infer2sigma: Infer the radius of Wright's genetic neighborhood from...

In Andrew-Shirk/sGD: Spatially explicit estimation of genetic diversity indices and Wright's neighborhood size (NS)

Description

Infer the radius of Wright's genetic neighborhood from codominant marker genotypes. The correct radius is equal to 2 sigma, where sigma is the mean parent-offspring dispersal distance.

Usage

infer2sigma(genind_obj, xy, dist.mat, radii, min_N, max_N = NULL)

Arguments

genind_obj	A genind object (created by the adegenet package function import2genind or related methods) containing individual genotypes. The order of the individuals must be the same as the order in the xy and dist.mat inputs below.
xy	A dataframe containing 3 columns in the following order: individual IDs, X coordinates, and Y coordinates. The order of the rows must match the order in the genind_obj and dist.mat inputs.
dist.mat	An NxN (N= sample size) matrix of pairwise landscape distances (Euclidean or effective). The distmat function in the sGD package may be used to produce Euclidean and cost-weighted distance matrices. The order of the rows and columns in the matrix must match the order in the xy and genind_obj inputs.
radii	A vector of neighborhood radii at which to evaluate the evidence for the correct neighborhood radius.
min_N	The minimum sample size per neighborhood for indices to be calculated. NA is returned for neighborhoods < min_N.
max_N	Optional. The maximum sample size per neighborhood for indices to be calculated. If the number of individuals in the neighborhood exceeds max_N, a sample of size max_N will be used from the neighborhood to compute the metrics and output files specified by the user. Note that if max_N is specified, and the value is too small to be representative of the neighobrhood, the results could differ significantly compared to if all individuals in the neighborhood were used.

Examples

library(sGD)
library(adegenet)

# read in genotypes, locations, and distance matrix
genepop.file <- system.file("extdata","sGD_demo_IBR.gen",package="sGD") 
xy = read.csv(system.file("extdata","sGD_demo_xy.csv",package="sGD"))
dist.mat <- as.matrix(read.csv(system.file("extdata","sGD_demo_cdmat.csv",package="sGD"),
                               header=FALSE))

# convert genepop to genind (make sure you specify the correct allele code digits - ncode)
genind_obj <- read.genepop(genepop.file,ncode=3L,quiet=TRUE) 

# specify radii to evaluate
radii = c(8000,12000,16000,20000,24000)

# run infer2sigma
est2sigma <- infer2sigma(genind_obj,xy,dist.mat,radii,min_N=20)
radii_summary = aggregate(FIS~radius,est2sigma,median)

Andrew-Shirk/sGD documentation built on May 26, 2019, 6:38 a.m.