calc_te function

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
)

Arguments

• x: a vector of numeric values, ordered by time. Also allowed are xts, zoo, or ts objects.
• y: a vector of numeric values, ordered by time. Also allowed are xts, zoo, or ts objects.
• lx: Markov order of x, i.e. the number of lagged values affecting the current value of x. Default is lx = 1.
• ly: Markov order of y, i.e. the number of lagged values affecting the current value of y. Default is ly = 1.
• q: a weighting parameter used to estimate Renyi transfer entropy, parameter is between 0 and 1. For q = 1, Renyi transfer entropy converges to Shannon transfer entropy. Default is q = 0.1.
• 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 entropy = 'Shannon'.
• shuffles: the number of shuffles used to calculate the effective transfer entropy. Default is shuffles = 100.
• type: specifies the type of discretization applied to the observed time series:'quantiles', 'bins' or 'limits'. Default is type = 'quantiles'.
• quantiles: specifies the quantiles of the empirical distribution of the respective time series used for discretization. Default is quantiles = c(5,95).
• bins: specifies the number of bins with equal width used for discretization. Default is bins = NULL.
• limits: specifies the limits on values used for discretization. Default is limits = NULL.
• burn: the number of observations that are dropped from the beginning of the bootstrapped Markov chain. Default is burn = 50.
• seed: a seed that seeds the PRNG (will internally just call set.seed), default is seed = NULL.
• na.rm: if missing values should be removed (will remove the values at the same point in the other series as well). Default is TRUE.

Returns

a single numerical value for the transfer entropy

Examples

# 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)

See Also

calc_ete andtransfer_entropy