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: | 0.1.0 |
Built: | 2024-12-23 13:40:05 UTC |
Source: | CRAN |
Information-Theoretic Measures for Spatial Association
itm( formula, data, method = c("vm", "icm"), beta = 1, unit = c("e", "2", "10"), seed = 42, permutation_number = 999 )
itm( formula, data, method = c("vm", "icm"), beta = 1, unit = c("e", "2", "10"), seed = 42, permutation_number = 999 )
formula |
A formula. |
data |
A |
method |
(optional) whether |
beta |
(optional) The |
unit |
(optional) Logarithm base, default is |
seed |
(optional) Random number seed, default is |
permutation_number |
(optional) Number of Random Permutations, default is |
A tibble
.
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')
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')