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, ...)"dist" object."euclidean", "maximum", "manhattan",
"canberra", "binary" or "minkowski".
Any unambiguous substring can be given.print.dist.print.dist."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).format inside of
print().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"):
diag
and upper above, specifying how the object should be printed.call used to create the
object.dist(), the (match.arg()ed) method
argument.euclidean:
maximum:
manhattan:
canberra: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:
minkowski:
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.
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.
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
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