flex.sim | R Documentation |
flex.test
on simualated dataflex.sim
efficiently performs
flex.test
on a simulated data set. The
function is meant to be used internally by the
flex.test
function, but is informative for
better understanding the implementation of the test.
flex.sim( nsim = 1, zones, ty, ex, type = "poisson", ein = NULL, eout = NULL, tpop = NULL, popin = NULL, popout = NULL, cl = NULL )
nsim |
A positive integer indicating the number of simulations to perform. |
zones |
A list of zones to compute the test statistic over for each simulated data set. |
ty |
The total number of cases in the study area. |
ex |
The expected number of cases for each region. The default is calculated under the constant risk hypothesis. |
type |
The type of scan statistic to compute. The
default is |
ein |
The expected number of cases in the zone. Conventionally, this is the estimated overall disease risk across the study area, multiplied by the total population size of the zone. |
eout |
The expected number of cases outside the
zone. This should be |
tpop |
The total population in the study area. |
popin |
The total population in the zone. |
popout |
The population outside the zone. This
should be |
cl |
A cluster object created by |
A vector with the maximum test statistic for each simulated data set.
data(nydf) data(nyw) coords <- with(nydf, cbind(longitude, latitude)) zones <- flex.zones(coords, w = nyw, k = 3, longlat = TRUE) cases <- floor(nydf$cases) ty <- sum(cases) ex <- ty / sum(nydf$pop) * nydf$pop ein <- zones.sum(zones, ex) tsim <- flex.sim(nsim = 2, zones, ty, ex, ein = ein, eout = ty - ein)
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