multispatialccm {tEDM} | R Documentation |
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,
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, |
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)