---
title: "codec"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{codec}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

### Dependence and Conditional Dependence
For random variable $Y$ and random vectors $Z$ and $X$, $T(Y, Z\mid X)\in[0, 1]$, conditional dependence coefficient, gives a measure of dependence of $Y$ on $Z$ given $X$. $T(Y, Z\mid X)$ is zero if and only if $Y$ is independent of $Z$ given $X$ and is 1 if and only if $Y$ is a function of $Z$ given $X$. This measure is well-defined if $Y$ is not almost surely a function of $X$. For more details on the definition of $T$ and its properties, and its estimator see the paper [*A Simple Measure Of Conditional Dependence*](https://arxiv.org/pdf/1910.12327.pdf).


Given a sample of $n$ i.i.d observations of triple $(X, Y, Z)$, we can estimate $T(Y, Z\mid X)$ efficiently in a non-parametric fashion. Function `codec` estimates this value. The default value for $X$ is `NULL` and if is not provided by the user, `codec` gives the estimate of the dependence measure of $Y$ on $Z$, $T(Y, Z)$. 

In the following examples, we illustrate the behavior of this estimator is different settings. 
```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
knitr::opts_chunk$set(fig.width=7, fig.height=5) 
```

```{r setup, results='hide', message=FALSE, warning=FALSE}
library(FOCI)
```

In this example we have generated a $10000 \times 3$ matrix $x$, with i.i.d elements from $unif[0, 1]$. The observed value of $y$ is the sum of the elements of each row of $x$ mod $1$. Although $y$ is a function of $x$, it can be seen that it is independent of each of the single columns of $x$ or each pair of its columns. On the other hand conditional on the last column, $y$ is a function of the first two columns but it is still independent of any of the first two columns separately. 
```{r}
n = 10000
p = 3
x = matrix(runif(n * p), ncol = p)
y = (x[, 1] + x[, 2] + x[, 3]) %% 1
# y is independent of each of column of x 
codec(y, x[, 1])
codec(y, x[, 2])
codec(y, x[, 3])

# y is independent of the first two columns of x, x[, c(1, 2)]
codec(y, x[, c(1, 2)])

# y is a function of x
codec(y, x)

# conditional on the last column of x, y is a function of the first two columns
codec(y, x[, c(1, 2)], x[, 3])
# conditional on x[, 3], y is independent of x[, 1]
codec(y, x[, 1], x[, 3])

```

In the following example we have generated a $10000 \times 2$ matrix $x$, with i.i.d normal standard elements. Each row of this matrix represent a point in the 2-dimensional plane. We call the square distance of this point from the origin $y$ and its angle with the horizontal axis, $z$. It can be seen that $y$ and $z$ are independent of each other, but conditional on any of the coordinates of the given point $y$ can be fully determind using $z$.
```{r}
n = 1000
p = 2
x = matrix(rnorm(n * p), ncol = p)
y = x[, 1]^2 + x[, 2]^2
z = atan(x[, 1] / x[, 2])
# y is independent of z
codec(y, z)
# conditional on x[, 1], y is a function of z
codec(y, z, x[, 1])
```
