gcmc: geographical cross mapping cardinality

In spEDM: Spatial Empirical Dynamic Modeling

geographical cross mapping cardinality

Description

geographical cross mapping cardinality

Usage

## S4 method for signature 'sf'
gcmc(
  data,
  cause,
  effect,
  libsizes = NULL,
  E = 3,
  k = min(E^2),
  tau = 1,
  style = 1,
  lib = NULL,
  pred = NULL,
  dist.metric = "L2",
  threads = detectThreads(),
  detrend = FALSE,
  parallel.level = "low",
  bidirectional = TRUE,
  progressbar = TRUE,
  nb = NULL
)

## S4 method for signature 'SpatRaster'
gcmc(
  data,
  cause,
  effect,
  libsizes = NULL,
  E = 3,
  k = min(E^2),
  tau = 1,
  style = 1,
  lib = NULL,
  pred = NULL,
  dist.metric = "L2",
  threads = detectThreads(),
  detrend = FALSE,
  parallel.level = "low",
  bidirectional = TRUE,
  progressbar = TRUE,
  grid.coord = TRUE,
  embed.direction = 0
)

Arguments

data	observation data.
cause	name of causal variable.
effect	name of effect variable.
libsizes	(optional) number of spatial units used (input needed: vector - spatial vector, matrix - spatial raster).
E	(optional) embedding dimensions.
k	(optional) number of nearest neighbors.
tau	(optional) step of spatial lags.
style	(optional) embedding style (0 includes current state, 1 excludes it).
lib	(optional) libraries indices (input requirement same as libsizes).
pred	(optional) predictions indices (input requirement same as libsizes).
dist.metric	(optional) distance metric (L1: Manhattan, L2: Euclidean).
threads	(optional) number of threads to use.
detrend	(optional) whether to remove the linear trend.
parallel.level	(optional) level of parallelism, low or high.
bidirectional	(optional) whether to examine bidirectional causality.
progressbar	(optional) whether to show the progress bar.
nb	(optional) neighbours list.
grid.coord	(optional) whether to detrend using cell center coordinates (TRUE) or row/column numbers (FALSE).
embed.direction	(optional) direction selector for embeddings (0 returns all directions, 1-8 correspond to NW, N, NE, W, E, SW, S, SE).

Value

A list

xmap

cross mapping results

cs

causal strength

varname

names of causal and effect variables

bidirectional

whether to examine bidirectional causality

References

Lyu, W., Dai, S., Song, Y., Zhao, W., Yi, W., Xiao, Y., Jia, N., 2026. Measuring causal strengths from spatial cross-sectional data with geographical cross mapping cardinality. International Journal of Geographical Information Science 1–23.

Examples

columbus = sf::read_sf(system.file("case/columbus.gpkg",package="spEDM"))

g = spEDM::gcmc(columbus,"hoval","crime",E = 7,k = 19)
g

spEDM documentation built on Sept. 9, 2026, 5:07 p.m.