calc_ete {RTransferEntropy}

Calculates the Effective Transfer Entropy for two time series

Description

Calculates the Effective Transfer Entropy for two time series

Usage

calc_ete(
  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.

Value

a single numerical value for the effective transfer entropy

See Also

calc_te andtransfer_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_ete(x, y)

# calculate the Y->X transfer entropy value
calc_ete(y, x)


  # Compare the results
  # even with the same seed, transfer_entropy might return slightly different
  # results from calc_ete
  calc_ete(x, y, seed = 123)
  calc_ete(y, x, seed = 123)
  transfer_entropy(x, y, nboot = 0, seed = 123)