cmc: cross mapping cardinality

In tEDM: Temporal Empirical Dynamic Modeling

cross mapping cardinality

Description

cross mapping cardinality

Usage

## S4 method for signature 'data.frame'
cmc(
  data,
  cause,
  effect,
  libsizes = NULL,
  E = 3,
  tau = 1,
  k = pmin(E^2),
  lib = NULL,
  pred = NULL,
  dist.metric = "L1",
  threads = length(pred),
  parallel.level = "low",
  bidirectional = TRUE,
  progressbar = TRUE
)

Arguments

data	observation data.
cause	name of causal variable.
effect	name of effect variable.
libsizes	(optional) number of time points used.
E	(optional) embedding dimensions.
tau	(optional) step of time lags.
k	(optional) number of nearest neighbors.
lib	(optional) libraries indices.
pred	(optional) predictions indices.
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.
bidirectional	(optional) whether to examine bidirectional causality.
progressbar	(optional) whether to show the progress bar.

Value

A list

xmap

cross mapping results

cs

causal strength

varname

names of causal and effect variables

bidirectional

whether to examine bidirectional causality

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)
cmc(sim,"x","y",E = 4,k = 15,threads = 1)

tEDM documentation built on March 30, 2026, 5:07 p.m.