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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
