dissimilarity.object: Dissimilarity Matrix Object
Description
Objects of class "dissimilarity" representing the dissimilarity
matrix of a dataset.
Value
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 $d(i,j)$
from above is just as.matrix(do)[i,j].The object has the following attributes:
- Size
- the number of observations in the dataset.
- Metric
- 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".
- Labels
- optionally, contains the labels, if any, of the
observations of the dataset.
- NA.message
- optionally, if a dissimilarity could not be
computed, because of too many missing values for some observations
of the dataset.
- Types
- when a mixed metric was used, the types for each
variable as one-letter codes (as in the book, e.g. p.54):
- A
- Asymmetric binary
- S
- Symmetric binary
- N
- Nominal (factor)
- O
- Ordinal (ordered factor)
- I
- Interval scaled (numeric)
- T
- raTio to be log transformed (positive numeric)
.
GENERATION
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.METHODS
The "dissimilarity" class has methods for the following generic
functions: print, summary.