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

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.

## Value

Pairwise distances between individuals present in the genind 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.

## 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.

prevosti.dist, bitwise.dist (for SNP data)

Zhian N. Kamvar

## Examples


# A simple example. Let's analyze the mean distance among populations of A.
# euteiches.

data(Aeut)
mean(diss.dist(popsub(Aeut, 1)))
#> [1] 9.503866
# \dontrun{
mean(diss.dist(popsub(Aeut, 2)))
#> [1] 8.848939
mean(diss.dist(Aeut))
#> [1] 20.44299
# }