cmc {tEDM} | R Documentation |
cross mapping cardinality
Description
cross mapping cardinality
Usage
## S4 method for signature '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, |
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)