local_multiquantilelisa: Multivariate Quantile LISA Statistics

In GeoDaCenter/rgeoda: R Library for Spatial Data Analysis

Multivariate Quantile LISA Statistics

Description

The function to apply multivariate quantile LISA statistics

Usage

local_multiquantilelisa(
  w,
  df,
  k,
  q,
  permutations = 999,
  permutation_method = "complete",
  significance_cutoff = 0.05,
  cpu_threads = 6,
  seed = 123456789
)

Arguments

w	An instance of Weight object
df	A data frame with selected variables only. E.g. guerry[c("TopCrm", "TopWealth", "TopLit")]
k	A vector of "k" values indicate the number of quantiles for each variable. Value range e.g. [1, 10]
q	A vector of "q" values indicate which quantile or interval for each variable used in local join count statistics. Value stars from 1.
permutations	(optional) The number of permutations for the LISA computation
permutation_method	(optional) The permutation method used for the LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.
significance_cutoff	(optional) A cutoff value for significance p-values to filter not-significant clusters
cpu_threads	(optional) The number of cpu threads used for parallel LISA computation
seed	(optional) The seed for random number generator

Value

An instance of LISA-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_multiquantilelisa(queen_w, guerry[c("Crm_prp", "Litercy")],
k=c(4,4), q=c(1,1))
clsts <- lisa_clusters(lisa)
clsts

GeoDaCenter/rgeoda documentation built on June 9, 2025, 7:06 p.m.