pcm | R Documentation |
partial cross mapping
## S4 method for signature '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
)
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 |
(optional) prediction algorithm. |
lib |
(optional) libraries indices. |
pred |
(optional) predictions indices. |
dist.metric |
(optional) distance metric ( |
dist.average |
(optional) whether to average distance. |
threads |
(optional) number of threads to use. |
parallel.level |
(optional) level of parallelism, |
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. |
A list
pxmap
partial cross mapping results
xmap
cross mapping results
varname
names of causal and effect variable
bidirectional
whether to examine bidirectional causality
Leng, S., Ma, H., Kurths, J. et al. Partial cross mapping eliminates indirect causal influences. Nat Commun 11, 2632 (2020).
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)
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