[R] Using statistical test to distinguish two groups

[Erik Iverson]

One of many possible approaches is called k-means clustering.

my.data <- 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)
split(my.data, kmeans(my.data, 2)$cluster)

$`1`
[1] 400 340

$`2`
  [1] 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

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.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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