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convergent cross mapping
# 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 )
A list
xmap
cross mapping results
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) weighting parameter for distances, useful when algorithm is smap.
algorithm
smap
(optional) prediction algorithm.
(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.
Sugihara, G., May, R., Ye, H., Hsieh, C., Deyle, E., Fogarty, M., Munch, S., 2012. Detecting Causality in Complex Ecosystems. Science 338, 496–500.
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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