partial cross mapping

Usage

# S4 method for class 'data.frame'
pcm(
  data,
  cause,
  effect,
  conds,
  libsizes = NULL,
  E = 3,
  tau = 0,
  k = E + 1,
  theta = 1,
  algorithm = "simplex",
  lib = NULL,
  pred = NULL,
  dist.metric = "L1",
  dist.average = TRUE,
  threads = length(pred),
  parallel.level = "low",
  bidirectional = TRUE,
  cumulate = FALSE,
  progressbar = TRUE
)

Arguments

data: observation data.
cause: name of causal variable.
effect: name of effect variable.
conds: name of conditioning variables.
libsizes: (optional) number of time points used.
E: (optional) embedding dimensions.
tau: (optional) step of time lags.
k: (optional) number of nearest neighbors.
theta: (optional) weighting parameter for distances, useful when algorithm is smap.
algorithm: (optional) prediction algorithm.
lib: (optional) libraries indices.
pred: (optional) predictions indices.
dist.metric: (optional) distance metric (L1: Manhattan, L2: Euclidean).
dist.average: (optional) whether to average distance.
threads: (optional) number of threads to use.
parallel.level: (optional) level of parallelism, low or high.
bidirectional: (optional) whether to examine bidirectional causality.
cumulate: (optional) serial or cumulative computation of partial cross mapping.
progressbar: (optional) whether to show the progress bar.

Value

A list

pxmap: partial cross mapping results
xmap: cross mapping results
varname: names of causal and effect variable
bidirectional: whether to examine bidirectional causality

References

Leng, S., Ma, H., Kurths, J. et al. Partial cross mapping eliminates indirect causal influences. Nat Commun 11, 2632 (2020).

Examples

sim = logistic_map(x = 0.4,y = 0.4,z = 0.4,step = 45,
                   beta_xy = 0.5, beta_xz = 0,
                   beta_yx = 0, beta_yz = 0.5,
                   beta_zx = 0, beta_zy = 0)
pcm(sim,"x","z","y",libsizes = seq(5,45,5),E = 10,k = 7,threads = 1)
#> 
Computing: [========================================] 100% (done)                         
#> 
Computing: [========================================] 100% (done)                         
#> -------------------------------------- 
#> ***partial cross mapping prediction*** 
#> -------------------------------------- 
#>   libsizes        x->z       z->x
#> 1        5 0.410387689 0.11654973
#> 2       10 0.316685463 0.04464733
#> 3       15 0.256396689 0.02586971
#> 4       20 0.250855810 0.07990501
#> 5       25 0.250303599 0.09410492
#> 6       27 0.007456209 0.15394446
#> 
#> ------------------------------ 
#> ***cross mapping prediction*** 
#> ------------------------------ 
#>   libsizes      x->z      z->x
#> 1        5 0.8104853 0.6090928
#> 2       10 0.8695076 0.6226428
#> 3       15 0.8802282 0.6245086
#> 4       20 0.8808134 0.6285323
#> 5       25 0.8801786 0.6288845
#> 6       27 0.9314565 0.6174386