spEDM (version 1.9)

ic: optimal parameter search for intersection cardinality

Description

optimal parameter search for intersection cardinality

Usage

# S4 method for 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 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
)

Value

A list

xmap

cross mapping performance

varname

name of target variable

method

method of cross mapping

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).

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)
# }