Dear all,

I'm analyzing a dataset (A) of 400 cases with 11 binary variables.
Unfortunately, several (actually a lot) of cases are identical. NA are also
present.
I want to to plot distances between cases.
For this, I obtained a distance matrix by dist(A, method="binary"). I then
analyzed the obtained distance via Principal coordinate analysis with
cmdscale(). Results are fine.
However, do you think this is a wrong approach? After reading the
literature and previous posts, I noticed that non metrical MDS (via isoMDS
or metaMDS) could be a more correct choice.
The problem is that, when trying this methods, I immediately get problems
due to the identity between several of mycases or the presence of NA.

Typical error messages are

*"Error in isoMDS(DistB, k = 3) : zero or negative distance between objects
1 and 2"*

or

*"Error in if (any(autotransform, noshare > 0, wascores) && any(comm < 0))
{ : missing value where TRUE/FALSE needed*
*In addition: Warning message:*
*In Ops.factor(left, right) : < not meaningful for factor"*


Do you think Principal coordinate analysis on a binary distance matrix is a
decent strategy?
Thanks for any suggestion
marco

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