Dear R-help,
In performing cluster analysis (packages: hopach, cluster, boot, and
many others), I got these errors:
makeoutput(kidney, gene.hobj, bobj, file= kidney.out, gene.names=
gene.acc)
Error: could not find function makeoutput
boot2fuzzy(kidney, bobj, gene.hobj, array.hobj,
Hello,
I would like to know if there is a function in an R library that
allows to do cluster analysis under contiguity constraints ?
Thank you very much for your answer !
Lise Bellanger
--
Lise Bellanger,
Université de Nantes
Département de Mathématiques, Laboratoire Jean
Dear All,
a long time ago I ran a cluster analysis where the dissimilarity matrix used
consisted of Dmax (or Kolmogorov-Smirnov distance) values. In other words
the maximum difference between two cumulative proportion curves. This all
worked very well indeed. The matrix was calculated using
Dear Kris,
a) how would one go about calculating the matrix of Dmax/KS distance values?
Hmm, I'd implement this directly by comparing the curves on a dense
sequence of equidistant points over a given value
range (hope you know a suitable one) and looking for the maximum
difference...
b) of
Hi list,
I am interested in cluster analysis of microarray data. The data was generated
using cDNA method and a loop design.
I was wondering if any one has a suggestion about which package I can use to
analyse such data.
Many thanks in advance
Mahdi
--
---
Mahdi Osman [EMAIL PROTECTED] writes:
Hi list,
I am interested in cluster analysis of microarray data. The data was
generated using cDNA method and a loop design.
I was wondering if any one has a suggestion about which package I can
use to analyse such data.
There are many packages
Wade == Wade Wall [EMAIL PROTECTED]
on Fri, 14 Jul 2006 10:10:11 -0400 writes:
Wade I am trying to run a cluster analysis using Sorenson
Wade (Bray-Curtis) distance measure with flexible beta
Wade linkage method. However, I can't seem to find
Wade flexible beta in any of
Hi all,
I am trying to run a cluster analysis using Sorenson (Bray-Curtis) distance
measure with flexible beta linkage method. However, I can't seem to find
flexible beta in any of the functions/packages I have looked at.
Any help would be appreciated.
[[alternative HTML version
Hello,
I'm playing around with cluster analysis, and am looking for methods to
select the number of clusters. I am aware of methods based on a 'pseudo
F' or a 'pseudo T^2'. Are there packages in R that will generate these
statistics, and/or other statistics to aid in cluster number
Have you checked the amap package? It has been updated just recently and if
I am not wrong there is a method which indicates the best number of k groups
for your data.
Best wishes,
P. Olsson
2006/2/5, John Janmaat [EMAIL PROTECTED]:
Hello,
I'm playing around with cluster analysis, and am
Hi,
as said before, some statistics to estimate the number of clusters are in
the cluster.stats function of package fpc. These are distance-based,
not pseudo F or T^2. They are documented in the book
of Gordon (1999) Classification (see ?cluster.stats for more references).
It also includes
Dear John,
You can play around with cluster.stats function in library fpc, e.g. you
can try:
library(fpc)
library(cluster)
data(xclara)
dM - dist(xclara)
cl - vector()
for(i in 2:7){
cl[i] - cluster.stats(d=dM, clustering=clara(d,i)$cluster,
silhouette=FALSE)$wb.ratio
}
plot(1:6,cl[2:7],
Hello,
I'm trying some cluster analysis, using the hclust command. I am looking for
some help in selecting the 'best' number of clusters. Some software reports
pseudo-F and pseudo-T^2 statistics, for each cluster merge. Is there any way
to generate such statistics simply in R?
Thanks,
Le 05.02.2006 17:50, John Janmaat a écrit :
Hello,
I'm trying some cluster analysis, using the hclust command. I am looking for
some help in selecting the 'best' number of clusters. Some software reports
pseudo-F and pseudo-T^2 statistics, for each cluster merge. Is there any way
to
Markus == Markus Preisetanz [EMAIL PROTECTED]
on Thu, 26 Jan 2006 20:48:29 +0100 writes:
Markus Dear R Specialists,
Markus when trying to cluster a data.frame with about 80.000 rows and 25
columns I get the above error message. I tried hclust (using dist), agnes
(entering the
Dear R Specialists,
when trying to cluster a data.frame with about 80.000 rows and 25 columns I get
the above error message. I tried hclust (using dist), agnes (entering the
data.frame directly) and pam (entering the data.frame directly). What I
actually do not want to do is generate a
Let's do some simple calculation: The dist object from a data set with
8 cases would have
8 * (8 - 1) / 2
elements, each takes 8 bytes to be stored in double precision. That's over
24GB if my arithmetic isn't too flaky. You'd have a devil of a time trying
to do this on a 64-bit
Hi All,
I am wondering if there is any literature or any prior implementations
of cluster analysis for only nominal (categorical) variables for a
large dataset, apprx 20,000 rows with 15 variables.
I came across one or two such implementations, but they seem to assume
certain data distributions.
Hi,
Im using hclust to make a cluster analysis in Q mode, but I have too many
objects (observations) and its difficult to identify them in the plot. Id like
to get a list with the objects ordered in the same way they appear in the
cluster.
I have already tried order, labels and merge but I
Hi!
Take a look at the packages mclust and flexmix!
They use the EM algorithm for mixture modelling, sometimes called model
based cluster analysis.
Best,
Christian
On Wed, 26 Jan 2005 [EMAIL PROTECTED] wrote:
Hi,
I am looking for a package to do the clustering analysis using the
Hi,
I am looking for a package to do the clustering analysis using the
expectation maximization algorithm.
Thanks in advance.
