What’s that “pre- and post-multiply” stuff?
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Often in SEM scripts you will see matrices being pre- and post-multiplied by some other matrix. For instance, this figures in scripts computing the genetic correlation between variables. How does pre- and post-multiplying a variance/covariance matrix give us a correlation matrix? And what is it that we are multiplying this matrix by?
In general, a covariance matrix can be converted to a correlation matrix by pre- and post-multiplying by a diagonal matrix with 1/SD for each variable on the diagonal.
In R, matrix inversion (usually signified by A -1) is done using the solve() function.
For the diagonal case, the inverse of a matrix is simply 1/x in each cell.
Example with variance matrix A
A = matrix(nrow = 3, byrow = T,c(
1,0,0,
0,2,0,
0,0,3)
);
solve(A)
[,1] [,2] [,3]
[1,] 1 0.0 0.00
[2,] 0 0.5 0.00
[3,] 0 0.0 0.33
A number times its inverse = 1. For Matrices
solve(A) %*% A = Identity Matrixsolve(A) %*% A # = I: The Standardized diagonal matrix
[,1] [,2] [,3]
[1,] 1 0 0
[2,] 0 1 0
[3,] 0 0 1
An example with values (covariances) in the off-diagonals
A = matrix(nrow = 3, byrow = T, c(
1, .5, .9,
.5, 2, .4,
.9, .4, 4)
);
I = matrix(nrow = 3, byrow = T, c(
1, 0, 0,
0, 1, 0,
0, 0, 1)
);
varianceA = I * A # zero the off-diagonal (regular, NOT matrix multiplication)
sdMatrix = sqrt(varianceA) # element sqrt to get SDs on diagonal: SD=sqrt(var)
invSD = solve(sdMatrix) # 1/SD = inverse of sdMatrix
invSD
[,1] [,2] [,3]
[1,] 1 0.00 0.0
[2,] 0 0.71 0.0
[3,] 0 0.00 0.5
Any number times its inverse = 1, so this sweeps covariances into correlations
corr = invSD %*% A %*% invSD # pre- and post- multiply by 1/SD
[,1] [,2] [,3]
[1,] 1.00 0.35 0.45
[2,] 0.35 1.00 0.14
[3,] 0.45 0.14 1.00
Easy way of doing this in R
Using diag to grab the diagonal and make a new one, and capitalising on the fact that inv(X) = 1/x for a diagonal matrix
diag(1/sqrt(diag(A))) %&% A # The %&% is a shortcut to pre- and post-mul
[,1] [,2] [,3]
[1,] 1.00 0.35 0.45
[2,] 0.35 1.00 0.14
[3,] 0.45 0.14 1.00
Even-easier built-in way
cov2cor(A)
[,1] [,2] [,3]
[1,] 1.00 0.35 0.45
[2,] 0.35 1.00 0.14
[3,] 0.45 0.14 1.00
Note: See also this followup post on getting a correlation matrix when you are starting with a lower-triangular Cholesky composition.
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