On Wed, 5 May 2010, Ralf B wrote:
Hi R friends,
I am posting this question even though I know that the nature of it is
closer to general stats than R. Please let me know if you are aware of
a list for general statistical questions:
I am looking for a simple method to distinguish two groups of data in
a long vector of numbers:
list <- c(1,2,3,2,3,2,3,4,3,2,3,4,3,2,400,340,3,2,4,5,6,4,3,6,4,5,3)
I would like to 'learn' that 400,430 are different numbers by using a
simple approach.
It seems that you want to cluster the data. There are, of course, loads of
clustering algorithms around, see e.g.,
http://CRAN.R-project.org/view=Cluster
In this simple example a standard hierarchical clustering approach shows
you what you're after.
## data
list <- c(1,2,3,2,3,2,3,4,3,2,3,4,3,2,400,340,3,2,4,5,6,4,3,6,4,5,3)
## cluster using Ward method for Euclidian distances
hc <- hclust(dist(list, method = "euclidian"), method = "ward")
plot(hc)
hc
## cut into two clusters
split(list, cutree(hc, k = 2))
hth,
Z
The outcome of processing 'list' should therefore be:
listA <- c(1,2,3,2,3,2,3,4,3,2,3,4,3,2,3,2,4,5,6,4,3,6,4,5,3)
listB <- c(400,340)
I am thinking a non-parametric test since I have no knowledge of the
underlying distribution. The numbers are time differences between two
actions recorded from a the same person over time. Because the data
was obtained from the same person I would naturally tend to use
Wilcoxon Signed-Rank test. Any thoughts on that?
Are there any R packages that would process such a vector and use
non-parametric methods to split or divide groups based on their
values? Could clustering be the answer given that I already know that
I always have two groups with a significant difference between the
two.
Thanks a lot,
Ralf
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