How to use Proc Mds?

badikidiki — Fri, 12 Jun 2015 14:27:38 GMT

Hello,

we do have a distance matrix with "1 minus jaccard" as distances in a triangle shape.

To estimate the coordinates we use proc mds.

What level to we have to assume? Is it level=absolut as given in the example with the flying mileages?

When using the level=absolut option we get a fit plot where the data points differ very much from the diagonal.

And they don't differ randomly but in a certain pattern.

Does this mean, that the estimated coordinates aren't good or interpretable?

Thanks!

Badikidiki

Re: How to use Proc Mds?

badikidiki — Mon, 14 Sep 2015 07:25:26 GMT

Hello all together,

today we can share our experiences on MDS with you.

Our data is like this: matrix of distances with more than 500 rows and columns, distances between 0 and 1, small distances are more important than bigger ones.

We got the best results with level=ordinal, which fits an ordinal MDS
To estimate the smaller distances with higher accuracy we used the following weights: weight=(observed distances)**(-5)
Especially increasing the number of levels of the MDS (up to 15 or 20) gave better results.

Better results were measured as a lower Stress value, better fit plot with randomly scattered points around the diagonal, better Shepard-Plot (observes versus estimated distances).

Our Shepard-Plot showed less variance for smaller distances than for bigger ones due to our weights.

For further information see P. Groenen, I. Borg (2005), "Modern Multidimensional Scaling", Springer

Badikidiki

topic How to use Proc Mds? in Statistical Procedures

How to use Proc Mds?

Re: How to use Proc Mds?