# diss.dist: Calculate a distance matrix based on relative dissimilarity In poppr: Genetic Analysis of Populations with Mixed Reproduction

## Description

diss.dist uses the same discrete dissimilarity matrix utilized by the index of association (see `ia` for details). By default, it returns a distance reflecting the number of allelic differences between two individuals. When `percent = TRUE`, it returns a ratio of the number of observed differences by the number of possible differences. Eg. two individuals who share half of the same alleles will have a distance of 0.5. This function can analyze distances for any marker system.

## Usage

 `1` ```diss.dist(x, percent = FALSE, mat = FALSE) ```

## Arguments

 `x` a `genind` object. `percent` `logical`. Should the distance be represented as a percent? If set to `FALSE` (default), the distance will be reflected as the number of alleles differing between to individuals. When set to `TRUE`, These will be divided by the ploidy multiplied by the number of loci. `mat` `logical`. Return a matrix object. Default set to `FALSE`, returning a dist object. `TRUE` returns a matrix object.

## Details

The distance calculated here is quite simple and goes by many names, depending on its application. The most familiar name might be the Hamming distance, or the number of differences between two strings.

## Value

Pairwise distances between individuals present in the genind object.

## Note

When `percent = TRUE`, this is exactly the same as `provesti.dist`, except that it performs better for large numbers of individuals (n > 125) at the cost of available memory.

## Author(s)

Zhian N. Kamvar

`prevosti.dist`, `bitwise.dist` (for SNP data)
 ``` 1 2 3 4 5 6 7 8 9 10``` ```# A simple example. Let's analyze the mean distance among populations of A. # euteiches. data(Aeut) mean(diss.dist(popsub(Aeut, 1))) ## Not run: mean(diss.dist(popsub(Aeut, 2))) mean(diss.dist(Aeut)) ## End(Not run) ```