The simplest way, if you have a function that returns the distance matrix,
is to use as.dist(). E.g.,
myDist <- function(...) {
## compute distance matrix dmat.
...
return(as.dist(dmat))
}
I believe most clustering algorithms in R will accept dist objects.
If that doesn't do it, you can download the source for the `cluster' package
from CRAN.
Andy
From: Ricardo Zorzetto Nicoliello Vencio
Hi everyone,
I want to create my own distance measure, other than 'euclidean' or
'manhatan', to use in cluster pckgs. To do this I think that I need to
change dist(), in mva pckg, or daisy(), in cluster pckg. (or
is there a
cleaver way ?)
But this functions are in fact things like: .Fortran(
"daisy", ... ) or
.C("dist",...).
I tried unsuccessfully to find source code of .Fortran or .C
function to
lear how R calculate dissimilarity matrix and then modify
source to add my
own distance metrix.
Could someone help me ? Help me to find .Fortran(...) source
or helpe me
with better idea to implement my own distance metrix ?
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