The goal of RTransferEntropy is to implement transfer entropy for the Shannon and the Renyi-methodology.
You can install RTransferEntropy from github with:
# install.packages("devtools")
devtools::install_github("BZPaper/Transfer-Entropy")Simulate a simple model to obtain two time series that are not independent (see simulation study in Dimpfl and Peter (2013)), i.e. one time series is lag of the other plus noise. In this case, one expects significant information flow from x to y and none from y to x.
library(RTransferEntropy)
set.seed(20180108)
n <- 100000
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]library(data.table)
library(ggplot2)
library(latex2exp)
theme_set(theme_light())
df <- rbindlist(list(
data.table(x, y, grp = "Contemporaneous Effect"),
data.table(shift(x, 1), y, grp = "Time-Delayed Effect")
))
ggplot(df, aes(x = x, y = y, color = grp)) +
geom_point(alpha = 0.1) +
geom_smooth() +
facet_wrap(~grp) +
labs(x = TeX("Left: x_t Right: x_{t-1}"), y = TeX("y_t"),
title = "Scatterplot of the Time-Series") +
scale_color_brewer(palette = "Set1", guide = F)
#> `geom_smooth()` using method = 'gam'
#> Warning: Removed 1 rows containing non-finite values (stat_smooth).
#> Warning: Removed 1 rows containing missing values (geom_point).
set.seed(20180108 + 1)
n_cores <- parallel::detectCores() - 1
shannon_te <- transfer_entropy(x = x,
lx = 1,
y = y,
ly = 1,
nboot = n_cores,
cl = n_cores)
#> Calculating Shannon's entropy on 7 cores with 6 shuffle(s) and 7 bootstrap(s)
#> The timeseries have length 100000 (0 NAs removed)
#> Calculate the x->y transfer entropy
#> Calculate the y->x transfer entropy
#> Bootstrap the transfer entropy
#> Done - Total time 74.4 seconds
shannon_te
#> Transfer Entropy Result:
#> Direction te ete sete p-value sign
#> ----------------------------------------------------------------
#> X->Y 0.09686 0.09679 NA NA
#> Y->X 0.00011 0.00004 NA NA
#> ----------------------------------------------------------------
#> Number of Observations: 100000
#> ----------------------------------------------------------------