[R] 2D fellows
TRAN Liem FTM/Men
liem.tran at francetelecom.fr
Tue May 16 12:08:20 CEST 2000
Hello Winfried
> I don't know what you mean exactly by a "2D-array", I would think first of
> something you could easily convert to a matrix, or a data frame! (I just had a
> look on "Notes on R" and the description of arrays there confirms my belief
> about the convertion.)
Indeed, my objects can be considered as matrix or data frame
> Well, if you could convert your arrays into vectors (via matrix or data frame
> if you want to) and collect them in a data frame you could use these functions.
It would give me (for example with "dist") the (relative) distances
between
my collection of rows.
My problem (I realize I wasn't precise enough) is to calulate, for
example, the distance between two matrices and not between two rows.
If I consider 2 matrices A and B with N rows each (and M columns), I
could use dist on each pair of rows (one for A, one for B), and compute
N time "dist" on 2 x M matrices.
And I could after calculate a distance with the N distances but I find
it a bit heavy, and I don't know how to
perform an object which would be usable as input for, let's say,
"hclust".
> If you need a special distance you could program a function in which you compute
> the pairwise distances and write them to a dissimilarity matrix as in the
> package cluster.
That is typically my problem with R: How can I write the right object
like a dissimilarity matrix if I need one?
I'm not used enough with the "statistical" objects and not used enough
with R to succeed writing directly the objects in the right "R-shape".
That's why I would like to find already "R-shaped" objects.
> For a description see the documentation there (or just type
> daisy after loading the package cluster). With this you could use the functions
> diana for divisive hierarchical
> clustering, agnes for agglomerative nesting or for k-means pam.
>
I just installed the cluster package among others. I hope to find what
I'm looking for.
Otherwise, I'll be back ;-)
> I hope this helps,
>
It does. Many thanks
Liêm
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