cross mapping cardinality

Usage

# S4 method for class '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
)

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.

Value

A list

xmap: cross mapping results
cs: causal strength
varname: names of causal and effect variable
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)
#> 
Computing: [========================================] 100% (done)                         
#> 
Computing: [========================================] 100% (done)                         
#>   neighbors      x->y      y->x
#> 1        15 0.2533333 0.1266667