turn on suggestions

Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

Find a Community

- Home
- /
- Analytics
- /
- Stat Procs
- /
- How to use Proc Mds?

Topic Options

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

06-12-2015 10:27 AM

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

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

09-14-2015 03:25 AM

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