I found the problem.
For some reason, when I converted the list object with the data in it to
numeric, the values changed. This resulted in different clustering
results. Once that was fixed, the clustering was the same.
Thanks for the responses!
On Mon, Nov 15, 2010 at 2:37 PM, Peter Langfelder <
peter.langfel...@gmail.com> wrote:
> On Mon, Nov 15, 2010 at 2:19 PM, Reshmi Chowdhury
> <rchowdh...@alumni.upenn.edu> wrote:
> > Here is the code I am using:
> >
> > m <- read.csv("data_unsorted.csv",header=TRUE)
> > m <- na.omit(m)
> > cs <- hclust(dist(t(m),method="euclidean"),method="complete")
> > ds <- as.dendrogram(cs)
>
> As Christian said, you may want to plot the cs tree (i.e., plot(cs))
> in both cases and make sure that the differences do not just stem from
> equal distances. Also, check the matrix m to make sure that the first
> column in "data_unsorted.csv" is interpreted correctly by the read.csv
> function - if your first data column is interpreted as row names, the
> dendograms may indeed look different. Other than the ambiguity of
> equal distances, the dendrogram produced by hclust should not depend
> on the order of the columns in input to dist.
>
> Peter
>
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