# Eigensystem for simple equi-correlation matrix

I'm trying to get a set of eigenvectors for a correlation matrix, but getting stuck, maybe because I do not properly normalize them. For example, the following code works, in the sense that I get back the original matrix from the decomposition. I also recognize the eigenvector set as a Helmert matrix.

But if I replace 0.7 with 7/10, I no longer get the original matrix back.

What are proper ways to normalize so that it works with 7/10, or even better, with symbolic values?

r = 0.7; k = 3;
Co[i_, j_, n_] := If[i == j, 1, r]; dim = Array[# &, k];
c = Outer[Co[#1, #2, n] &, dim, dim];
MatrixForm[c]
{vals, vecs} = Eigensystem[c];
vv = Transpose[Normalize /@ vecs];
MatrixForm[vv]
sD = DiagonalMatrix[Sqrt[vals]];
vv.sD.Transpose[vv.sD] // MatrixForm


Eigensystem does not orthonormalize the eigenvectors if the matrix consists only of exact input. What worses the situation in this case is that there is an eigenvalue with multiplicity 2 so that the eigenvectors returned this way need not be perpendicular to each other.

k = 3;
r = 7/10;
c = ConstantArray[r, {k, k}] + (1 - r) IdentityMatrix[k];
{vals, vecs} = Eigensystem[c];


So, while we still have the relation

c == Inverse[vecs].(vals vecs)


True

the following does not hold any more with exact input:

c == Transpose[vecs].(vals vecs)


False

You may try Orthogonalize:

vecs1 = Orthogonalize[vecs];
c == Transpose[vecs1].(vals vecs1)


True

This works also with symbolic correlation r and higher dimensions k:

k = 12;
r =.;
c = ConstantArray[r, {k, k}] + (1 - r) IdentityMatrix[k];
{vals, vecs} = Eigensystem[c];
vecs1 = Orthogonalize[vecs];
c == Transpose[vecs1].(vals vecs1) // Simplify


True

• I guess it makes parsimonious sense, don't do unless explicitly asked, but I was so engrossed in my statistical application, I haven't even considered non-orthogonality. Documentation is not very clear on this, but my fault, I admit. Thank you so much, you saved me hours of frustration, when I couldn't reproduce a noncentrality vector derived by hand. I also like the way you construct the matrix, that's how I'd do it in R. Will borrow. Nov 4, 2018 at 16:10
• You're welcome. Nov 4, 2018 at 16:41