are the links to the Knox Test function Description [1] and it's Code [2]. [1]: http://finzi.psych.upenn.edu/R/library/surveillance/html/knox.html [2]: https://r-forge.r-project.org/scm/viewvc.php/pkg/R/knox.R?view=markup&root=surveillance&pathrev=1292 In advance, thank you for your help