smerc (version 1.8.3)

fast.test: Fast Subset Scan Test

Description

fast.test performs the fast subset scan test of Neill (2012).

Usage

fast.test(
  coords,
  cases,
  pop,
  ex = sum(cases)/sum(pop) * pop,
  nsim = 499,
  alpha = 0.1,
  ubpop = 0.5,
  longlat = FALSE,
  cl = NULL,
  type = "poisson"
)

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:

locids

The location ids of regions in a significant cluster.

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.

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.

Arguments

coords

An \(n \times 2\) matrix of centroid coordinates for the regions in the form (x, y) or (longitude, latitude) is using great circle distance.

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). It can also be "future" to use a future backend (see Details), NULL (default) refers to sequential evaluation.

type

The type of scan statistic to compute. The default is "poisson". The other choice is "binomial".

Author

Joshua French

Details

The test is performed using the spatial scan test based on the Poisson test statistic and a fixed number of cases. The windows are based on the Upper Level Sets proposed by Patil and Taillie (2004). 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).

References

Neill, D. B. (2012), Fast subset scan for spatial pattern detection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 74: 337-360. <doi:10.1111/j.1467-9868.2011.01014.x>

See Also

print.smerc_cluster, summary.smerc_cluster, plot.smerc_cluster, scan.stat, scan.test

Examples

data(nydf)
coords <- with(nydf, cbind(longitude, latitude))
out <- fast.test(
  coords = coords, cases = floor(nydf$cases),
  pop = nydf$pop,
  alpha = 0.05, longlat = TRUE,
  nsim = 49, ubpop = 0.5
)