I'm trying to determine how many random initializations (restarts) I should use when performing an nMDS ordination. I understand I want to choose the solution that minimizes the stress, but how many random restarts are necessary to make sure I've actually found the lowest stress configuration for the ordination? How many random restarts will deliver an appropriate number of configurations from which to pick the one with the lowest stress?
(Note: there was a confusion in my original formulation of this question. The number of iterations (vs. random starts) for each initialization cannot really be determined without actually performing the iterations and examining when convergence in stress minimization actually occurs).
I would like to ensure I'm not simply finding a 'local solution', and I would assume that the increased number of restarts would often (though not always) increase my chances of avoiding that issue (though this is almost an entirely different issue that has likely been addressed elsewhere).
Should the number of restarts be influenced by: number of samples, number of variables per sample, number of dimensions, etc? OR should I simply just pick a large number of starts and run with it? Is there a rule?
Let's assume I have a powerful enough computer (e.g., a powerful PC but not a super computing cluster) that allows for processing time and power to not be the limiting factors.
My data (if interested):
- A Bray-Curtis distance matrix with 130816 values that was calculated from data with 512 different rows (samples) and 49 different columns (variables).
- I want a 2D ordination.
- I'm using the
nmdsfunction in the
ecodistpackage in R.