spatial granger causality test

Usage

# S4 method for class 'sf'
sgc(
  data,
  cause,
  effect,
  k,
  block = 3,
  boot = 399,
  seed = 42,
  base = 2,
  lib = NULL,
  pred = NULL,
  nb = NULL,
  threads = detectThreads(),
  trend.rm = TRUE,
  normalize = FALSE,
  progressbar = FALSE
)

# S4 method for class 'SpatRaster'
sgc(
  data,
  cause,
  effect,
  k,
  block = 3,
  boot = 399,
  seed = 42,
  base = 2,
  lib = NULL,
  pred = NULL,
  threads = detectThreads(),
  trend.rm = TRUE,
  normalize = FALSE,
  progressbar = FALSE
)

Arguments

data: The observation data.
cause: Name of causal variable.
effect: Name of effect variable.
k: (optional) Number of nearest neighbors used in symbolization.
block: (optional) Number of blocks used in spatial block bootstrap.
boot: (optional) Number of bootstraps to perform.
seed: (optional) The random seed.
base: (optional) Base of the logarithm.
lib: (optional) Libraries indices.
pred: (optional) Predictions indices.
nb: (optional) The neighbours list.
threads: (optional) Number of threads.
trend.rm: (optional) Whether to remove the linear trend.
normalize: (optional) Whether to normalize the result.
progressbar: (optional) Whether to show the progress bar.

Value

A list

sc: statistic for spatial causality
varname: names of causal and effect variable

References

Herrera, M., Mur, J., & Ruiz, M. (2016). Detecting causal relationships between spatial processes. Papers in Regional Science, 95(3), 577–595.

Examples

columbus = sf::read_sf(system.file("case/columbus.gpkg", package="spEDM"))
# \donttest{
sgc(columbus,"hoval","crime", k = 15)
#> spatial granger causality test
#> hoval -> crime: statistic = 1.114, p value = 0.539
#> crime -> hoval: statistic = 1.555, p value = 0.005
# }