scan.sim.adj | 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.adj(
nsim = 1,
nn,
ty,
ex,
type = "poisson",
logein = NULL,
logeout = NULL,
tpop = NULL,
popin = NULL,
popout = NULL,
logpopin = NULL,
logpopout = NULL,
cl = NULL,
simdist = "multinomial",
pop = 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. |
type |
The type of scan statistic to compute. The
default is |
logein |
The |
logeout |
The |
tpop |
The total population in the study area. |
popin |
The total population in the zone. |
popout |
The population outside the zone. This
should be |
logpopin |
The |
logpopout |
The |
cl |
A cluster object created by |
simdist |
Character string indicating the simulation
distribution. The default is |
pop |
The population size associated with each region. |
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)
coords <- with(nydf, cbind(longitude, latitude))
d <- gedist(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.adj(
nsim = 2, nn, ty, ex,
logein = log(ein),
logeout = log(sum(ex) - ein)
)
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