scan.test {smerc} | R Documentation |
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 |
cl |
A cluster object created by |
type |
The type of scan statistic to compute. The
default is |
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 |
Returns a smerc_cluster
object.
Joshua French
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
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]