skater: Spatial C(K)luster Analysis by Tree Edge Removal

In lixun910/rgeoda: R Library for Spatial Data Analysis

Description

SKATER forms clusters by spatially partitioning data that has similar values for features of interest.

Usage

skater(
  k,
  w,
  data,
  bound_vals = vector("numeric"),
  min_bound = 0,
  distance_method = "euclidean",
  random_seed = 123456789,
  cpu_threads = 6
)

Arguments

k	The number of clusters
w	An instance of Weight class
data	A list of numeric vectors of selected variable
bound_vals	(optional) A 1-d vector of selected bounding variable
min_bound	(optional) A minimum value that the sum value of bounding variable int each cluster should be greater than
distance_method	(optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
random_seed	(int,optional) The seed for random number generator. Defaults to 123456789.
cpu_threads	(optional) The number of cpu threads used for parallel computation

Value

A list of numeric vectors represents a group of clusters

Examples

guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- geoda_open(guerry_path)
queen_w <- queen_weights(guerry)
guerry_df <- as.data.frame(guerry) # use as data.frame
data <- guerry_df[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
guerry_clusters <- skater(4, queen_w, data)
guerry_clusters

lixun910/rgeoda documentation built on March 19, 2021, 3:49 p.m.