```
library(ggplot2)
library(manipulate)
library(firatheme) # https://github.com/vankesteren/firatheme
library(Massign) # https://github.com/vankesteren/Massign
```

A while ago, I blogged that a matrix \(A\) can be seen as an operation and that the determinant of this matrix \(|A|\) says something about the volume of the transformation. This blog is about another property of matrices: the characteristic function.

The characteristic function of a square matrix \(A\) of order \(n\) is defined as follows:

\[p_A(\lambda) = | \lambda I_n - A |\]

Letâ€™s look at this function for the following \(2\times 2\) matrix:

\[A = \begin{bmatrix} 3 & 1 \\ 2 & 4 \end{bmatrix}\] \[ \begin{align} p_A(\lambda) &= | \lambda I_n - A | \\ &= \left| \begin{bmatrix} \lambda & 0 \\ 0 & \lambda \end{bmatrix} - \begin{bmatrix} 3 & 1 \\ 2 & 4 \end{bmatrix} \right| \\ &= \left| \begin{bmatrix} \lambda - 3 & -1 \\ -2 & \lambda - 4 \end{bmatrix} \right|\\ \end{align}\]

The roots of this function - a polynomial of order \(n\) - are the eigenvalues of the matrix. We can find them using some algebra, remembering that the determinant of a \(2\times 2\) matrix is calculated as \(ad-bc\):

\[ \begin{align} p_A(\lambda) &= (\lambda - 3)(\lambda - 4) + 2\\ &= \lambda^2 - 7\lambda + 10 \\ &= (\lambda - 2)(\lambda - 5) \end{align}\]

So \(\lambda = 2\) or \(\lambda = 5\). These are the two eigenvalues of this matrix.

Using the power of `R`

, we can get a better intuition for the characteristic function by visualising it. Below the code for a function that takes a matrix and visualises this function.

```
charfun <- function(mat, from, to) {
n <- ncol(mat)
stopifnot(n == nrow(mat))
x <- seq(from, to, length.out = 3000)
ggdat <- data.frame(
x = x,
y = vapply(x, function(lambda) det(lambda*diag(n) - mat), 1.0)
)
ev <- eigen(mat)$values
ev <- ev[ev <= to & ev > from]
evdat <- data.frame(x = ev, y = rep(0, length(ev)))
ggplot(ggdat, aes(x = x, y = y)) +
geom_hline(yintercept = 0, col = firaCols[5], lwd = 1) +
geom_vline(xintercept = ev, col = firaCols[2], lwd = 1, lty = 2) +
geom_line(col = firaCols[1], lwd = 1) +
geom_point(aes(x, y), evdat, size = 3, col = firaCols[1]) +
labs(x = "Lambda",
y = "Characteristic function value",
title = "Characteristic function of a matrix") +
theme_fira()
}
A %<-% "3, 1
2, 4"
charfun(A, 0, 7) + ggtitle("Characteristic function of A")
```

- The characteristic function crosses the axis at 2 and 5, just as we expected
- The characteristic function is indeed a quadratic function, i.e., a polynomial of order \(n = 2\).

You can play around with this function in `R`

by trying out different matrices. Try out a covariance matrix, different kinds of symmetric and assymmetric matrices!

After running the above `R`

chunks, you can run the following to play around with different covariance matrices of the following form:

\[A = \begin{bmatrix} 1 & a & b \\ a & 1 & c \\ b & c & 1 \end{bmatrix}\] Play around with it to see what happens to the eigenvalues of this matrix! For example, note that when a, b, c, and d are all 0 the eigenvalues are all 1. There are some nice symmetries to be explored here.

```
manipulate(
{
A %<-% " 1,
a, 1
b, c, 1"
charfun(A, 0, 2) + ylim(c(-.7,.7))
},
a = slider(-1, 1, initial = .5, step = 0.1),
b = slider(-1, 1, initial = .3, step = 0.1),
c = slider(-1, 1, initial = .2, step = 0.1)
)
```