nnpop: Determine nearest neighbors for minimum spanning trees
Description
mst.nn determines the nearest
neighbors for a set of observations based on the
distance matrix according to a distance-based upperbound.
These neighbors are directly related to the zones
considered in dmst.test,
dc.test, and mlink.test
Usage
nnpop(d, pop, ubpop)
scan.nn(d, pop, ubpop)
Arguments
d
An \(n\times n\) square distance matrix
containing the intercentroid distance between the
\(n\) region centroids.
pop
The population size associated with each
region.
ubpop
The upperbound of the proportion of the
total population to consider for a cluster.
Value
Returns the indices of the nearest neighbors as a
list. For each element of the list, the indices are
ordered from nearest to farthest from each centroid.
Details
This function determines the nearest neighbors of each
centroid based on the intercentroid distance. The number
of nearest neighbors is limited by the sum of the
population values among the nearest neighbors. The set
of nearest neighbors can contain no more than ubpop
* sum(pop) members of the population. The nearest
neighbors are ordered from nearest to farthest.
# NOT RUN {data(nydf)
coords = as.matrix(nydf[,c("longitude", "latitude")])
d = as.matrix(dist(coords))
nn = scan.nn(d, pop = nydf$pop, ubpop = 0.1)
# }