convergent cross mapping

Usage

# S4 method for class 'data.frame'
ccm(
  data,
  cause,
  effect,
  libsizes,
  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
)

Arguments

data: observation data.
cause: name of causal variable.
effect: name of effect variable.
libsizes: number of time points used in prediction.
E: (optional) embedding dimensions.
tau: (optional) step of time lags.
k: (optional) number of nearest neighbors used in prediction.
theta: (optional) weighting parameter for distances, useful when algorithm is smap.
algorithm: (optional) prediction algorithm.
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
varname: names of causal and effect variable
bidirectional: whether to examine bidirectional causality

References

Sugihara, G., May, R., Ye, H., Hsieh, C., Deyle, E., Fogarty, M., Munch, S., 2012. Detecting Causality in Complex Ecosystems. Science 338, 496–500.

Examples

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)
#> 
Computing: [========================================] 100% (done)                         
#> 
Computing: [========================================] 100% (done)                         
#>   libsizes      x->y      y->x
#> 1        5 0.6658725 0.4459743
#> 2       10 0.7903164 0.5440701
#> 3       15 0.8073800 0.5425474
#> 4       20 0.8056960 0.5364679
#> 5       25 0.8020987 0.5307466
#> 6       30 0.7983809 0.5253733
#> 7       35 0.7965829 0.5258838