[R-sig-eco] help: Cluster analysis: obtaining average distances per cluster

[Marcelino de la Cruz]

Hi,

try this:


mydata2 <- mydata
for (i in 1:9) mydata2[groups.9==i, groups.9==i] <- 
mean(as.dist(mydata[groups.9==i,groups.9==i]))

diag(mydata2) <-0

plot( hclust(as.dist(mydata2)))



and please, send messages only to r-sig-ecology at r-project.org

HTH,

Marcelino


El 09/06/2013 21:36, Barnabas Daru escribió:
> Dear all,
> I am using R to generate clusters for a pairwise distance matrix of a large dataset (over 3000 grid cells) and to group the cells based on similarity into 9 or fewer clusters.
>
> I have successfully used the "cutree" function in the R CLUSTER package to get 9 clusters; but I got stuck on how to create a new matrix based on means of all pairwise grid cell values for the new clusters defined by my cutree function as follows:
>
> mydata <- read.csv("my_distance_matrix.csv", header=T, row.names=1, sep=",")
>
> mydata_dist <- as.dist(mydata)
> UPGMA <- hclust(mydata_dist)
> plot(UPGMA) # Gives a large dendrogram with tip.labels difficult to read!
> # I only want a summary dendrogram
> groups.9 <- cutree(UPGMA, 9)
>
> groups.9.mean <- aggregate(UPGMA,list(groups.9),median)
> # I got the following error:
> Error in as.data.frame.default(x):
> 	cannot coerce class '"hclust"' into a data.frame
>
> What I am interested in is to obtain the following:
> (a). A dendrogram showing only the summary branches i.e. the dendrogram with only nine branches and the tip labels as the mean pairwise distance connecting each group for each clusters.
> (b) to be able to use the "summary dendrogram" converted as a new distance matrix as described in (a) for further analysis e.g. NMDS etc.
>
> Any help especially in the form of R code will be highly appreciated.
> Thanks and kind regards
> Barnabas
>
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>   /---/ Barnabas Daru
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