# 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

 ```1 2 3``` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28``` ```## 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.