Package 'itmsa'

Title: Information-Theoretic Measures for Spatial Association
Description: Leveraging information-theoretic measures like mutual information and v-measure to quantify spatial associations between patterns (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>; Bai, H. et al. (2023) <doi:10.1080/24694452.2023.2223700>).
Authors: Wenbo Lv [aut, cre, cph]
Maintainer: Wenbo Lv <[email protected]>
License: GPL-3
Version: 0.2.0
Built: 2024-12-23 14:21:23 UTC
Source: https://github.com/stscl/itmsa

Help Index

Information-Theoretic Measures for Spatial Association

Description

Information-Theoretic Measures for Spatial Association

Usage

itm(
  formula,
  data,
  method = c("vm", "icm"),
  beta = 1,
  unit = c("e", "2", "10"),
  seed = 42,
  permutation_number = 999
)

Arguments

formula

A formula.
data

A data.frame, tibble or sf object of observation data.
method

(optional) whether vm(default) or icm.
beta

(optional) The β\beta value used fo vm measure, default is 1.
unit

(optional) Logarithm base, default is e.
seed

(optional) Random number seed, default is 42.
permutation_number

(optional) Number of Random Permutations, default is 999.

Value

A tibble.

Examples

sim = readr::read_csv(system.file('extdata/sim.csv',package = 'itmsa'))

# Information-theoretical V-measure
itm(z1 ~ z2, data = sim, method = 'vm')
# Information Consistency-Based Measures
itm(z1 ~ z2, data = sim, method = 'icm')