Rodrigo,
You need to think about what type of clustering algorithm you intend to use.
There are many choices. A simple example to get you started is:
dist <- read.table("matrix.txt", header=TRUE) # read in your matrix
from a text file
dend <- hclust(as.dist(dist), method="average") # do clustering,
using UPGMA method
plot(dend) #plot your dendrogram
See ?hclust for more methods. There are also other packages on CRAN that do
cluster analysis.
Simon.
Simon Blomberg, PhD
Depression & Anxiety Consumer Research Unit
Centre for Mental Health Research
Australian National University
http://www.anu.edu.au/cmhr/
Simon.Blomberg at anu.edu.au +61 (2) 6125 3379
> -----Original Message-----
> From: Rodrigo [mailto:rgazaffi at uol.com.br]
> Sent: Friday, 18 July 2003 9:00 AM
> To: r-help at stat.math.ethz.ch
> Subject: [R] i need help in cluster analyse
>
>
> Hello,
> My name is Rodrigo, I am using R program and I have a trouble.
>
> I am trying to do a dendrogram with genetics information.
> Let me explain...
> The Similarity Matrix was already did, and with this matrix I
> want to construct a dendrogram.
> So, the distance is done. I need to transform this matrix
> (that I have) in a dendrogram,
>
> I woud be very grateful if someone could help me.
> PS: I am sending a example together, for the matrix I have
> and want to transform in a dendrogram.
>
> best wish to all.
> Thank you for the attention.
>
> ##############################################################
> ##################################
> 1 2 3 4 5 6
> 7 8 9 10 11 12
> 13 14
> 01 1.0000 0.4500 0.2174 0.3636 0.3810 0.2632 0.5000 0.4167
> 0.2500 0.4286 0.3182 0.3333 0.3684 0.2778
> 02 0.4500 1.0000 0.3750 0.4583 0.4167 0.4737 0.4074 0.3929
> 0.3600 0.4000 0.2593 0.3846 0.4762 0.3500
> 03 0.2174 0.3750 1.0000 0.4167 0.4348 0.3684 0.3214 0.4074
> 0.3333 0.4167 0.3200 0.4783 0.3636 0.3889
> 04 0.3636 0.4583 0.4167 1.0000 0.5217 0.3478 0.4444 0.5385
> 0.5417 0.5000 0.4583 0.5600 0.5238 0.4286
> 05 0.3810 0.4167 0.4348 0.5217 1.0000 0.4000 0.5833 0.5000
> 0.5455 0.5217 0.4167 0.5652 0.3478 0.2273
> 06 0.2632 0.4737 0.3684 0.3478 0.4000 1.0000 0.2800 0.2593
> 0.3913 0.3182 0.3043 0.3077 0.3000 0.4706
> 07 0.5000 0.4074 0.3214 0.4444 0.5833 0.2800 1.0000 0.5926
> 0.5200 0.5600 0.4615 0.4815 0.3462 0.2917
> 08 0.4167 0.3929 0.4074 0.5385 0.5000 0.2593 0.5926 1.0000
> 0.4815 0.4815 0.4444 0.5556 0.3846 0.3200
> 09 0.2500 0.3600 0.3333 0.5417 0.5455 0.3913 0.5200 0.4815
> 1.0000 0.5909 0.5652 0.4815 0.4545 0.3478
> 10 0.4286 0.4000 0.4167 0.5000 0.5217 0.3182 0.5600 0.4815
> 0.5909 1.0000 0.4583 0.6087 0.4545 0.2727
> 11 0.3182 0.2593 0.3200 0.4583 0.4167 0.3043 0.4615 0.4444
> 0.5652 0.4583 1.0000 0.5200 0.3478 0.2609
> 12 0.3333 0.3846 0.4783 0.5600 0.5652 0.3077 0.4815 0.5556
> 0.4815 0.6087 0.5200 1.0000 0.4167 0.2222
> 13 0.3684 0.4762 0.3636 0.5238 0.3478 0.3000 0.3462 0.3846
> 0.4545 0.4545 0.3478 0.4167 1.0000 0.3158
> 14 0.2778 0.3500 0.3889 0.4286 0.2273 0.4706 0.2917 0.3200
> 0.3478 0.2727 0.2609 0.2222 0.3158 1.0000
>
>
> This matrix have 14 individuals and the analyse of similarity
> (the range of statistic is 0 to 1).
> I want to use it in that way to the cluster analyse.
>
>
>
>
>
> ---
>
>
>
>
>
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