ic: intersection cardinality

In tEDM: Temporal Empirical Dynamic Modeling

intersection cardinality

Description

intersection cardinality

Usage

## S4 method for signature 'data.frame'
ic(
  data,
  column,
  target,
  lib = NULL,
  pred = NULL,
  E = 2:10,
  tau = 1,
  k = E + 2,
  dist.metric = "L1",
  threads = length(pred),
  parallel.level = "low"
)

Arguments

data	observation data.
column	name of library variable.
target	name of target variable.
lib	(optional) libraries indices.
pred	(optional) predictions indices.
E	(optional) embedding dimensions.
tau	(optional) step of time lags.
k	(optional) number of nearest neighbors used in prediction.
dist.metric	(optional) distance metric (L1: Manhattan, L2: Euclidean).
threads	(optional) number of threads to use.
parallel.level	(optional) level of parallelism, low or high.

Value

A list

xmap

cross mapping performance

varname

name of target variable

method

method of cross mapping

tau

step of time lag

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

sim = logistic_map(x = 0.4,y = 0.4,step = 45,beta_xy = 0.5,beta_yx = 0)
ic(sim,"x","y",E = 4,k = 15:30,threads = 1)

tEDM documentation built on Aug. 25, 2025, 5:12 p.m.