Objects of class "dissimilarity" representing the dissimilarity
matrix of a dataset.
The dissimilarity matrix is symmetric, and hence its lower triangle
(column wise) is represented as a vector to save storage space.
If the object, is called do, and n the number of
observations, i.e., n <- attr(do, "Size"), then
for \(i < j <= n\), the dissimilarity between (row) i and j is
do[n*(i-1) - i*(i-1)/2 + j-i].
The length of the vector is \(n*(n-1)/2\), i.e., of order \(n^2\).
"dissimilarity" objects also inherit from class
dist and can use dist methods, in
particular, as.matrix, such that \(d_{ij}\)
from above is just as.matrix(do)[i,j].
The object has the following attributes:
the number of observations in the dataset.
the metric used for calculating the dissimilarities. Possible values are "euclidean", "manhattan", "mixed" (if variables of different types were present in the dataset), and "unspecified".
optionally, contains the labels, if any, of the observations of the dataset.
optionally, if a dissimilarity could not be computed, because of too many missing values for some observations of the dataset.
when a mixed metric was used, the types for each variable as one-letter codes (as in the book, e.g. p.54):
Asymmetric binary
Symmetric binary
Nominal (factor)
Ordinal (ordered factor)
Interval scaled (numeric)
raTio to be log transformed (positive numeric)
daisy returns this class of objects.
Also the functions pam, clara, fanny,
agnes, and diana return a dissimilarity object,
as one component of their return objects.
The "dissimilarity" class has methods for the following generic
functions: print, summary.