Kim Vanselow wrote:
Thanks to David and Frank for the suggestions. With a 2-dimensional input
rcorr.cens and John Baron's implementation works good. But I am not able to
calculate gamma for a multivariate matrix
example: columns=species; rows=releves; the numbers are BB-values (ordinal scale;
1<3 but 3-1 is not necessarily 2)
K. ceratoides S. caucasica A. tibeticum
A1 3 1 1
A2 0 3 2
A3 1 1 0
A4 2 2 0
A5 0 3 2
B1 1 1 1
B2 4 3 1
I want to calculate a distance matrix with scale unit "Goodman-Kruskals gamma"
(instead of classical euclidean, bray curtis, manhattan etc.) which I can use for
hierachical cluster analysis (e.g. amap, vegan, cluster) in order to compare the
different releves.
Further suggestions would be greatly appreciated,
Thank you very much,
Kim
-------- Original-Nachricht --------
Datum: Mon, 09 Mar 2009 13:27:29 -0500
Von: Frank E Harrell Jr <f.harr...@vanderbilt.edu>
An: David Winsemius <dwinsem...@comcast.net>
CC: Kim Vanselow <vanse...@gmx.de>, r-help@r-project.org
Betreff: Re: [R] rcorr.cens Goodman-Kruskal gamma
David Winsemius wrote:
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.
Yes rcorr.cens was designed to handle censored data but works fine with
uncensored Y. You may need so specify Surv(Y) but first try just Y. It
would be worth testing the execution speed of the two approaches.
Frank
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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
A function related to that is Hmisc's varclus function which will use
Spearman, Pearson, or Hoeffding indexes for similarity measures.
Frank
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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