disscosangle {hopach} | R Documentation

## Functions to compute pair-wise distances

### Description

Given a matrix `X`, these functions compute the `nrow(X)` by `nrow{X}` matrix of pair-wise distances between all variables (rows) in `X`, across all observations (columns) of `X`. Each function uses a different distance metric, i.e. definition of what it means for two variables to be similar.

### Usage

    disscosangle(X, na.rm = TRUE)

    disseuclid(X, na.rm = TRUE)

    disscor(X, na.rm = TRUE)

    dissabscosangle(X, na.rm = TRUE)

    dissabseuclid(X, na.rm = TRUE)

    dissabscor(X, na.rm = TRUE)

    vdisscosangle(X, y, na.rm = TRUE)

    vdisseuclid(X, y, na.rm = TRUE)

    vdisscor(X, y, na.rm = TRUE)

    vdissabscosangle(X, y, na.rm = TRUE)

    vdissabseuclid(X, y, na.rm = TRUE)

    vdissabscor(X, y, na.rm = TRUE)

### Arguments

`X` | A numeric data matrix. Each column corresponds to an observation, and each row corresponds to a variable. In the gene expression context, observations are arrays and variables are genes. All values must be numeric. Missing values are ignored.
`na.rm` | Indicator of whether to remove missing values (i.e. only compute distance over non-missing observations).
`y` | A numeric data vector of length `ncol(X)`.

### Details

Different choices of distance metric are discussed in the references. Briefly, Euclidean distance (`disseuclid`) defines two variables to be close if they are similar in magnitude across observations. Correlation distance (`disscor`), in contrast, defines similarity to mean having the same pattern, but not necessarily the same magnitude. Cosine-angle (`disscosangle`) distance is a correlation distance that also accounts for magnitude. Cosine-angle distance is also known as uncentered correlation distance. The distance metrics with 'abs' in their names are absolute versions of each metric; the absolute value is applied to the data before computing the distance.

### Value

A numeric `nrow(X)` by `nrow{X}` matrix of pair-wise distances between all variables (rows) in `X`. For the vector versions (e.g. `vdisscosangle`), a numeric vector of `nrow(X)` pair-wise distances between each variable (row) in `X` and the vector `y`.

### Author(s)

Katherine S. Pollard <kpollard@soe.ucsc.edu> and Mark J. van der Laan <laan@stat.berkeley.edu>

### References

van der Laan, M.J. and Pollard, K.S. A new algorithm for hybrid hierarchical clustering with visualization and the bootstrap. Journal of Statistical Planning and Inference, 2003, 117, pp. 275-303.

http://www.stat.berkeley.edu/~laan/Research/Research_subpages/Papers/hopach.pdf

http://www.bepress.com/ucbbiostat/paper107/

http://www.stat.berkeley.edu/~laan/Research/Research_subpages/Papers/jsmpaper.pdf

### See Also

`distancematrix`

### Examples

    data<-matrix(rnorm(50),nr=5)
    disscosangle(data)

[Package hopach version 1.0.2 Index]