Distance Matrix Computation

This function computes and returns the distance matrix computed by using the specified distance measure to compute the distances between the rows of a data matrix.

multivariate, cluster
dist(x, method = "euclidean", diag = FALSE, upper = FALSE, p = 2)

as.dist(m, diag = FALSE, upper = FALSE) # S3 method for default as.dist(m, diag = FALSE, upper = FALSE)

# S3 method for dist print(x, diag = NULL, upper = NULL, digits = getOption("digits"), justify = "none", right = TRUE, …)

# S3 method for dist as.matrix(x, …)

a numeric matrix, data frame or "dist" object.
the distance measure to be used. This must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". Any unambiguous substring can be given.
logical value indicating whether the diagonal of the distance matrix should be printed by print.dist.
logical value indicating whether the upper triangle of the distance matrix should be printed by print.dist.
The power of the Minkowski distance.
An object with distance information to be converted to a "dist" object. For the default method, a "dist" object, or a matrix (of distances) or an object which can be coerced to such a matrix using as.matrix(). (Only the lower triangle of the matrix is used, the rest is ignored).
digits, justify
passed to format inside of print().
right, …
further arguments, passed to other methods.

Available distance measures are (written for two vectors \(x\) and \(y\)):

Usual distance between the two vectors (2 norm aka \(L_2\)), \(\sqrt{\sum_i (x_i - y_i)^2}\).
Maximum distance between two components of \(x\) and \(y\) (supremum norm)
Absolute distance between the two vectors (1 norm aka \(L_1\)).
\(\sum_i |x_i - y_i| / |x_i + y_i|\). Terms with zero numerator and denominator are omitted from the sum and treated as if the values were missing. This is intended for non-negative values (e.g., counts): taking the absolute value of the denominator is a 1998 R modification to avoid negative distances.
(aka asymmetric binary): The vectors are regarded as binary bits, so non-zero elements are ‘on’ and zero elements are ‘off’. The distance is the proportion of bits in which only one is on amongst those in which at least one is on.
The \(p\) norm, the \(p\)th root of the sum of the \(p\)th powers of the differences of the components.
Missing values are allowed, and are excluded from all computations involving the rows within which they occur. Further, when Inf values are involved, all pairs of values are excluded when their contribution to the distance gave NaN or NA. 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. If all pairs are excluded when calculating a particular distance, the value is NA. The "dist" method of as.matrix() and as.dist() can be used for conversion between objects of class "dist" and conventional distance matrices. as.dist() is a generic function. Its default method handles objects inheriting from class "dist", or coercible to matrices using as.matrix(). Support for classes representing distances (also known as dissimilarities) can be added by providing an as.matrix() or, more directly, an as.dist method for such a class.


dist returns an object of class "dist". The lower triangle of the distance matrix stored by columns in a vector, say do. If n is the number of observations, i.e., n <- attr(do, "Size"), then for \(i < j \le n\), 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 \(n^2\). The object has the following attributes (besides "class" equal to "dist"):

integer, the number of observations in the dataset.
optionally, contains the labels, if any, of the observations of the dataset.
Diag, Upper
logicals corresponding to the arguments diag and upper above, specifying how the object should be printed.
optionally, the call used to create the object.
optionally, the distance method used; resulting from dist(), the (match.arg()ed) method argument.


Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole. Mardia, K. V., Kent, J. T. and Bibby, J. M. (1979) Multivariate Analysis. Academic Press. Borg, I. and Groenen, P. (1997) Modern Multidimensional Scaling. Theory and Applications. Springer.

See Also

daisy in the https://CRAN.R-project.org/package=cluster package with more possibilities in the case of mixed (continuous / categorical) variables. hclust.

  • dist
  • print.dist
  • format.dist
  • labels.dist
  • as.matrix.dist
  • as.dist
  • as.dist.default
library(stats) require(graphics) x <- matrix(rnorm(100), nrow = 5) dist(x) dist(x, diag = TRUE) dist(x, upper = TRUE) m <- as.matrix(dist(x)) d <- as.dist(m) stopifnot(d == dist(x)) ## Use correlations between variables "as distance" dd <- as.dist((1 - cor(USJudgeRatings))/2) round(1000 * dd) # (prints more nicely) plot(hclust(dd)) # to see a dendrogram of clustered variables ## example of binary and canberra distances. x <- c(0, 0, 1, 1, 1, 1) y <- c(1, 0, 1, 1, 0, 1) dist(rbind(x, y), method = "binary") ## answer 0.4 = 2/5 dist(rbind(x, y), method = "canberra") ## answer 2 * (6/5) ## To find the names labels(eurodist) ## Examples involving "Inf" : ## 1) x[6] <- Inf (m2 <- rbind(x, y)) dist(m2, method = "binary") # warning, answer 0.5 = 2/4 ## These all give "Inf": stopifnot(Inf == dist(m2, method = "euclidean"), Inf == dist(m2, method = "maximum"), Inf == dist(m2, method = "manhattan")) ## "Inf" is same as very large number: x1 <- x; x1[6] <- 1e100 stopifnot(dist(cbind(x, y), method = "canberra") == print(dist(cbind(x1, y), method = "canberra"))) ## 2) y[6] <- Inf #-> 6-th pair is excluded dist(rbind(x, y), method = "binary" ) # warning; 0.5 dist(rbind(x, y), method = "canberra" ) # 3 dist(rbind(x, y), method = "maximum") # 1 dist(rbind(x, y), method = "manhattan") # 2.4
Documentation reproduced from package stats, version 3.4.0, License: Part of R 3.4.0

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