I looked at the help page for rcorr.cens and was surprised that
function, designed for censored data and taking input as a Surv
object, was being considered for that purpose. This posting to r-help
may be of interest. John Baron offers a simple implementation that
takes its input as (x,y):
http://finzi.psych.upenn.edu/R/Rhelp02/archive/19749.html
goodman <- function(x,y){
Rx <- outer(x,x,function(u,v) sign(u-v))
Ry <- outer(y,y,function(u,v) sign(u-v))
S1 <- Rx*Ry
return(sum(S1)/sum(abs(S1)))}
I then read Frank's response to John and it's clear that my impression
regarding potential uses of rcorr.cens was too limited. Appears that
you could supply a "y" vector to the "S" argument and get more
efficient execution.
--
David Winsemius
--
On Mar 9, 2009, at 11:13 AM, Kim Vanselow wrote:
Dear r-helpers!
I want to classify my vegetation data with hierachical cluster
analysis.
My Dataset consist of Abundance-Values (Braun-Blanquet ordinal
scale; ranked) for each plant species and relevé.
I found a lot of r-packages dealing with cluster analysis, but none
of them is able to calculate a distance measure for ranked data.
Podani recommends the use of Goodman and Kruskals' Gamma for the
distance. I found the function rcorr.cens (outx=true) of the Hmisc
package which should do it.
What I don't understand is how to define the input vectors x, y with
my vegetation dataset. The other thing how I can use the output of
rcorr.cens for a distance measure in the cluster analysis (e.g. in
vegan or amap).
Any help would be greatly appreciated,
Thank you very much,
Kim
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