Arguments
order
a vector giving a permutation of the original observations to allow for
plotting, in the sense that the branches of a clustering tree will not cross.
order.lab
a vector similar to order, but containing observation labels instead of
observation numbers. This component is only available if the original
observations were labelled.
height
a vector with the distances between merging clusters at the successive
stages.
ac
the agglomerative coefficient, measuring the clustering structure of the
dataset.
For each observation i, denote by m(i) its dissimilarity to the first cluster
it is merged with, divided by the dissimilarity of the merger in the
final step of the algorith
merge
an (n-1) by 2 matrix, where n is the number of observations. Row i of merge
describes the merging of clusters at step i of the clustering. If a number
j in the row is negative, then the single observation |j| is merged at this
stage. If j is
diss
an object of class "dissimilarity", representing the total dissimilarity
matrix of the dataset.
data
a matrix containing the original or standardized measurements, depending
on the stand option of the function agnes. If a dissimilarity matrix was
given as input structure, then this component is not available.
GENERATION
This class of objects is returned from agnes.METHODS
The "agnes" class has methods for the following generic functions:
print, summary, plot.INHERITANCE
The class "agnes" inherits from "twins".
Therefore, the generic function pltree can be used on an agnes object.STRUCTURE
A legitimate agnes object is a list with the following components: