hierarchical clustering with spatial soft constraints

Usage

hclustgeo_disc(data, n, alpha = 0.5, D1 = NULL, scale = TRUE, wt = NULL, ...)

Arguments

data: An sf object, tibble, data.frame, matrix or vector of observations data.
n: The number of hierarchical clustering classes, which can be a numeric value or vector.
alpha: (optional) A positive value between 0 and 1. This mixing parameter gives the relative importance of "feature" space and "constraint" space. Default is 0.5.
D1: (optional) A matrix with other dissimilarities between the same observations data. if data is an sf object and alpha is not 0, the D1 will be generated by sdsfun::sf_distance_matrix(), others will use a matrix with all elements equal to 0.
scale: (optional) Whether to scaled the dissimilarities matrix, default is TRUE.
wt: (optional) Vector with the weights of the observations. By default, wt is NULL.
...: (optional) Other arguments passed to stats::dist().

Value

A vector with grouped memberships if n are scalar, otherwise a matrix with grouped memberships is returned where each column corresponds to the elements of n, respectively.

Note

This is a C++ enhanced implementation of the hclustgeo function in ClustGeo package.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
gzma$group = hclustgeo_disc(gzma,5,alpha = 0.75)
plot(gzma["group"])