geographical cross mapping cardinality

Usage

# S4 method for class '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 class '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
)

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).

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"))
# \donttest{
g = spEDM::gcmc(columbus,"hoval","crime",E = 7,k = 19)
#> 
Computing: [========================================] 100% (done)                         
#> 
Computing: [========================================] 100% (done)                         
g
#>   neighbors hoval->crime crime->hoval
#> 1        19    0.1634349    0.2770083
# }