# metric.dist

0th

Percentile

##### Distance Matrix Computation

This function computes the distances between the rows of a data matrix by using the specified distance measure.

Keywords
cluster
##### Usage
metric.dist(x, y = NULL, method = "euclidean", p = 2, dscale = 1, ...)
##### Arguments
x

Data frame 1. The dimension is (n1 x m).

y

Data frame 2. The dimension is (n2 x m).

method

The distance measure to be used. This must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski".

p

The power of the Minkowski distance.

dscale

If scale is a numeric, the distance matrix is divided by the scale value. If scale is a function (as the mean for example) the distance matrix is divided by the corresponding value from the output of the function.

Further arguments passed to dist function.

##### Details

This function returns a distance matrix by using dist function. The matrix dimension is (n1 x n1) if y=NULL, (n1 x n2) otherwise.

See also dist for multivariate date case and metric.lp for functional data case

• metric.dist
##### Examples
# NOT RUN {
data(iris)
d<-metric.dist(iris[,1:4])
matplot(d,type="l",col=as.numeric(iris[,5]))
# }

Documentation reproduced from package fda.usc, version 2.0.1, License: GPL-2

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