dartR (version 2.9.9.5)

gl.dist.pop: 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
)

Value

An object of class 'dist' giving distances between populations

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].

Author

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

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.

Examples

 if (FALSE) {
# 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')
}
res <- gl.dist.pop(platypus.gl)