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$events)), eps.s = 50, simulate.p.value = TRUE, B = 19, .parallel = 2, .seed = 1, .verbose = FALSE ) knoxtest
Knox test with Poisson approximation data: dt = dist(imdepiB$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?