geographical cross mapping cardinality

Usage

# S4 method for class '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 class '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"))
# \donttest{
g = gcmc(columbus,"hoval","crime",E = 2,k = 25)
#> 
Computing: [========================================] 100% (done)                         
#> 
Computing: [========================================] 100% (done)                         
g
#>   neighbors hoval->crime crime->hoval
#> 1        25        0.092         0.14
# }