[R] isoMDS vs. other non-metric non-R routines

[Christian Hennig]

Dear Phil,

I don't have experiences with Minissa but I know that isoMDS is bad in 
some situations. I have even seen situations with non-metric 
dissimilarities in which the classical MDS was preferable.

Some alternatives that you have:
1) Try to start isoMDS from other initial configurations (by default, it 
starts from the classical solution).
2) Try sammon mapping (command should be "sammon").
3) Have a look at XGvis/GGvis (which may be part of XGobi/GGobi). These 
are not directly part of R but have R interfaces. They allow you to toy
around quite a lot with different algorithms, stress functions (the 
isoMDS stress is not necessarily what you want) and initial 
configurations so that you can find a better solution and understand your 
data better. Unfortunately I don't have the time to give you more detail, 
but google for it (or somebody else will tell you more).

Best,
Christian


On Tue, 13 Feb 2007, Philip Leifeld wrote:

> Dear useRs,
>
> last week I asked you about a problem related to isoMDS. It turned
> out that in my case isoMDS was trapped. Nonetheless, I still have
> some problems with other data sets. Therefore I would like to know if
> anyone here has experience with how well isoMDS performs in
> comparison to other non-metric MDS routines, like Minissa.
>
> I have the feeling that for large data sets with a high stress value
> (e.g. around 0.20) in cases where the intrinsic dimensionality of the
> data cannot be significantly reduced without considerably increasing
> stress, isoMDS performs worse (and yields a stress value of 0.31 in
> my example), while solutions tend to be similar for better fits and
> lower intrinsic dimensionality. I tried this on another data set
> where isoMDS yields a stress value of 0.19 and Minissa a stress value
> of 0.14.
>
> Now the latter would still be considered a fair solution by some
> people while the former indicates a poor fit regardless of how strict
> your judgment is. I generally prefer using R over mixing with
> different programs, so it would be nice if results were of comparable
> quality...
>
> Cheers
>
> Phil
>
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*** --- ***
Christian Hennig
University College London, Department of Statistical Science
Gower St., London WC1E 6BT, phone +44 207 679 1698
chrish at stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche