intersection cardinality

Usage

# S4 method for class 'data.frame'
ic(
  data,
  column,
  target,
  lib = NULL,
  pred = NULL,
  E = 2:10,
  tau = 1,
  k = E + 2,
  dist.metric = "L1",
  threads = length(pred),
  parallel.level = "low"
)

Arguments

data: observation data.
column: name of library variable.
target: name of target variable.
lib: (optional) libraries indices.
pred: (optional) predictions indices.
E: (optional) embedding dimensions.
tau: (optional) step of time lags.
k: (optional) number of nearest neighbors used in prediction.
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.

Value

A list

xmap: cross mapping performance
varname: name of target variable
method: method of cross mapping
tau: step of time lag

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)
ic(sim,"x","y",E = 4,k = 15:30,threads = 1)
#> The suggested E and k for variable y is 4 and 15