gl.dist.pop {dartR}

Calculates a distance matrix for populations with SNP genotypes in a genlight object

Description

This script calculates various distances between populations based on allele frequencies (SNP genotypes) or frequency of presences in presence-absence data (Euclidean and Fixed-diff distances only).

Usage

gl.dist.pop(
  x,
  method = "euclidean",
  plot.out = TRUE,
  scale = FALSE,
  output = "dist",
  plot_theme = theme_dartR(),
  plot_colors = two_colors,
  save2tmp = FALSE,
  verbose = NULL
)

Arguments

x

Name of the genlight containing the SNP genotypes [required].

method

Specify distance measure [default euclidean].

plot.out

If TRUE, display a histogram of the genetic distances, and a whisker plot [default TRUE].

scale

If TRUE and method='Euclidean', the distance will be scaled to fall in the range [0,1] [default FALSE].

output

Specify the format and class of the object to be returned, dist for a object of class dist, matrix for an object of class matrix [default "dist"].

plot_theme

User specified theme [default theme_dartR()].

plot_colors

Vector with two color names for the borders and fill [default two_colors].

save2tmp

If TRUE, saves any ggplots and listings to the session temporary directory (tempdir) [default FALSE].

verbose

Verbosity: 0, silent or fatal errors; 1, begin and end; 2, progress log ; 3, progress and results summary; 5, full report [default 2 or as specified using gl.set.verbosity].

Details

The distance measure can be one of 'euclidean', 'fixed-diff', 'reynolds', 'nei' and 'chord'. Refer to the documentation of functions described in the the dartR Distance Analysis tutorial for algorithms and definitions.

Value

An object of class 'dist' giving distances between populations

Author(s)

author(s): Arthur Georges. Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

Examples

 ## Not run: 
# SNP genotypes
D <- gl.dist.pop(possums.gl[1:90,1:100], method='euclidean')
D <- gl.dist.pop(possums.gl[1:90,1:100], method='euclidean',scale=TRUE)
#D <- gl.dist.pop(possums.gl, method='nei')
#D <- gl.dist.pop(possums.gl, method='reynolds')
#D <- gl.dist.pop(possums.gl, method='chord')
#D <- gl.dist.pop(possums.gl, method='fixed-diff')
#Presence-Absence data [only 10 individuals due to speed]
D <- gl.dist.pop(testset.gs[1:10,], method='euclidean')

## End(Not run)
res <- gl.dist.pop(platypus.gl)