View source: R/elliptic.sim.old.R
elliptic.sim | R Documentation |
elliptic.test
on simulated dataelliptic.sim
efficiently performs
elliptic.test
on a simulated data set. The
function is meant to be used internally by the
elliptic.test
function, but is informative
for better understanding the implementation of the test.
elliptic.sim( nsim = 1, nn, ty, ex, a, shape_all, ein, eout, cl = NULL, min.cases = 2 )
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. |
a |
The penalty for the spatial scan statistic. The default is 0.5. |
shape_all |
A vector of the shapes associated with
all of the possible zones constructed from |
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 |
cl |
A cluster object created by |
min.cases |
The minimum number of cases required for a cluster. The default is 2. |
A vector with the maximum test statistic for each simulated data set.
data(nydf) data(nyw) coords <- with(nydf, cbind(longitude, latitude)) pop <- nydf$pop enn <- elliptic.nn(coords, pop, ubpop = 0.1, shape = c(1, 1.5), nangle = c(1, 4) ) cases <- floor(nydf$cases) ty <- sum(cases) ex <- ty / sum(pop) * pop yin <- nn.cumsum(enn$nn, cases) ein <- nn.cumsum(enn$nn, ex) tsim <- elliptic.sim( nsim = 2, nn = enn$nn, ty = ty, ex = ex, a = 0.5, shape_all = enn$shape_all, ein = ein, eout = ty - ein )
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