[R] ML optimization question--unidimensional unfolding scalin g
Peter Muhlberger
peterm at andrew.cmu.edu
Thu Nov 3 19:02:16 CET 2005
Hi Spencer: Just realized I may have misunderstood your comments about
branching--you may have been thinking about a restart. Sorry if I
misrepresented them.
See below:
On 11/3/05 11:03 AM, "Spencer Graves" <spencer.graves at pdf.com> wrote:
> Hi, Andy and Peter:
>
> That's interesting. I still like the idea of making my own local
> copy, because I can more easily add comments and test ideas while
> working through the code. I haven't used "debug", but I think I should
> try it, because some things occur when running a function that don't
> occur when I walk through it line by line, e.g., parsing the "call" and
> "..." arguments.
>
Debug's handy tho I think it is line by line.
> Two more comments on the original question:
>
> 1. What is the structure of your data? Have you considered
> techniques for Multidimensional Scaling (MDS)? It seems that your
> problem is just a univariate analogue of the MDS problem. For metric
> MDS from a complete distance matrix, the solution is relatively
> straightforward computation of eigenvalues and vectors from a matrix
> computed from the distance matrix, and there is software widely
> available for the nonmetric MDS problem. For a terse introduction to
> that literature, see Venables and Ripley (2002) Modern Applied
> Statistics with S, 4th ed. (Springer, "distance methods" in sec. 11.1,
> pp. 306-308).
>
I was looking for something on MDS in R, that'll be handy!
The data structure is a set of variables (say about 6) that I have reason to
believe measure an underlying dimension. I suspect that several of the
variables are unfolding--that is, they have their highest value for some
point on the scale and fall off w/ distance from that point in either
direction. The degree of fall-off may vary depending on the variable. Some
seem to fall off very rapidly, others not. A couple variables probably
monotonically increase w/ the underlying scale, so they don't unfold. I can
construct a distance matrix consisting of distances between these variables.
Do you think MDS might be able to handle an arrangement like this, w/ some
values folded about a scale point and with drop-off varying between
variables? The distances between the variables do not map in any
straightforward way into distances on the underlying scale because of
folding and non-linearity.
> 2. If you don't have a complete distance matrix, might it be
> feasible to approach the problem starting small and building larger,
> i.e., start with 3 nodes, then add a fourth, etc.?
>
Not sure I follow, but I do have a complete distance matrix of distances
between the variables.
> spencer graves
Thanks,
Peter
More information about the R-help
mailing list