gdist: Generalized Distance Matrix Computation

In GMD: Generalized Minimum Distance of distributions

Description

gdist computes and returns the distance matrix computed by using user-defined distance measure.

Usage

gdist(x,method="euclidean",MoreArgs=NULL,diag=FALSE,upper=FALSE)

is.dist(d)

Arguments

x	a numeric matrix, data frame or ‘dist’ object.
method	the distance measure to be used. This can either be one of the methods used in dist (see help("dist", package="stats")) or "correlation", "correlation.of.observations" and "correlation.of.variables". In addition, user-defined distance measure are also allowed, which returns a dist object and should at least have attributes "Size" and "Labels".
MoreArgs	a list of other arguments to be passed to gdist.
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.
d	an R object.

Details

is.dist tests if its argument is a ‘dist’ object.

The distance (or dissimilarity) function (FUN) can be any distance measure applied to x. For instance, "euclidean", "maximum", "manhattan","canberra", "binary", "minkowski", "correlation.of.variables", "correlation.of.observations" or gmdm. "correlation.of.variables" computes the correlation distance of the variables (the columns); all the other compute the distances between the observations (the rows) of a data matrix.

Value

gdist returns an object of ‘dist’.
is.dist returns a logical value whether an object is ‘dist’.

Examples

## load library
require("GMD")
require(cluster)

## compute distance using Euclidean metric (default)
data(ruspini)
x <- gdist(ruspini)

## see a dendrogram result by hierarchical clustering
dev.new(width=12, height=6)
plot(hclust(x),
     main="Cluster Dendrogram of Ruspini data",
     xlab="Observations")

## convert to a distance matrix
m <- as.matrix(x)

## convert from a distance matrix
d <- as.dist(m)
stopifnot(d == x)

## Use correlations between variables "as distance"
data(USJudgeRatings)
dd <- gdist(x=USJudgeRatings,method="correlation.of.variables")
dev.new(width=12, height=6)
plot(hclust(dd),
     main="Cluster Dendrogram of USJudgeRatings data",
     xlab="Variables")

Example output

Loading required package: cluster
dev.new(): using pdf(file="Rplots1.pdf")

GMD documentation built on May 29, 2017, 10:41 a.m.