# metric.dist: Distance Matrix Computation In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

## Description

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

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

## Usage

 `1` ```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.

## Author(s)

Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es

See also `dist` for multivariate date case and `metric.lp for functional data case`
 ```1 2 3 4 5 6``` ```## Not run: data(iris) d<-metric.dist(iris[,1:4]) matplot(d,type="l",col=as.numeric(iris[,5])) ## End(Not run) ```