# metric.dist

##### 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

See also `dist`

for multivariate date case and
`metric.lp for functional data case`

##### 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*