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tEDM (version 1.0)

cmc: cross mapping cardinality

Description

cross mapping cardinality

Usage

# S4 method for data.frame
cmc(
  data,
  cause,
  effect,
  libsizes = NULL,
  E = 3,
  tau = 0,
  k = pmin(E^2),
  lib = NULL,
  pred = NULL,
  threads = length(libsizes),
  parallel.level = "low",
  bidirectional = TRUE,
  progressbar = TRUE
)

Value

A list

xmap

cross mapping results

cs

causal strength

varname

names of causal and effect variable

bidirectional

whether to examine bidirectional causality

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.

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.

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

Run this code
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)

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