RTransferEntropy

Code to implement transfer entropy (Shannon and Renyi)

https://github.com/bzpaper/rtransferentropy

Science Score: 33.0%

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Repository

Code to implement transfer entropy (Shannon and Renyi)

Basic Info

Host: GitHub
Owner: BZPaper
License: gpl-3.0
Language: R
Default Branch: master
Homepage:
Size: 2.05 MB

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Created over 8 years ago · Last pushed over 3 years ago

Metadata Files

Readme License

README.Rmd

---
output: github_document
---



[![R-CMD-check](https://github.com/BZPaper/RTransferEntropy/workflows/R-CMD-check/badge.svg)](https://github.com/BZPaper/RTransferEntropy/actions)
[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/RTransferEntropy)](https://cran.r-project.org/package=RTransferEntropy)
[![](https://cranlogs.r-pkg.org/badges/RTransferEntropy)](https://cran.r-project.org/package=RTransferEntropy)
[![](https://img.shields.io/badge/doi-10.1016/j.softx.2019.100265-yellow.svg)](https://doi.org/10.1016/j.softx.2019.100265)



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-"
)
```

# RTransferEntropy

The goal of `RTransferEntropy` is to implement the calculation of the transfer entropy metric using Shannon's or the Renyi's methodology.

A short introduction can be found below, for a more thorough introduction to the transfer entropy methodology and the `RTransferEntropy` package, see the [vignette](https://cran.r-project.org/package=RTransferEntropy/vignettes/transfer-entropy.html) and the [`RTransferEntropy` paper](https://www.sciencedirect.com/science/article/pii/S2352711019300779).
If you use the package in academic work, please make sure to cite us, see also `citation("RTransferEntropy")`.

The authors of the [`TransferEntropy`](https://CRAN.R-project.org/package=TransferEntropy) package no longer develop their package, which is deprecated as of 2018-08-10, and recommend the use of this package.

## Installation

You can install `RTransferEntropy` with
```{r, eval = FALSE}
install.packages("RTransferEntropy")
```

or the development version from github with

```{r gh_installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("BZPaper/RTransferEntropy")
```

## Example using simulated data

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.

### Simulating a Time-Series
```{r}
library(RTransferEntropy)
library(future)
# enable parallel processing
plan(multisession)

set.seed(20180108)
n <- 2000
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]
```


### Visualisation
```{r contemp_plot, message=FALSE, warning=FALSE}
library(ggplot2)
library(gridExtra)
theme_set(theme_light())

# Lagged X-Plot
p1 <- ggplot(data.frame(x = c(NA, x[1:(length(x) - 1)]), y = y), aes(x, y)) +
  geom_smooth() +
  geom_point(alpha = 0.5, size = 0.5) +
  labs(x = expression(X[t - 1]), y = expression(Y[t])) +
  coord_fixed(1) +
  scale_x_continuous(limits = range(x)) +
  scale_y_continuous(limits = range(y))

# X-Y Plot
p2 <- ggplot(data.frame(x = x, y = y), aes(x, y)) +
  geom_smooth() +
  geom_point(alpha = 0.5, size = 0.5) +
  labs(x = expression(X[t]), y = expression(Y[t])) +
  coord_fixed(1) +
  scale_x_continuous(limits = range(x)) +
  scale_y_continuous(limits = range(y))

# Lagged Y Plot
p3 <- ggplot(data.frame(x = x, y = c(NA, y[1:(length(y) - 1)])), aes(x, y)) +
  geom_smooth() +
  geom_point(alpha = 0.5, size = 0.5) +
  labs(x = expression(X[t]), y = expression(Y[t - 1])) +
  coord_fixed(1) +
  scale_x_continuous(limits = range(x)) +
  scale_y_continuous(limits = range(y))

a <- grid.arrange(p1, p2, p3, ncol = 3)
```

### Shannon Transfer Entropy
```{r}
set.seed(20180108 + 1)

shannon_te <- transfer_entropy(x = x, y = y)

shannon_te

summary(shannon_te)
```

Alternatively, you can calculate only the transfer entropy or the effective transfer entropy with
```{r}
calc_te(x, y)
calc_te(y, x)

calc_ete(x, y)
calc_ete(y, x)
```


### Renyi Transfer Entropy
```{r}
set.seed(20180108 + 1)

renyi_te <- transfer_entropy(x = x, y = y, entropy = "renyi", q = 0.5)

renyi_te

calc_te(x, y, entropy = "renyi", q = 0.5)
calc_te(y, x, entropy = "renyi", q = 0.5)

calc_ete(x, y, entropy = "renyi", q = 0.5)
calc_ete(y, x, entropy = "renyi", q = 0.5)
```

# Function Verbosity aka `quiet = TRUE`

To disable the verbosity of a function you can use the argument `quiet`. Note that we have set `nboot = 0` as we don't need bootstrapped quantiles for this example.
```{r}
te <- transfer_entropy(x, y, nboot = 0, quiet = T)

te
```

If you want to disable feedback from `transfer_entropy` functions, you can do so by using `set_quiet(TRUE)`
```{r}
set_quiet(TRUE)
te <- transfer_entropy(x, y, nboot = 0)

te

# revert back with
set_quiet(FALSE)

te <- transfer_entropy(x, y, nboot = 0)
```

# Parallel Programming

Using the `future` package and its `plan`s we can execute all computations in parallel like so
```{r, warning=F}
library(future)
plan(multisession)
te <- transfer_entropy(x, y, nboot = 100)

# revert to sequential mode
plan(sequential)
te <- transfer_entropy(x, y, nboot = 100)

# close multisession, see also ?plan
plan(sequential)
```

Owner

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Login: BZPaper
Kind: organization

Repositories: 1
Profile: https://github.com/BZPaper

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cran.r-project.org: RTransferEntropy

Measuring Information Flow Between Time Series with Shannon and Renyi Transfer Entropy

Homepage: https://github.com/BZPaper/RTransferEntropy
Documentation: http://cran.r-project.org/web/packages/RTransferEntropy/RTransferEntropy.pdf
License: GPL-3
Latest release: 0.2.21
published over 3 years ago

Versions: 7
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Dependencies

DESCRIPTION cran

R >= 3.1.2 depends
Rcpp * imports
future >= 1.19.0 imports
future.apply * imports
data.table * suggests
ggplot2 * suggests
gridExtra * suggests
knitr * suggests
quantmod * suggests
rmarkdown * suggests
testthat * suggests
vars * suggests
xts * suggests
zoo * suggests

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RTransferEntropy

Science Score: 33.0%

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