stats (version 3.3)

dist: Distance Matrix Computation

Description

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.

Usage

dist(x, method = "euclidean", diag = FALSE, upper = FALSE, p = 2)
as.dist(m, diag = FALSE, upper = FALSE)
## S3 method for class 'default':
as.dist(m, diag = FALSE, upper = FALSE)
## S3 method for class 'dist':
print(x, diag = NULL, upper = NULL,
      digits = getOption("digits"), justify = "none",
      right = TRUE, ...)
## S3 method for class 'dist':
as.matrix(x, \dots)

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.

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.

concept

dissimilarity

Details

Available distance measures are (written for two vectors $x$ and $y$): [object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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.

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.

See Also

daisy in the cluster package with more possibilities in the case of mixed (continuous / categorical) variables. hclust.

Examples

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