gl.ibd  R Documentation 
This function performs an isolation by distance analysis based on a Mantel test and also produces an isolation by distance plot. If a genlight object with coordinates is provided, then an Euclidean and genetic distance matrices are calculated.'
gl.ibd(
x = NULL,
distance = "Fst",
coordinates = "latlon",
Dgen = NULL,
Dgeo = NULL,
Dgeo_trans = "Dgeo",
Dgen_trans = "Dgen",
permutations = 999,
plot.out = TRUE,
paircols = NULL,
plot_theme = theme_dartR(),
save2tmp = FALSE,
verbose = NULL
)
x 
Genlight object. If provided a standard analysis on Fst/1Fst and log(distance) is performed [required]. 
distance 
Type of distance that is calculated and used for the analysis. Can be either population based 'Fst' [stamppFst], 'D' [stamppNeisD] or individual based 'propShared', [gl.propShared], 'euclidean' [gl.dist.ind, method='Euclidean'] [default "Fst"]. 
coordinates 
Can be either 'latlon', 'xy' or a two column data.frame
with column names 'lat','lon', 'x', 'y'). Coordinates are provided via

Dgen 
Genetic distance matrix if no genlight object is provided [default NULL]. 
Dgeo 
Euclidean distance matrix if no genlight object is provided [default NULL]. 
Dgeo_trans 
Transformation to be used on the Euclidean distances. See Dgen_trans [default "Dgeo"]. 
Dgen_trans 
You can provide a formula to transform the genetic
distance. The transformation can be applied as a formula using Dgen as the
variable to be transformed. For example: 
permutations 
Number of permutations in the Mantel test [default 999]. 
plot.out 
Should an isolation by distance plot be returned [default TRUE]. 
paircols 
Should pairwise dots colored by 'pop'ulation/'ind'ividual pairs [default 'pop']. You can color pairwise individuals by pairwise population colors. 
plot_theme 
Theme for the plot. See details for options [default theme_dartR()]. 
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]. 
Currently pairwise Fst and D between populations and 1propShared and Euclidean distance between individuals are implemented. Coordinates are expected as lat long and converted to Google Earth Mercator projection. If coordinates are already projected, provide them at the x@other$xy slot.
You can provide also your own genetic and Euclidean distance matrices. The function is based on the code provided by the adegenet tutorial (http://adegenet.rforge.rproject.org/files/tutorialbasics.pdf), using the functions mantel (package vegan), stamppFst, stamppNeisD (package StAMPP) and gl.propShared or gl.dist.ind. For transformation you need to have the dismo package installed. As a new feature you can plot pairwise relationship using double colored points (paircols=TRUE). Pairwise relationship can be visualised via populations or individuals, depending which distance is calculated. Please note: Often a problem arises, if an individual based distance is calculated (e.g. propShared) and some individuals have identical coordinates as this results in distances of zero between those pairs of individuals.
If the standard transformation [log(Dgeo)] is used, this results in an infinite value, because of trying to calculate'log(0)'. To avoid this, the easiest fix is to change the transformation from log(Dgeo) to log(Dgeo+1) or you could add some "noise" to the coordinates of the individuals (e.g. + 1m, but be aware if you use lat lon then you rather want to add +0.00001 degrees or so).
Returns a list of the following components: Dgen (the genetic distance matrix), Dgeo (the Euclidean distance matrix), Mantel (the statistics of the Mantel test).
Bernd Gruber (bugs? Post to https://groups.google.com/d/forum/dartr)
Rousset, F. (1997). Genetic differentiation and estimation of gene flow from Fstatistics under isolation by distance. Genetics, 145(4), 12191228.
mantel, stamppFst
#because of speed only the first 100 loci
ibd < gl.ibd(bandicoot.gl[,1:100], Dgeo_trans='log(Dgeo)' ,
Dgen_trans='Dgen/(1Dgen)')
#because of speed only the first 10 individuals)
ibd < gl.ibd(bandicoot.gl[1:10,], distance='euclidean', paircols='pop',
Dgeo_trans='Dgeo')
#only first 100 loci
ibd < gl.ibd(bandicoot.gl[,1:100])
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