data("imdepi")
imdepiB <- subset(imdepi, type == "B")
## Perform the Knox test using the Poisson approximation
knoxtest <- knox(
dt = dist(imdepiB$events$time), eps.t = 30,
ds = dist(coordinates(imdepiB$events)), eps.s = 50,
simulate.p.value = FALSE
)
knoxtest
## The Poisson approximation works well for these data since
## the proportion of close pairs is rather small (204/56280).
.opt <- options(xtable.comment = FALSE)
## contingency table in LaTeX
toLatex(knoxtest)
options(.opt)
## Obtain the p-value via a Monte Carlo permutation test,
## where the permutations can be computed in parallel
## (using forking on Unix-alikes and a cluster on Windows, see ?plapply)
knoxtestMC <- knox(
dt = dist(imdepiB$events$time), eps.t = 30,
ds = dist(coordinates(imdepiB$events)), eps.s = 50,
simulate.p.value = TRUE, B = 99, # limited here for speed
.parallel = 2, .seed = 1, .verbose = FALSE
)
knoxtestMC
plot(knoxtestMC)
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