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
)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.