View source: R/elliptic.sim.adj.R
elliptic.sim.adj | 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.adj( nsim = 1, ex, nn, ty, logein, logeout, a, pen, min.cases = 2, cl = NULL )
nsim |
A positive integer indicating the number of simulations to perform. |
ex |
The expected number of cases for each region. The default is calculated under the constant risk hypothesis. |
nn |
A list of nearest neighbors produced by
|
ty |
The total number of cases in the study area. |
logein |
The |
logeout |
The |
a |
The penalty for the spatial scan statistic. The default is 0.5. |
pen |
The eccentricity penalty for each candidate zone. |
min.cases |
The minimum number of cases required for a cluster. The default is 2. |
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)) pop <- nydf$pop enn <- elliptic.nn(coords, pop, ubpop = 0.5) cases <- floor(nydf$cases) ty <- sum(cases) ex <- ty / sum(pop) * pop yin <- nn.cumsum(enn$nn, cases) ein <- nn.cumsum(enn$nn, ex) logein <- log(ein) logeout <- log(ty - ein) pen <- elliptic.penalty(0.5, enn$shape_all) tsim <- elliptic.sim.adj( nsim = 3, ex = ex, nn = enn$nn, ty = ty, logein = logein, logeout = logeout, a = 0.5, pen = pen )
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