skater {rgeoda}

Spatial C(K)luster Analysis by Tree Edge Removal

Description

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

Usage

skater(
  k,
  w,
  df,
  bound_variable = data.frame(),
  min_bound = 0,
  scale_method = "standardize",
  distance_method = "euclidean",
  random_seed = 123456789,
  cpu_threads = 6,
  rdist = numeric()
)

Arguments

k

The number of clusters

w

An instance of Weight class

df

A data frame with selected variables only. E.g. guerry[c("Crm_prs", "Crm_prp", "Litercy")]

bound_variable

(optional) A data frame with selected bound variable

min_bound

(optional) A minimum bound value that applies to all clusters

scale_method

One of the scaling methods 'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust' to apply on input data. Default is 'standardize' (Z-score normalization).

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

rdist

(optional) The distance matrix (lower triangular matrix, column wise storage)

Value

A names list with names "Clusters", "Total sum of squares", "Within-cluster sum of squares", "Total within-cluster sum of squares", and "The ratio of between to total sum of squares".

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
guerry_clusters <- skater(4, queen_w, data)
guerry_clusters