multispatial convergent cross mapping

Usage

# S4 method for class 'list'
multispatialccm(
  data,
  cause,
  effect,
  libsizes,
  E = 3,
  tau = 0,
  k = E + 1,
  boot = 99,
  seed = 42,
  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.
boot: (optional) number of bootstraps to perform.
seed: (optional) random seed.
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

Clark, A.T., Ye, H., Isbell, F., Deyle, E.R., Cowles, J., Tilman, G.D., Sugihara, G., 2015. Spatial convergent cross mapping to detect causal relationships from short time series. Ecology 96, 1174–1181.

Examples

set.seed(42)
obs = runif(15,0,0.1)
sim = vector("list",15)
for (i in seq_along(obs)){
  sim[[i]] = logistic_map(x = obs[i],y = obs[i],step = 15,beta_xy = 0.5,beta_yx = 0)
}

lst = list(x = do.call(rbind, lapply(sim, function(df) df$x)),
           y = do.call(rbind, lapply(sim, function(df) df$y)))
multispatialccm(lst,"x","y",libsizes = seq(5,15,1),E = c(2,4),k = 5,threads = 1)
#> 
Computing: [========================================] 100% (done)                         
#> 
Computing: [========================================] 100% (done)                         
#>    libsizes      x->y      y->x
#> 1         5 0.3605495 0.3301142
#> 2         6 0.4306829 0.4012219
#> 3         7 0.4799676 0.4636211
#> 4         8 0.5138123 0.4956088
#> 5         9 0.5408698 0.5333870
#> 6        10 0.5747756 0.5742982
#> 7        11 0.5953998 0.5999543
#> 8        12 0.6362903 0.6425245
#> 9        13 0.6670359 0.6687122
#> 10       14 0.6982020 0.6980796
#> 11       15 0.7214717 0.7203842