# dist

0th

Percentile

##### 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.

Keywords
multivariate, cluster
##### Usage
dist(x, method = "euclidean", diag = FALSE, upper = FALSE, p = 2)
as.dist(m, diag = FALSE, upper = FALSE)
"as.dist"(m, diag = FALSE, upper = FALSE)
"print"(x, diag = NULL, upper = NULL, digits = getOption("digits"), justify = "none", right = TRUE, ...)
"as.matrix"(x, ...)
##### Arguments
x
a numeric matrix, data frame or "dist" object.
method
the distance measure to be used. This must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". Any unambiguous substring can be given.
diag
logical value indicating whether the diagonal of the distance matrix should be printed by print.dist.
upper
logical value indicating whether the upper triangle of the distance matrix should be printed by print.dist.
p
The power of the Minkowski distance.
m
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.
##### Details

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

euclidean:
Usual distance between the two vectors (2 norm aka $L_2$), $sqrt(sum((x_i - y_i)^2))$.

maximum:
Maximum distance between two components of $x$ and $y$ (supremum norm)

manhattan:
Absolute distance between the two vectors (1 norm aka $L_1$).

canberra:
$sum(|x_i - y_i| / |x_i + y_i|)$. 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.

binary:
(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.

minkowski:
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.

##### Value

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"):
Size
integer, the number of observations in the dataset.
Labels
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.
call
optionally, the call used to create the object.
method
optionally, the distance method used; resulting from dist(), the (match.arg()ed) method argument.

##### References

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.

daisy in the \href{https://CRAN.R-project.org/package=#1}{\pkg{#1}}clustercluster package with more possibilities in the case of mixed (continuous / categorical) variables. hclust.

##### Aliases
• dist
• print.dist
• format.dist
• labels.dist
• as.matrix.dist
• as.dist
• as.dist.default
##### Examples
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 3.2.2, License: Part of R 3.2.2

### Community examples

Looks like there are no examples yet.