rflex.zones {smerc} | R Documentation |
rflex.zones
determines the unique zones to
consider for the flexibly shaped spatial scan test of
Tango and Takahashi (2012). The algorithm uses a
breadth-first search to find all subgraphs connected to
each vertex (region) in the data set of size k or
less with the constraint that the middle p-value of each
region must be less than alpha1
.
rflex.zones( nn, w, cases, ex, alpha1 = 0.2, type = "poisson", pop = NULL, cl = NULL, loop = FALSE, verbose = FALSE, pfreq = 1 )
nn |
An n by k matrix providing the k nearest
neighbors of each region, presumably produced by the
|
w |
A binary spatial adjacency matrix for the regions. |
cases |
The number of cases observed in each region. |
ex |
The expected number of cases for each region. The default is calculated under the constant risk hypothesis. |
alpha1 |
The middle p-value threshold. |
type |
The type of scan statistic to compute. The
default is |
pop |
The population size associated with each
region. The default is |
cl |
A cluster object created by |
loop |
A logical value indicating whether a loop
should be used to implement the function instead of
|
verbose |
A logical value indicating whether
progress messages should be provided.
The default is |
pfreq |
The frequency that messages are reported
from the loop (if |
Returns a list of zones to consider for clustering. Each element of the list contains a vector with the location ids of the regions in that zone.
Joshua French
Tango, T. and Takahashi, K. (2012), A flexible spatial scan statistic with a restricted likelihood ratio for detecting disease clusters. Statist. Med., 31: 4207-4218. <doi:10.1002/sim.5478>
rflex.midp
data(nydf) data(nyw) coords = cbind(nydf$x, nydf$y) nn = knn(coords, k = 5) cases = floor(nydf$cases) pop = nydf$pop ex = pop * sum(cases)/sum(pop) # zones for poisson model pzones = rflex.zones(nn, w = nyw, cases = cases, ex = ex) ## Not run: pzones = rflex.zones(nn, w = nyw, cases = cases, ex = ex, verbose = TRUE) # zones for binomial model bzones = rflex.zones(nn, w = nyw, cases = cases, ex = ex, type = "binomial", pop = pop) ## End(Not run)