dist  R Documentation 
These functions compute and return the autodistance/similarity matrix between either rows or columns of a matrix/data frame, or a list, as well as the crossdistance matrix between two matrices/data frames/lists.
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, ...)
x 
For 
y 

method 
a function, a registry entry, or a mnemonic string referencing the
proximity measure. A list of all available measures can be obtained
using 
diag 
logical value indicating whether the diagonal of the
distance/similarity matrix should be printed by
In the context of 
upper 
logical value indicating whether the upper triangle of the
distance/similarity matrix should be printed by

pairwise 
logical value indicating whether distances should be
computed for the pairs of 
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 
... 
further arguments passed to the proximity function. 
The interface is fashioned after dist
, but can
also compute crossdistances, 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.
Auto distances/similarities are returned as an object of class dist
/simil
and
crossdistances/similarities as an object of class crossdist
/crosssimil
.
David Meyer David.Meyer@Rproject.org and Christian Buchta Christian.Buchta@wuwien.ac.at
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.
dist
for compatibility information, and
pr_DB
for the proximity data base.
### 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 realvalued data dist(x, method = "eJaccard") ### for positive realvalued 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 selfproximities as.matrix(as.simil(d, function(x) exp(x)), diag = 1) ## row and column indexes row.dist(d) col.dist(d)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.