distanceMatrix {ClassDiscovery} R Documentation

## Distance Matrix Computation

### Description

This function computes and returns the distance matrix determined by using the specified distance metric to compute the distances between the columns of a data matrix.

### Usage

```distanceMatrix(dataset, metric, ...)
```

### Arguments

 `dataset` A numeric matrix or an `ExpressionSet` `metric` A character string defining the distance metric. This can be `pearson`, `sqrt pearson`, `spearman`, `absolute pearson`, `uncentered correlation`, `weird`, `cosine`, or any of the metrics accepted by the `dist` function. At present, the latter function accepts `euclidean`, `maximum`, `manhattan`, `canberra`, `binary`, or `minkowski`. Any initial substring that uniquely defines one of the metrics will work. `...` Additional parameters to be passed on to `dist`.

### Details

This function differs from `dist` in two ways, both of which are motivated by common practice in the analysis of microarray or proteomics data. First, it computes distances between column vectors instead of between row vectors. In a typical microarray experiment, the data is organized so the rows represent genes and the columns represent different biological samples. In many applications, relations between the biological samples are more interesting than relationships between genes. Second, `distanceMatrix` adds additional distance metrics based on correlation.

• `pearson`The most common metric used in the microarray literature is the `pearson` distance, which can be computed in terms of the Pearson correlation coefficient as `(1-cor(dataset))/2`.

• `uncentered correlation`This metric was introduced in the Cluster and TreeView software from the Eisen lab at Stanford. It is computed using the formulas for Pearson correlation, but assuming that both vectors have mean zero.

• `spearman`The `spearman` metric used the same formula, but substitutes the Spearman rank correlation for the Pearson correlation.

• `absolute pearson`The `absolute pearson` metric used the absolute correlation coefficient; i.e., `(1-abs(cor(dataset)))`.

• `sqrt pearson`The `sqrt pearson` metric used the square root of the pearson distance metric; i.e., `sqrt(1-cor(dataset))`.

• `weird`The `weird` metric uses the Euclidean distance between the vectors of correlation coefficients; i.e., dist(cor(dataset)).

### Value

A distance matrix in the form of an object of class `dist`, of the sort returned by the `dist` function or the `as.dist` function.

### BUGS

It would be good to have a better name for the `weird` metric.

### Author(s)

Kevin R. Coombes krc@silicovore.com

`dist`, `as.dist`

### Examples

```dd <- matrix(rnorm(100*5, rnorm(100)), nrow=100, ncol=5)
distanceMatrix(dd, 'pearson')
distanceMatrix(dd, 'euclid')
distanceMatrix(dd, 'sqrt')
distanceMatrix(dd, 'weird')
rm(dd) # cleanup
```

[Package ClassDiscovery version 3.4.0 Index]