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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