geographical cross mapping cardinality

Usage

# S4 method for class 'sf'
gcmc(
  data,
  cause,
  effect,
  E = 3,
  tau = 1,
  k = NULL,
  r = 0,
  lib = NULL,
  pred = NULL,
  nb = NULL,
  threads = detectThreads(),
  bidirectional = TRUE,
  trend.rm = TRUE,
  progressbar = TRUE
)

# S4 method for class 'SpatRaster'
gcmc(
  data,
  cause,
  effect,
  E = 3,
  tau = 1,
  k = NULL,
  r = 0,
  lib = NULL,
  pred = NULL,
  threads = detectThreads(),
  bidirectional = TRUE,
  trend.rm = TRUE,
  progressbar = TRUE
)

Arguments

data: The observation data.
cause: Name of causal variable.
effect: Name of effect variable.
E: (optional) Dimensions of the embedding.
tau: (optional) Step of spatial lags.
k: (optional) Number of nearest neighbors used in intersection.
r: (optional) Number of excluded neighbors in intersection.
lib: (optional) Libraries indices.
pred: (optional) Predictions indices.
nb: (optional) The neighbours list.
threads: (optional) Number of threads.
bidirectional: (optional) whether to examine bidirectional causality.
trend.rm: (optional) Whether to remove the linear trend.
progressbar: (optional) whether to show the progress bar.

Value

A list

xmap: cross mapping results
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"))
# \donttest{
g = gcmc(columbus,"hoval","crime",E = 5)
#> 
Computing: [========================================] 100% (done)                         
#> 
Computing: [========================================] 100% (done)                         
g
#>   neighbors hoval->crime crime->hoval
#> 1        17      0.16609    0.2041522
# }