`scan.test`

performs the original spatial scan test
of Kulldorf (1997) based on a fixed number of cases.
Candidate zones are circular and extend from the observed
region centroids. The clusters returned are
non-overlapping, ordered from most significant to least
significant. The first cluster is the most likely to be
a cluster. If no significant clusters are found, then
the most likely cluster is returned (along with a
warning).

```
scan.test(
coords,
cases,
pop,
ex = sum(cases)/sum(pop) * pop,
nsim = 499,
alpha = 0.1,
ubpop = 0.5,
longlat = FALSE,
cl = NULL,
type = "poisson",
min.cases = 2,
simdist = "multinomial"
)
```

coords

An \(n \times 2\) matrix of centroid coordinates for the regions.

cases

The number of cases observed in each region.

pop

The population size associated with each region.

ex

The expected number of cases for each region. The default is calculated under the constant risk hypothesis.

nsim

The number of simulations from which to compute the p-value.

alpha

The significance level to determine whether a cluster is signficant. Default is 0.10.

ubpop

The upperbound of the proportion of the total population to consider for a cluster.

longlat

The default is `FALSE`

, which
specifies that Euclidean distance should be used. If
`longlat`

is `TRUE`

, then the great circle
distance is used to calculate the intercentroid
distance.

cl

A cluster object created by `makeCluster`

,
or an integer to indicate number of child-processes
(integer values are ignored on Windows) for parallel evaluations
(see Details on performance).

type

The type of scan statistic to compute. The
default is `"poisson"`

. The other choice
is `"binomial"`

.

min.cases

The minimum number of cases required for a cluster. The default is 2.

simdist

Character string indicating the simulation
distribution. The default is `"multinomial"`

, which
conditions on the total number of cases observed. The
other options are `"poisson"`

and `"binomial"`

Returns a `smerc_cluster`

object.

Kulldorff, M. (1997) A spatial scan statistic. Communications in Statistics - Theory and Methods, 26(6): 1481-1496, <doi:10.1080/03610929708831995>

Waller, L.A. and Gotway, C.A. (2005). Applied Spatial Statistics for Public Health Data. Hoboken, NJ: Wiley.

`print.smerc_cluster`

,
`summary.smerc_cluster`

,
`plot.smerc_cluster`

,
`scan.stat`

# NOT RUN { data(nydf) coords = with(nydf, cbind(longitude, latitude)) out = scan.test(coords = coords, cases = floor(nydf$cases), pop = nydf$pop, nsim = 0, alpha = 1, longlat = TRUE) ## plot output for new york state # specify desired argument values mapargs = list(database = "county", region = "new york", xlim = range(out$coords[,1]), ylim = range(out$coords[,2])) # needed for "state" database (unless you execute library(maps)) data(countyMapEnv, package = "maps") plot(out, usemap = TRUE, mapargs = mapargs) # a second example to match the results of Waller and Gotway (2005) # in chapter 7 of their book (pp. 220-221). # Note that the 'longitude' and 'latitude' used by them has # been switched. When giving their input to SatScan, the coords # were given in the order 'longitude' and 'latitude'. # However, the SatScan program takes coordinates in the order # 'latitude' and 'longitude', so the results are slightly different # from the example above. coords = with(nydf, cbind(y, x)) out2 = scan.test(coords = coords, cases = floor(nydf$cases), pop = nydf$pop, nsim = 0, alpha = 1, longlat = TRUE) # the cases observed for the clusters in Waller and Gotway: 117, 47, 44 # the second set of results match sget(out2$clusters, name = "cases")[1:3] # }