calc_te | R Documentation |
Calculates the Transfer Entropy for two time series
calc_te( x, y, lx = 1, ly = 1, q = 0.1, entropy = "Shannon", shuffles = 100, type = "quantiles", quantiles = c(5, 95), bins = NULL, limits = NULL, burn = 50, seed = NULL, na.rm = TRUE )
x |
a vector of numeric values, ordered by time.
Also allowed are |
y |
a vector of numeric values, ordered by time.
Also allowed are |
lx |
Markov order of x, i.e. the number of lagged values affecting the
current value of x. Default is |
ly |
Markov order of y, i.e. the number of lagged values affecting the
current value of y. Default is |
q |
a weighting parameter used to estimate Renyi transfer entropy,
parameter is between 0 and 1. For |
entropy |
specifies the transfer entropy measure that is estimated,
either 'Shannon' or 'Renyi'. The first character can be used
to specify the type of transfer entropy as well. Default is
|
shuffles |
the number of shuffles used to calculate the effective
transfer entropy. Default is |
type |
specifies the type of discretization applied to the observed time
series:'quantiles', 'bins' or 'limits'. Default is
|
quantiles |
specifies the quantiles of the empirical distribution of the
respective time series used for discretization.
Default is |
bins |
specifies the number of bins with equal width used for
discretization. Default is |
limits |
specifies the limits on values used for discretization.
Default is |
burn |
the number of observations that are dropped from the beginning of
the bootstrapped Markov chain. Default is |
seed |
a seed that seeds the PRNG (will internally just call set.seed),
default is |
na.rm |
if missing values should be removed (will remove the values at
the same point in the other series as well). Default is |
a single numerical value for the transfer entropy
calc_ete
andtransfer_entropy
# construct two time-series set.seed(1234567890) n <- 1000 x <- rep(0, n + 1) y <- rep(0, n + 1) for (i in seq(n)) { x[i + 1] <- 0.2 * x[i] + rnorm(1, 0, 2) y[i + 1] <- x[i] + rnorm(1, 0, 2) } x <- x[-1] y <- y[-1] # calculate the X->Y transfer entropy value calc_te(x, y) # calculate the Y->X transfer entropy value calc_te(y, x) # Compare the results calc_te(x, y, seed = 123) calc_te(y, x, seed = 123) transfer_entropy(x, y, nboot = 0, seed = 123)
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