smacpod (version 2.0)

spscan.test: Spatial Scan Test

Description

spscan.test performs the spatial scan test of Kulldorf (1997) for case/control point data.

Usage

spscan.test(x, case = 2, nsim = 499, alpha = 0.1, maxd = NULL,
  cl = NULL, longlat = FALSE)

Arguments

x

A ppp object from the spatstat package with marks for the case and control groups.

case

The position of the name of the "case" group in levels(x$marks). The default is 2. x$marks is assumed to be a factor. Automatic conversion is attempted if it is not.

nsim

The number of simulations from which to compute the p-value. A non-negative integer. Default is 499.

alpha

The significance level to determine whether a cluster is signficiant. Default is 0.1.

maxd

The radius of the largest possible cluster to consider. Default is NULL, i.e., no limit.

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.

longlat

A logical value indicating whether Euclidean distance (FALSE) or Great Circle (WGS84 ellipsoid, FALSE) should be used. Default is FALSE, i.e., Euclidean distance.

Value

Returns a list of length two of class scan. The first element (clusters) is a list containing the significant, non-ovlappering clusters, and has the the following components:

coords

The centroid of the significant clusters.

r

The radius of the window of the clusters.

pop

The total population in the cluser window.

cases

The observed number of cases in the cluster window.

expected

The expected number of cases in the cluster window.

smr

Standarized mortaility ratio (observed/expected) in the cluster window.

rr

Relative risk in the cluster window.

propcases

Proportion of cases in the cluster window.

loglikrat

The loglikelihood ratio for the cluster window (i.e., the log of the test statistic).

pvalue

The pvalue of the test statistic associated with the cluster window.

The second element of the list is the centroid coordinates. This is needed for plotting purposes.

Details

The test is performed using the random labeling hypothesis. The windows are circular and extend from the observed data locations. 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).

Setting cl to a positive integer MAY speed up computations on non-Windows computers. However, parallelization does have overhead cost, and there are cases where parallelization results in slower computations.

References

Waller, L.A. and Gotway, C.A. (2005). Applied Spatial Statistics for Public Health Data. Hoboken, NJ: Wiley. Kulldorff M., Nagarwalla N. (1995) Spatial disease clusters: Detection and Inference. Statistics in Medicine 14, 799-810. Kulldorff, M. (1997) A spatial scan statistic. Communications in Statistics -- Theory and Methods 26, 1481-1496.

Examples

# NOT RUN {
data(grave)
out = spscan.test(grave)
plot(out, chars = c(1, 20), main = "most likely cluster")
# get warning if no significant cluster
out2 = spscan.test(grave, alpha = 0.01)
# }