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,
  tau = 1,
  k = pmin(E^2),
  lib = NULL,
  pred = NULL,
  nb = NULL,
  threads = detectThreads(),
  parallel.level = "low",
  bidirectional = TRUE,
  detrend = FALSE,
  progressbar = TRUE
)

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

Arguments

data	observation data.
cause	name of causal variable.
effect	name of effect variable.
libsizes	(optional) number of spatial units used.
E	(optional) embedding dimensions.
tau	(optional) step of spatial lags.
k	(optional) number of nearest neighbors.
lib	(optional) libraries indices.
pred	(optional) predictions indices.
nb	(optional) neighbours list.
threads	(optional) number of threads to use.
parallel.level	(optional) level of parallelism, low or high.
bidirectional	(optional) whether to examine bidirectional causality.
detrend	(optional) whether to remove the linear trend.
progressbar	(optional) whether to show the progress bar.

Value

A list

xmap

cross mapping results

cs

causal strength

varname

names of causal and effect variable

bidirectional

whether to examine bidirectional causality

Examples

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

g = gcmc(columbus,"hoval","crime",E = 2,k = 25)
g

spEDM documentation built on June 25, 2025, 9:07 a.m.