问题
I have a species abundance dataset with quite a few zeros in it and even when I set trymax = 1000 for metaMDS() the program is unable to find a stable solution for the stress. I have already tried combining data (collapsing multiple years together to reduce the number of zeros) and I can't do any more. I was just wondering if anyone knows - is it scientifically valid to pick what R gives me at the end (the lowest of the 1000 solutions) or should I not be using NMDS because it cannot find a stable spot? There seems to be very little information about this on the internet.
回答1:
One explanation for this is that you are trying to use too few dimensions for the mapping. I presume you are using the default k = 2? If so, try k = 3 and compare the stress from the best solution you got from the 1000 tries for the k = 2 solution.
I would be a little concerned to take one solution out of 1000 just because it had the best/lowest stress.
You could also try 1000 more random starts and see if it converges if you run more iterations. When you saved the output from metaMDS(), you can supply that object to another call to metaMDS() via the previous.best argument. It will then do trymax further random starts but compare any lower-stress solutions with the previous best and converge if it finds one similar to it, rather than have to find two similar low-stress solutions in the 1000 starts.
来源：https://stackoverflow.com/questions/14434794/no-stable-solution-using-metamds-in-vegan