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dist

Matrix Distance/Similarity Computation

Description

These functions compute and return the auto-distance/similarity matrix between either rows or columns of a matrix/data frame, or a list, as well as the cross-distance matrix between two matrices/data frames/lists.

Usage

dist(x, y = NULL, method = NULL, ..., diag = FALSE, upper = FALSE,
     pairwise = FALSE, by_rows = TRUE, convert_similarities = TRUE,
     auto_convert_data_frames = TRUE)
simil(x, y = NULL, method = NULL, ..., diag = FALSE, upper = FALSE,
      pairwise = FALSE, by_rows = TRUE, convert_distances = TRUE,
      auto_convert_data_frames = TRUE)

pr_dist2simil(x)
pr_simil2dist(x)

as.dist(x, FUN = NULL)
as.simil(x, FUN = NULL)

## S3 method for class 'dist'
as.matrix(x, diag = 0, ...)
## S3 method for class 'simil'
as.matrix(x, diag = NA, ...)

Arguments

`x`	For `dist` and `simil`, a numeric matrix object, a data frame, or a list. A vector will be converted into a column matrix. For `as.simil` and `as.dist`, an object of class `dist` and `simil`, respectively, or a numeric matrix. For `pr_dist2simil` and `pr_simil2dist`, any numeric vector.
`y`	`NULL`, or a similar object than `x`
`method`	a function, a registry entry, or a mnemonic string referencing the proximity measure. A list of all available measures can be obtained using `pr_DB` (see examples). The default for `dist` is `"Euclidean"`, and for `simil` `"correlation"`.
`diag`	logical value indicating whether the diagonal of the distance/similarity matrix should be printed by `print.dist`/`print.simil`. Note that the diagonal values are never stored in `dist` objects. In the context of `as.matrix` the value to use on the diagonal representing self-proximities. In case of similarities, this defaults to `NA` since a priori there are no upper bounds, so the maximum similarity needs to be specified by the user.
`upper`	logical value indicating whether the upper triangle of the distance/similarity matrix should be printed by `print.dist`/`print.simil`
`pairwise`	logical value indicating whether distances should be computed for the pairs of `x` and `y` only.
`by_rows`	logical indicating whether proximities between rows, or columns should be computed.
`convert_similarities, convert_distances`	logical indicating whether distances should be automatically converted into similarities (and the other way round) if needed.
`auto_convert_data_frames`	logical indicating whether data frames should be converted to matrices if all variables are numeric, or all are logical, or all are complex.
`FUN`	optional function to be used by `as.dist` and `as.simil`. If `NULL`, it is looked up in the method registry. If there is none specified there, `FUN` defaults to `pr_simil2dist` and `pr_dist2simil`, respectively.
`...`	further arguments passed to the proximity function.

Details

The interface is fashioned after dist, but can also compute cross-distances, and allows user extensions by means of registry of all proximity measures (see pr_DB).

Missing values are allowed but are excluded from all computations involving the rows within which they occur. If some columns are excluded in calculating a Euclidean, Manhattan, Canberra or Minkowski distance, the sum is scaled up proportionally to the number of columns used (compare dist in package stats).

Data frames are silently coerced to matrix if all columns are of (same) mode numeric or logical.

Distance measures can be used with simil, and similarity measures with dist. In these cases, the result is transformed accordingly using the specified coercion functions (default: pr\_simil2dist(x) = 1 - abs(x) and pr\_dist2simil(x) = 1 / (1 + x)). Objects of class simil and dist can be converted one in another using as.dist and as.simil, respectively.

Distance and similarity objects can conveniently be subset (see examples). Note that duplicate indexes are silently ignored.

Value

Auto distances/similarities are returned as an object of class dist/simil and cross-distances/similarities as an object of class crossdist/crosssimil.

Author(s)

David Meyer David.Meyer@R-project.org and Christian Buchta Christian.Buchta@wu-wien.ac.at

References

Anderberg, M.R. (1973), Cluster analysis for applications, 359 pp., Academic Press, New York, NY, USA.

Cox, M.F. and Cox, M.A.A. (2001), Multidimensional Scaling, Chapman and Hall.

Sokol, R.S. and Sneath P.H.A (1963), Principles of Numerical Taxonomy, W. H. Freeman and Co., San Francisco.

Examples

### show available proximities
summary(pr_DB)

### get more information about a particular one
pr_DB$get_entry("Jaccard")

### binary data
x <- matrix(sample(c(FALSE, TRUE), 8, rep = TRUE), ncol = 2)
dist(x, method = "Jaccard")

### for real-valued data
dist(x, method = "eJaccard")

### for positive real-valued data
dist(x, method = "fJaccard")

### cross distances
dist(x, x, method = "Jaccard")

### pairwise (diagonal)
dist(x, x, method = "Jaccard", 
	 pairwise = TRUE)

### this is the same but less efficient
as.matrix(stats::dist(x, method = "binary"))

### numeric data
x <- matrix(rnorm(16), ncol = 4)

## test inheritance of names
rownames(x) <- LETTERS[1:4]
colnames(x) <- letters[1:4]
dist(x)
dist(x, x)

## custom distance function
f <- function(x, y) sum(x * y)
dist(x, f)

## working with lists
z <- unlist(apply(x, 1, list), recursive = FALSE)
(d <- dist(z))
dist(z, z)

## subsetting
d[[1:2]]
subset(d, c(1,3,4))
d[[c(1,2,2)]]	    # duplicate index gets ignored

## transformations and self-proximities
as.matrix(as.simil(d, function(x) exp(-x)), diag = 1)

## row and column indexes
row.dist(d)
col.dist(d)

proxy

Distance and Similarity Measures

v0.4-25

GPL-2

Authors

David Meyer [aut, cre], Christian Buchta [aut]

Initial release