transfer_entropy: Transfer Entropy

In rinform: An R Wrapper of the 'Inform' C Library for Information Analysis of Complex Systems

Description

Compute the local or average transfer entropy from one time series ys to another xs with target history length k conditioned on the background ws.

Usage

transfer_entropy(ys, xs, ws = NULL, k, local = FALSE)

Arguments

ys	Vector or matrix specifying one or more source time series.
xs	Vector or matrix specifying one or more destination time series.
ws	Vector or matrix specifying one or more background time series.
k	Integer giving the history length.
local	Boolean specifying whether to compute the local transfer entropy.

Value

Numeric giving the average transfer entropy or a vector giving the local transfer entropy.

Examples

######################################################################
# The typical usage is to provide the time series and the history length. 
xs <- c(0, 0, 1, 1, 1, 1, 0, 0, 0)
ys <- c(0, 1, 1, 1, 1, 0, 0, 0, 1)
transfer_entropy(ys, xs, k = 1)       # 0.8112781
transfer_entropy(ys, xs, k = 2)       # 0.6792696
transfer_entropy(xs, ys, k = 1)       # 0.2169172
transfer_entropy(xs, ys, k = 2)       # 0

# [1] 0.4150375, 2.0, 0.4150375, 0.4150375, 0.4150375, 2.0, 0.4150375,
#     0.4150375
transfer_entropy(ys, xs, k = 1, local = TRUE)

# [1] 1.0, 0.0, 0.5849625, 0.5849625, 1.5849625, 0.0, 1.0
transfer_entropy(ys, xs, k = 2, local = TRUE)

# [1] 0.4150375, 0.4150375, -0.169925, -0.169925, 0.4150375, 1.0,
#     -0.5849625, 0.4150375
transfer_entropy(xs, ys, k = 1, local = TRUE)

# [1] 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
transfer_entropy(xs, ys, k = 2, local = TRUE)

# Multiple Initial Conditions
xs      <- matrix(0, nrow = 9, ncol = 2)
xs[, 1] <- c(0, 0, 1, 1, 1, 1, 0, 0, 0)
xs[, 2] <- c(1, 0, 0, 0, 0, 1, 1, 1, 0)
ys      <- matrix(0, nrow = 9, ncol = 2)
ys[, 1] <- c(1, 0, 0, 0, 0, 1, 1, 1, 1)
ys[, 2] <- c(1, 1, 1, 1, 0, 0, 0, 1, 1)
transfer_entropy(ys, xs, k = 1)       # 0.8828561
transfer_entropy(ys, xs, k = 2)       # 0.6935361
transfer_entropy(xs, ys, k = 1)       # 0.1596973
transfer_entropy(xs, ys, k = 2)       # 0.0
  
# [, 1] 0.4150375, 2.0, 0.67807191, 0.67807191, 0.67807191, 1.4150375,
#       0.4150375, 0.4150375
# [, 2] 1.4150375, 0.4150375, 0.4150375, 0.4150375, 2.0, 0.67807191,
#       0.67807191, 1.4150375
transfer_entropy(ys, xs, k = 1, local = TRUE)

# [, 1] 1.32192809, 0.0, 0.73696559, 0.73696559, 1.32192809, 0.0,
#       0.73696559
# [, 2] 0.0, 0.73696559, 0.73696559, 1.32192809, 0.0, 0.73696559,
#       1.32192809
transfer_entropy(ys, xs, k = 2, local = TRUE)

# [, 1] 0.5849625, 0.48542683, -0.25153877, -0.25153877, 0.48542683,
#       0.36257008, -0.22239242, -0.22239242
# [, 2] 0.36257008, -0.22239242, -0.22239242, 0.5849625, 0.48542683,
#       -0.25153877, 0.48542683, 0.36257008
transfer_entropy(xs, ys, k = 1, local = TRUE)

# [, 1] 0.000000e+00, -2.220446e-16, -2.220446e-16, -2.220446e-16,
#       0.000000e+00, -2.220446e-16, -2.220446e-16
# [, 2] -2.220446e-16, -2.220446e-16, -2.220446e-16, 0.000000e+00,
#       -2.220446e-16, -2.220446e-16, 0.000000e+00
transfer_entropy(xs, ys, k = 2, local = TRUE)

# With a background process
xs <- c(0, 1, 1, 1, 1, 0, 0, 0, 0)
ys <- c(0, 0, 1, 1, 1, 1, 0, 0, 0)
ws <- matrix(c(1, 0, 1, 0, 1, 1, 1, 1, 1,
               1, 1, 0, 1, 0, 1, 1, 1, 1), ncol = 2)
transfer_entropy(xs, ys, ws, k = 2) # 0

# [, 1] 0, 0, 0, 0, 0, 0, 0, 0, 0 
te <- transfer_entropy(xs, ys, ws, k = 2, local = TRUE)
t(te)

rinform documentation built on April 1, 2018, 12:12 p.m.