scan.sim {smerc} | R Documentation |
scan.test
on simulated datascan.sim
efficiently performs
scan.test
on a simulated data set. The
function is meant to be used internally by the
scan.test
function, but is informative for
better understanding the implementation of the test.
scan.sim( nsim = 1, nn, ty, ex, type = "poisson", ein = NULL, eout = NULL, tpop = NULL, popin = NULL, popout = NULL, cl = NULL, simdist = "multinomial", pop = NULL )
nsim |
A positive integer indicating the number of simulations to perform. |
nn |
A list of nearest neighbors produced by |
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 |
simdist |
Character string indicating the simulation
distribution. The default is |
pop |
The population size associated with each region. |
A vector with the maximum test statistic for each simulated data set.
data(nydf) coords = with(nydf, cbind(longitude, latitude)) d = sp::spDists(as.matrix(coords), longlat = TRUE) nn = scan.nn(d, pop = nydf$pop, ubpop = 0.1) cases = floor(nydf$cases) ty = sum(cases) ex = ty/sum(nydf$pop) * nydf$pop yin = nn.cumsum(nn, cases) ein = nn.cumsum(nn, ex) tsim = scan.sim(nsim = 1, nn, ty, ex, ein = ein, eout = sum(ex) - ein)