Package 'localsp'

Title: Local Indicator of Stratified Power
Description: Implements a local indicator of stratified power to analyze local spatial stratified association and demonstrate how spatial stratified association changes spatially and in local regions, as outlined in Hu et al. (2024) <doi:10.1080/13658816.2024.2437811>.
Authors: Wenbo Lyu [aut, cre, cph] (ORCID: <https://orcid.org/0009-0002-6003-3800>), Yongze Song [aut] (ORCID: <https://orcid.org/0000-0003-3420-9622>), Jiao Hu [aut] (ORCID: <https://orcid.org/0000-0002-9644-9763>)
Maintainer: Wenbo Lyu <[email protected]>
License: GPL-3
Version: 0.2.0
Built: 2026-05-19 09:07:25 UTC
Source: https://github.com/ausgis/localsp

Help Index

local indicator of stratified power

Description

local indicator of stratified power

Usage

lisp(
  formula,
  data,
  threshold,
  distmat,
  discvar = NULL,
  discnum = 3:8,
  discmethod = c("sd", "equal", "geometric", "quantile", "natural"),
  cores = 1,
  ...
)

Arguments

formula

A formula.
data

The observation data.
threshold

The distance threshold employed to select "local" data.
distmat

The distance matrices.
discvar

(optional) Name of continuous variable columns that need to be discretized. Noted that when formula has discvar, data must have these columns. By default, all independent variables are used as discvar.
discnum

(optional) A vector of number of classes for discretization. Default is 3:8.
discmethod

(optional) A vector of methods for discretization, default is using c("sd","equal","geometric","quantile","natural") by invoking sdsfun.
cores

(optional) Positive integer (default is 1). When cores are greater than 1, use multi-core parallel computing.
...

(optional) Other arguments passed to gdverse::gd_optunidisc(). A useful parameter is seed, which is used to set the random number seed.

Value

A tibble.

Examples

gtc = readr::read_csv(system.file("extdata/gtc.csv", package = "localsp"))
gtc


# Sample 100 observations from the original data to save runtime;
# This is unnecessary in practice;
set.seed(42)
gtc1 = gtc[sample.int(nrow(gtc),size = 100),]
distmat = as.matrix(dist(gtc1[, c("X","Y")]))
gtc1 = gtc1[, -c(1,2)]
gtc1

# Use 2 cores for parallel computing;
# Increase cores in practice to speed up;
lisp(GTC ~ ., data = gtc1, threshold = 8, distmat = distmat,
     discnum = 3:5, discmethod = "quantile", cores = 2)