# Using metric MDS with non-metric distances and assessing the fit quality

I'm going to perform MDS by means of cmdscale function of standard R library. I spent several hours googling it and finally have a number of questions (some of them more general, some could be more specific to its implementations in R ).

1. I have a matrix of non-metric distances, e.g. based on Sorensen-Dice coefficient. Can I use the matrix for cmdscale function? As I understand, the function will perform "metric MDS"; so, metric MDS for non-metric distances.
2. How can I estimate the quality of my model? I've found the stress term and GOF component of output of cmdscale function, but I am confused with their interpretation. Could you clarify it for me please? Is there anything else?
3. In PCA one often calculates the percentage of explained variance. Is this term applicable in MDS as well? How can I interpret it?
• 1. Yes. 2. See three. 3. cmdscale function implements so called "classical MDS" aka "Torgerson's MDS" aka "PCoA", and it is essentially the same thing as PCA, see here: stats.stackexchange.com/questions/14002 (including my answer there if you want technical details). So you can get "explained variance" and use it as a measure of fit quality. – amoeba says Reinstate Monica Apr 19 '16 at 17:32
• @amoeba Thanks for the response, editing and valuable link. – Denis Apr 19 '16 at 22:41

• I can't say about cmdscale being not R user (and won't be reading its documentation right now) so can't answer your pt 2.