Calculating Spearman's Rho Value Matrix for 6x6 Matrix

[swartzism]

So I'm interested in calculating the Spearman correlation matrix, but I am running into some issues. All of the examples I have found only calculate rho scores between two columns. I am trying to replicate results from a white paper (http://www.tandfonline.com/doi/abs/10.1080/03610918208812265?journalCode=lssp20) and they generate a 6x6 correlation matrix T for a 15x6 matrix R. I cannot figure out how they do this!

I know how to calculate Pearson's correlation coefficient in matrix form by performing R'*R and working on the resultant 6x6 matrix, but Spearman's doesn't seem clear how to produce this T matrix.

Any help on how I would go about generating this Spearman rank correlation matrix would be greatly appreciated!

 

[Stephen Tashi]

I am trying to replicate results from a white paper (http://www.tandfonline.com/doi/abs/10.1080/03610918208812265?journalCode=lssp20)

For your question, I suggest you give a reference that is available to the general public. There seem to be many explanations of the "Iman and Conover" method on the web. (Is there an example of your question in: https://www.casact.org/pubs/forum/06wforum/06w107.pdf ?)

 

[swartzism]

Whoops! I thought the download button would give access to the full PDF. Here is a publicly available version

https://www.uio.no/studier/emner/ma...ibution-free approach to rank correlation.pdf

The matrix in question is T on page 7 of the PDF (317 at the top right of the scanned page).

 

[Stephen Tashi]

The 15 x 6 matrix is matrix of sample data for 15 joint realizations of 6 random variables. The sample correlation matrix for 6 random variables is naturally 6 by 6.

 

[swartzism]

I've also discovered that this is not a Spearman's rank correlation, although they claim it is. If you do the the Spearman's coefficient calculation between columns, you do not get the T matrix given. It is in fact the Pearson's coefficient matrix of R! I had a bug in my correlation calculation. I am now getting

1.00000 0.00000 0.00000 0.00000 0.00000 0.00000
0.09688 1.00000 0.00000 0.00000 0.00000 0.00000
-0.46671 -0.31292 1.00000 0.00000 0.00000 0.00000
-0.23352 0.07098 0.33767 1.00000 0.00000 0.00000
0.26142 0.48383 -0.19696 -0.04125 1.00000 0.00000
0.17479 -0.22700 0.19021 -0.02984 0.05223 1.00000

As expected.