# distance: Calculate dissimilarity/distance metrics In ecodist: Dissimilarity-Based Functions for Ecological Analysis

## Description

This function calculates a variety of dissimilarity or distance metrics. Although it duplicates the functionality of dist() and bcdist(), it is written in such a way that new metrics can easily be added. distance() was written for extensibility and understandability, and is not necessarily an efficient choice for use with large matrices.

## Usage

 `1` ```distance(x, method = "euclidean", sprange=NULL, spweight=NULL, icov) ```

## Arguments

 `x` matrix or data frame with rows as samples and columns as variables (such as species). Distances will be calculated for each pair of rows. `method` Currently 7 dissimilarity metrics can be calculated: "euclidean", "bray-curtis", "manhattan", "mahalanobis" (squared Mahalanobis distance), "jaccard", "difference", "sorensen", "gower", "modgower10" (modified Gower, base 10), "modgower2" (modified Gower, base 2). Partial matching will work for selecting a method. `sprange` Gower dissimilarities offer the option of dividing by the species range. If sprange=NULL no range is used. If sprange is a vector of length nrow(x) it is used for standardizing the dissimilarities. `spweight` Euclidean, Manhattan, and Gower dissimilarities allow weighting. If spweight=NULL, no weighting is used. If spweight="absence", then W=0 if both species are absent and 1 otherwise, thus deleting joint absences. `icov` Optional covariance matrix; only used if method="mahalanobis" since Mahalanobis distance requires calculating the variance-covariance matrix for the entire dataset. Providing icov directly makes it possible to calculate distances for a subset of the full dataset.

## Value

Returns a lower-triangular distance matrix as an object of class "dist".

## Author(s)

Sarah Goslee

`dist`, `bcdist`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```data(iris) iris.bc <- distance(iris[, 1:4], "bray-curtis") # The effect of specifying icov: # calculate Mahalanobis distance for the full iris dataset iris.md <- full(distance(iris[, 1:4], "mahal")) iris.md[1, 2] # Mahalanobis distance between samples 1 and 2 # calculate Mahalanobis for just one species setosa.md <- full(distance(iris[iris\$Species == "setosa", 1:4], "mahal")) setosa.md[1, 2] # Mahalanobis distance between samples 1 and 2 # use the covariance matrix for the full dataset to scale for one species setosa.scaled.md <- full(distance(iris[iris\$Species == "setosa", 1:4], "mahal", icov=var(iris[,1:4]))) setosa.scaled.md[1, 2] # Mahalanobis distance between samples 1 and 2 ```