I re-did the code and got a consistent Perron root: hooray! ... View more

Thanks so much for your reply.   Actually, I'm only checking that the largest real number eigenvalue of each product is the same (which is the Perron root), because this is what Double Skew-Dual Scaling should yield across all four product matrices. Because SAS lists these in descending order by default, I only need to check the largest real number eigenvalue at the top (unless I'm wrong?).    Thinking about this problem, I may need to drop a variable which has only values of 1; I think this might create problems in the product matrices.     ... View more

Thank you so much for your reply.   Data are known and verified to only have values of 0 or 1 and to not have missing values, the original matrix (A) is "mirrored" (A') as its complement as follows:   do i=1 to 55;   vara_p(i) = abs(vara(i)-1); end;   Both original & complement are then transposed (B & B') & multiplied as A*B', A'*B, B*A', B'*A; regarding computation:   proc iml; use a_orig; read all var _num_ into y; use b_transp; read all var _num_ into i; c = y * i; create Out var {c}; append; close Out; quit;   Similarly I made an i*y for the same two matrices, then calculated products for the other two matrices.   The output for the four product matrices were two 3,025-long & two 9,709,456-long outputs, each of which I recognized as the square of 55 & 3116, the dimensions of the respective product matrix, so I reconfigured each of those to create the square matrices via datasteps. I then made a dataset (e.g., c_prod) for the square product matrices:   proc iml; use c_prod; read all var _num_ into C; call eigen(val, rvec, C); print val; quit;   There should be a single Perron root across all four square product matrices, but unfortunately, that's not what I'm finding:   http://www.psych.purdue.edu/~ehtibar/publications/DSDS.pdf :     SAS version: 9.04.01M5P09132017   Take care,   ~Cliff ... View more