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cross mapping cardinality
# 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 )
A list
xmap
cross mapping results
cs
causal strength
varname
names of causal and effect variable
bidirectional
whether to examine bidirectional causality
observation data.
name of causal variable.
name of effect variable.
(optional) number of time points used.
(optional) embedding dimensions.
(optional) step of time lags.
(optional) number of nearest neighbors.
(optional) libraries indices.
(optional) predictions indices.
(optional) number of threads to use.
(optional) level of parallelism, low or high.
low
high
(optional) whether to examine bidirectional causality.
(optional) whether to show the progress bar.
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.
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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