[R] Categories or clusters for univariate data

[Mulholland, Tom]

x <- c(1,2,3,4,5,8,9,10,11,12,15,16,17,18,19,22,23,24,33,34,35)
require(cluster)
pam(x,5)

Medoids:
     [,1]
[1,]    3
[2,]   10
[3,]   17
[4,]   23
[5,]   34
Clustering vector:
 [1] 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 5 5 5
Objective function:
   build     swap 
1.285714 1.047619 

Available components:
[1] "medoids"    "clustering" "objective"  "isolation"  "clusinfo"   "silinfo"    "diss"       "call"       "data"      

Does this help?


> -----Original Message-----
> From: Allen Hathaway [mailto:hathaway at sover.net]
> Sent: Tuesday, 22 February 2005 8:48 AM
> To: r-help list
> Subject: [R] Categories or clusters for univariate data
> 
> 
> If I have a vector, x, such that
> 
> x <- c(1,2,3,4,5,8,9,10,11,12,15,16,17,18,19,22,23,24,33,34,35)
> 
> if I plot that vector
> 
> plot(x)
> 
> it is visibly obvious that the data "groups" or "clusters" 
> into distinct 
> groupings.  The data trends along a more-or-less linear path, 
> and then an 
> abrupt jump.  For a trivial case, such as I have given, you 
> can pick out the 
> groups or categories visually, and manually derive the upper 
> and lower 
> bounds for each group.  My question is, is there a function 
> in R that can do 
> the same thing for more complex and subtle groupings in 
> univariate data, and 
> provide a statistical basis for the result?
> 
> Allen
> 
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