Obtain vertices of the ellipse on an ellipse covar

Question

By following this post one can draw an ellipse with a given shape matrix (A):

library(car)
A <- matrix(c(20.43, -8.59,-8.59, 24.03), nrow = 2)
ellipse(c(-0.05, 0.09), shape=A, radius=1.44, col="red", lty=2, asp = 1)

Now how to get the major/minor (pair of intersect points of the major/minor axis and the ellipse) vertices of this ellipse?

Answer 1

Still highly unsure this will really answer the question but here's my try:

first, define the center of the ellipse as a vector for later use:

center<-c(x=-0.05, y=0.09)

draw the ellipse and get the matrix of "points" with enought values to get a close enough to reality point:

tmp<-ellipse(c(-0.05, 0.09), shape=A, radius=1.44, segments=1e3, col="red", lty=2,add=FALSE)

Create a data.table with it and compute the distance of each point toward center (point_x - center_x)² + (point_y - center_y)²:

dt <- data.table(tmp)
dt[,dist:={dx=x-center[1];dy=y-center[2];dx*dx+dy*dy}]

Order the vertices by distance:

setorder(dt,dist)

Get the min and max points:

> tail(dt,2)
           x         y     dist
1:  4.990415 -6.138039 64.29517
2: -5.110415  6.318039 64.29517
> head(dt,2)
       x        y     dist
1:  4.045722  3.41267 27.89709
2: -4.165722 -3.23267 27.89709

Don't add too much segments or the two first values will be two points really close to each other instead of opposite.

with a visual results this sounds not so exact at end:

Answer 2

I know this question has been seen as solved, but actually there is a super elegant solution to this, in only a few lines as follow. Such computation is precise, without any sort of numerical optimization.

## target covariance matrix
A <- matrix(c(20.43, -8.59,-8.59, 24.03), nrow = 2)

E <- eigen(A, symmetric = TRUE)  ## symmetric eigen decomposition
U <- E[[2]]  ## eigen vectors, i.e., rotation matrix
D <- sqrt(E[[1]])  ## root eigen values, i.e., scaling factor

r <- 1.44  ## radius of original circle
Z <- rbind(c(r, 0), c(0, r), c(-r, 0), c(0, -r))  ## original vertices on major / minor axes
Z <- tcrossprod(Z * rep(D, each = 4), U)  ## transformed vertices on major / minor axes

#          [,1]      [,2]
#[1,] -5.055136  6.224212
#[2,] -4.099908 -3.329834
#[3,]  5.055136 -6.224212
#[4,]  4.099908  3.329834

C0 <- c(-0.05, 0.09)  ## new centre
Z <- Z + rep(C0, each = 4)  ## shift to new centre

#          [,1]      [,2]
#[1,] -5.105136  6.314212
#[2,] -4.149908 -3.239834
#[3,]  5.005136 -6.134212
#[4,]  4.049908  3.419834

In order to explain the mathematics behind, I am going to take 3 steps:

1. Where does this Ellipse come from?
2. Cholesky decomposition method and its drawback.
3. Eigen decomposition method and its natural interpretation.

---

Where does this ellipse comes from?

In practice, this ellipse can be obtained by some linear transformation to the unit circle x ^ 2 + y ^ 2 = 1.

---

Cholesky decomposition method and its drawback

## initial circle
r <- 1.44
theta <- seq(0, 2 * pi, by = 0.01 * pi)
X <- r * cbind(cos(theta), sin(theta))

## target covariance matrix
A <- matrix(c(20.43, -8.59,-8.59, 24.03), nrow = 2)

R <- chol(A)  ## Cholesky decomposition
X1 <- X %*% R  ## linear transformation

Z <- rbind(c(r, 0), c(0, r), c(-r, 0), c(0, -r))  ## original vertices on major / minor axes
Z1 <- Z %*% R  ## transformed coordinates

## different colour per quadrant
g <- floor(4 * (1:nrow(X) - 1) / nrow(X)) + 1

## draw ellipse
plot(X1, asp = 1, col = g)
points(Z1, cex = 1.5, pch = 21, bg = 5)

## draw circle
points(X, col = g, cex = 0.25)
points(Z, cex = 1.5, pch = 21, bg = 5)

## draw axes
abline(h = 0, lty = 3, col = "gray", lwd = 1.5)
abline(v = 0, lty = 3, col = "gray", lwd = 1.5)

We see that the linear transform matrix R does not appear to have natural interpretation. The original vertices of the circle do not map to vertices of the ellipse.

