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tEDM (version 1.0)

ccm: convergent cross mapping

Description

convergent cross mapping

Usage

# S4 method for data.frame
ccm(
  data,
  cause,
  effect,
  libsizes = NULL,
  E = 3,
  tau = 0,
  k = E + 1,
  theta = 1,
  algorithm = "simplex",
  lib = NULL,
  pred = NULL,
  threads = length(libsizes),
  parallel.level = "low",
  bidirectional = TRUE,
  progressbar = TRUE
)

Value

A list

xmap

cross mapping results

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.

theta

(optional) weighting parameter for distances, useful when algorithm is smap.

algorithm

(optional) prediction algorithm.

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

Sugihara, G., May, R., Ye, H., Hsieh, C., Deyle, E., Fogarty, M., Munch, S., 2012. Detecting Causality in Complex Ecosystems. Science 338, 496–500.

Examples

Run this code
sim = logistic_map(x = 0.4,y = 0.4,step = 45,beta_xy = 0.5,beta_yx = 0)
ccm(sim,"x","y",libsizes = seq(5,35,5),E = 8,k = 7,threads = 1)

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