optimal parameter search for intersection cardinality

Usage

# S4 method for class 'sf'
ic(
  data,
  column,
  target,
  E = 2:10,
  k = E + 2,
  tau = 1,
  style = 1,
  lib = NULL,
  pred = NULL,
  dist.metric = "L2",
  threads = detectThreads(),
  detrend = FALSE,
  nb = NULL
)

# S4 method for class 'SpatRaster'
ic(
  data,
  column,
  target,
  E = 2:10,
  k = E + 2,
  tau = 1,
  style = 1,
  lib = NULL,
  pred = NULL,
  dist.metric = "L2",
  threads = detectThreads(),
  detrend = FALSE,
  grid.coord = TRUE
)

Arguments

data: observation data.
column: name of library variable.
target: name of target variable.
E: (optional) embedding dimensions.
k: (optional) number of nearest neighbors used.
tau: (optional) step of spatial lags.
style: (optional) embedding style (0 includes current state, 1 excludes it).
lib: (optional) libraries indices (input needed: vector - spatial vector, matrix - spatial raster).
pred: (optional) predictions indices (input requirement same as lib).
dist.metric: (optional) distance metric (L1: Manhattan, L2: Euclidean).
threads: (optional) number of threads to use.
detrend: (optional) whether to remove the linear trend.
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 performance
varname: name of target variable
method: method of cross mapping

References

Tao, P., Wang, Q., Shi, J., Hao, X., Liu, X., Min, B., Zhang, Y., Li, C., Cui, H., Chen, L., 2023. Detecting dynamical causality by intersection cardinal concavity. Fundamental Research.

Examples

columbus = sf::read_sf(system.file("case/columbus.gpkg",package="spEDM"))
# \donttest{
ic(columbus,"hoval","crime",E = 7,k = 15:25)
#> The suggested E,k,tau for variable crime is 7, 18 and 1 
# }