---

Eigen decomposition method and its natural interpretation

## initial circle
r <- 1.44
theta <- seq(0, 2 * pi, by = 0.01 * pi)
X <- r * cbind(cos(theta), sin(theta))

## target covariance matrix
A <- matrix(c(20.43, -8.59,-8.59, 24.03), nrow = 2)

E <- eigen(A, symmetric = TRUE)  ## symmetric eigen decomposition
U <- E[[2]]  ## eigen vectors, i.e., rotation matrix
D <- sqrt(E[[1]])  ## root eigen values, i.e., scaling factor

r <- 1.44  ## radius of original circle
Z <- rbind(c(r, 0), c(0, r), c(-r, 0), c(0, -r))  ## original vertices on major / minor axes

## step 1: re-scaling
X1 <- X * rep(D, each = nrow(X))  ## anisotropic expansion to get an axes-aligned ellipse
Z1 <- Z * rep(D, each = 4L)  ## vertices on axes

## step 2: rotation
Z2 <- tcrossprod(Z1, U)  ## rotated vertices on major / minor axes
X2 <- tcrossprod(X1, U)  ## rotated ellipse

## different colour per quadrant
g <- floor(4 * (1:nrow(X) - 1) / nrow(X)) + 1

## draw rotated ellipse and vertices
plot(X2, asp = 1, col = g)
points(Z2, cex = 1.5, pch = 21, bg = 5)

## draw axes-aligned ellipse and vertices
points(X1, col = g)
points(Z1, cex = 1.5, pch = 21, bg = 5)

## draw original circle
points(X, col = g, cex = 0.25)
points(Z, cex = 1.5, pch = 21, bg = 5)

## draw axes
abline(h = 0, lty = 3, col = "gray", lwd = 1.5)
abline(v = 0, lty = 3, col = "gray", lwd = 1.5)

## draw major / minor axes
segments(Z2[1,1], Z2[1,2], Z2[3,1], Z2[3,2], lty = 2, col = "gray", lwd = 1.5)
segments(Z2[2,1], Z2[2,2], Z2[4,1], Z2[4,2], lty = 2, col = "gray", lwd = 1.5)

Here we see that in both stages of the transform, vertices are still mapped to vertices. It is exactly based on such property we have the neat solution given at the very beginning.

Answer 3

For practical purposes, @Tensibai's answer is probably good enough. Just use a large enough value for the segments argument so that the points give a good approximation to the true vertices.

If you want something a bit more rigorous, you can solve for the location along the ellipse that maximises/minimises the distance from the center, parametrised by the angle. This is more complex than just taking angle={0, pi/2, pi, 3pi/2} because of the presence of the shape matrix. But it's not too difficult:

# location along the ellipse
# linear algebra lifted from the code for ellipse()
ellipse.loc <- function(theta, center, shape, radius)
{
    vert <- cbind(cos(theta), sin(theta))
    Q <- chol(shape, pivot=TRUE)
    ord <- order(attr(Q, "pivot"))
    t(center + radius*t(vert %*% Q[, ord]))
}

# distance from this location on the ellipse to the center 
ellipse.rad <- function(theta, center, shape, radius)
{
    loc <- ellipse.loc(theta, center, shape, radius)
    (loc[,1] - center[1])^2 + (loc[,2] - center[2])^2
}

# ellipse parameters
center <- c(-0.05, 0.09)
A <- matrix(c(20.43, -8.59, -8.59, 24.03), nrow=2)
radius <- 1.44

# solve for the maximum distance in one hemisphere (hemi-ellipse?)
t1 <- optimize(ellipse.rad, c(0, pi - 1e-5), center=center, shape=A, radius=radius, maximum=TRUE)$m
l1 <- ellipse.loc(t1, center, A, radius)

# solve for the minimum distance
t2 <- optimize(ellipse.rad, c(0, pi - 1e-5), center=center, shape=A, radius=radius)$m
l2 <- ellipse.loc(t2, center, A, radius)

# other points obtained by symmetry
t3 <- pi + t1
l3 <- ellipse.loc(t3, center, A, radius)

t4 <- pi + t2
l4 <- ellipse.loc(t4, center, A, radius)

# plot everything
MASS::eqscplot(center[1], center[2], xlim=c(-7, 7), ylim=c(-7, 7), xlab="", ylab="")
ellipse(center, A, radius, col="red", lty=2)
points(rbind(l1, l2, l3, l4), cex=2, col="blue", lwd=2)