Skip to contents

Metacoupling Analysis

Source: R/metacoupling.R
metacoupling.Rd

Metacoupling Analysis

Usage

metacoupling(
  data,
  swm_peri = NULL,
  swm_tele = NULL,
  weight = NULL,
  method = c("standard", "wang", "fan"),
  threads = 1
)

Arguments

data

A numeric matrix or data.frame. Rows are observations, columns are indicators.

swm_peri

A numeric matrix representing the peri (local) spatial weight matrix. Must be square with dimension equal to nrow(data). If NULL, a zero matrix is used.

swm_tele

A numeric matrix representing the tele (long-distance) spatial weight matrix. Must be square with dimension equal to nrow(data). If NULL, a zero matrix is used.

weight

Numeric vector of indicator weights. Must have length equal to ncol(data). If NULL, equal weights are used.

method

Coupling model. One of "standard", "wang", or "fan".

threads

Number of threads used in computation.

Value

A data.frame with:

  • Intra_C: intra-system coupling degree

  • Intra_D: intra-system coordination degree

  • Peri_C: peri-coupling degree

  • Peri_D: peri coordination degree

  • Tele_C: tele-coupling degree

  • Tele_D: tele coordination degree

Details

Full model definitions and formulas are available at: https://github.com/stscl/coupling/discussions/8

Note

Input values should be normalized to [0, 1]. Spatial weight matrices are typically symmetric.

Examples

set.seed(42)
mat = matrix(runif(20), nrow = 5)
swm1 = apply(matrix(runif(25), 5, 5), 1, \(.x) .x / sum(.x))
swm2 = apply(matrix(runif(25), 5, 5), 1, \(.x) .x / sum(.x))
coupling::metacoupling(mat, swm1, swm2)
#>     Intra_C   Intra_D    Peri_C    Peri_D    Tele_C    Tele_D
#> 1 0.9497342 0.8199577 0.9090374 0.7621545 1.3099835 1.1089423
#> 2 0.9905121 0.9136491 1.1690717 1.0278882 0.5735267 0.5064461
#> 3 0.6926110 0.5050229 0.7869004 0.6461619 0.6749018 0.5429212
#> 4 0.9148146 0.7122036 0.8078109 0.6369757 1.2478349 0.9992342
#> 5 0.9877654 0.7649259 0.8117074 0.6539408 0.7006156 0.5491195