dissimilarity.object {cluster} R Documentation

## 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): AAsymmetric binary SSymmetric binary NNominal (factor) OOrdinal (ordered factor) IInterval scaled (numeric) TraTio 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`.

### See Also

`daisy`, `dist`, `pam`, `clara`, `fanny`, `agnes`, `diana`.

[Package cluster version 2.1.0 Index]