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 ? > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > > ------------------------------------------------------------------------------ Notice: This e-mail message, together with any attachments,...{{dropped}} ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html