multispatialccm: multispatial convergent cross mapping

In tEDM: Temporal Empirical Dynamic Modeling

multispatial convergent cross mapping

Description

multispatial convergent cross mapping

Usage

## S4 method for signature 'list'
multispatialccm(
  data,
  cause,
  effect,
  libsizes,
  E = 3,
  tau = 0,
  k = E + 1,
  boot = 99,
  seed = 42,
  dist.metric = "L1",
  dist.average = TRUE,
  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.
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.
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(cbind, lapply(sim, function(df) df$x)),
           y = do.call(cbind, lapply(sim, function(df) df$y)))
multispatialccm(lst,"x","y",libsizes = 5:15,E = c(7,2),k = 6,threads = 1)

tEDM documentation built on Aug. 25, 2025, 5:12 p.m.