Ming
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting
Hi people,
Does anybody know some Density-Based Method for clustering implemented in R?
Thanks,
Fernando Prass
___
Yahoo! Acesso Grátis - Internet rápida e grátis. Instale o discador agora!
Fernando Prass wrote:
Hi people,
Does anybody know some Density-Based Method for clustering implemented in R?
Have you looked at CRAN package mclust?
Thanks,
Fernando Prass
___
Yahoo! Acesso Grátis - Internet rápida e grátis. Instale o
Yes, but mclust don't have a density-based algorithm. Mclust have the algorithm
BIC, that is a model-based method...
Fernando Prass
--- Kjetil Brinchmann Halvorsen [EMAIL PROTECTED] escreveu:
Fernando Prass wrote:
Hi people,
Does anybody know some Density-Based Method for clustering
maybe ?kmeans is what you're looking for ...
ingmar
On 10/21/04 2:47 PM, Fernando Prass [EMAIL PROTECTED] wrote:
Yes, but mclust don't have a density-based algorithm. Mclust have the
algorithm
BIC, that is a model-based method...
Fernando Prass
--- Kjetil Brinchmann Halvorsen [EMAIL
No, kmeans is a partition method. I need a model-based method, like DBSCAN or
DENCLUE algorithm...
Fernando Prass
--- Ingmar Visser [EMAIL PROTECTED] escreveu:
maybe ?kmeans is what you're looking for ...
ingmar
On 10/21/04 2:47 PM, Fernando Prass [EMAIL PROTECTED] wrote:
Yes, but
I'm no expert in this, but mclust is `density-based' because it estimates
the density with a mixture of Gaussians. If this is not what you want, you
should clarify what you mean by `density-based'. Do you mean an algorithm
based on kernel estimator of the density?
Andy
From: Fernando Prass
Dear Fernando,
below you find a DBSCAN function I wrote for my own purposes.
It comes with no warranty and without proper documentation, but I followed
the notation of the original KDD-96 DBSCAN paper.
For large data sets, it may be slow.
Best,
Christian
On Thu, 21 Oct 2004, Fernando Prass
AndyL == Liaw, Andy [EMAIL PROTECTED]
on Thu, 21 Oct 2004 09:18:54 -0400 writes:
AndyL I'm no expert in this, but mclust is `density-based'
AndyL because it estimates the density with a mixture of
AndyL Gaussians. If this is not what you want, you should
AndyL clarify what
From: Martin Maechler
AndyL == Liaw, Andy [EMAIL PROTECTED]
on Thu, 21 Oct 2004 09:18:54 -0400 writes:
AndyL I'm no expert in this, but mclust is `density-based'
AndyL because it estimates the density with a mixture of
AndyL Gaussians. If this is not what you want, you
Andy,
I can be wrong, I'm no expert too, but density estimation is different of
density-model. MClust is a model-basead method because use model statistics
from clustering data (more information in
ftp://ftp.u.washington.edu/public/mclust/tr415R.pdf).
I need some package that implement
Dear James,
sorry, this is not really an answer.
I use cutree to obtain clusters from an hclust object.
I do not get from the identify help page that identify should do anything
like what you expect it to do... I tried it out and to my surprise it
behaved as you said, i.e., it indeed does
ChrisH == Christian Hennig [EMAIL PROTECTED]
on Fri, 15 Oct 2004 11:43:53 +0200 (MEST) writes:
ChrisH Dear James,
ChrisH sorry, this is not really an answer.
nor this. I'm answering Christian...
ChrisH I use cutree to obtain clusters from an hclust
ChrisH object. I do
On Friday 15 Oct 2004 10:43 am, you wrote:
PS: It seems that each value is typed twice because classi is named, and
each value is also a name. Try as.vector(classi). (Perhaps a little useful
help in the end?)
Indeed. I have tried, for example:
as.vector(classi[[1]])
and
On Friday 15 Oct 2004 11:02 am, you wrote:
or unname(classi) -- which is slightly more expressive in this
case and possibly more desirable in other situations.
Martin Maechler, ETH Zurich
Thanks, Martin.
I've tried, like you suggested:
un_classi - unname(classi)
but
James == James Foadi [EMAIL PROTECTED]
on Fri, 15 Oct 2004 11:36:14 +0100 writes:
James On Friday 15 Oct 2004 11:02 am, you wrote:
or unname(classi) -- which is slightly more expressive in this
case and possibly more desirable in other situations.
Martin
Hi,
testing the randomness of a cluster analysis is not a well defined
problem, because it depends crucially on your null model. In fpc, there is
nothing like this. Function prabtest in package prabclus performs such a
test, but this is for a particular data structure, namely presence-absence
Hi,
I am wondering if a Monte Carlo method (or equivalent) exist permitting to test the
randomness of a cluster analysis (eg got by
hclust(). I went through the package fpc (maybe too superficially) but dit not find
such method.
Thanks for any hint,
Patrick Giraudoux
Hi all,
Is it possible to run kmeans, pam or clara with a constraint such that
no resulting cluster has fewer than X cases?
These kmeans algorithms often find clusters that are too small for my
use. There are usually a few clusters with 1-10 cases (generally
substantial outliers). I then have
Is there anyone who would like to give me some examples of plots or data
frames on clustering anaylis?
if so, great thanks in advance!
Files can be sent to my big mail box as [EMAIL PROTECTED]
I want t operform cluster analysis on a set of data, the data is composed of
time-evolution rms
Hi,
it seems that you mix something up. hclust is for dissimilarity based
hierarchical cluster analysis, which has nothing to do with R squared,
Pseudo F
Informative output about the clustering is given as value of the hclust
object, function cutree may help to extract a concrete clustering
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