nei_coval: Calculating neighbor genotypic identity
In rNeighborGWAS: Testing Neighbor Effects in Marker-Based Regressions

Description Usage Arguments Details Value Author(s) References Examples

A function to calculate neighbor genotypic identity, with a given reference scale and a degree of distance decay.

nei_coval(
  geno,
  smap,
  scale,
  alpha = Inf,
  kernel = c("exp", "gaussian"),
  grouping = rep(1, nrow(smap)),
  n_core = 1L
)

`geno`	An individual x marker matrix. Bialleles (i.e., A or a) must be converted into -1 or 1 digit.
`smap`	A matrix showing a spatial map for individuals. The first and second column include spatial points along an x-axis and y-axis, respectively.
`scale`	A numeric scalar indicating the maximum spatial distance between a focal individual and neighbors to define neighbor effects.
`alpha`	An option to set a distance decay coefficient α in a dispersal kernel. Default is set at Inf, meaning no distance decay.
`kernel`	An option to select either `"exp"` or `"gaussian"` for a negative exponential kernel or Gaussian kernel, respectively.
`grouping`	A positive integer vector assigning each individual to a group. This argument can be useful when a "smap" contains different experimental replicates. Default setting means that all individuals are belong to a single group.
`n_core`	No. of cores for a multi-core computation. This does not work for Windows OS. Default is a single-core computation.

Default setting is recommended for alpha and kernel arguments unless spatial distance decay of neighbor effects needs to be modeled. If alpha is not Inf, output variables are weighted by a distance decay from a focal individual to scale. For the type of dispersal kernel in the distance decay, we can choose a negative exponential or Gaussian kernel as a fat-tailed or thin-tailed distribution, respectively. See Nathan et al. (2012) for detailed characteristics of the two dispersal kernels.

A numeric matrix for neighbor covariates, with no. of individuals x markers.

Yasuhiro Sato (sato.yasuhiro.36c@kyoto-u.jp)

Nathan R, Klein E, Robledo-Arnuncio JJ, Revilla E. (2012) Dispersal kernels: review. In: Clobert J, Baguette M, Benton TG, Bullock JM (Eds.), Dispersal Ecology and Evolution. Oxford University Press, pp.186-210.

set.seed(1)
g <- matrix(sample(c(-1,1),100*1000,replace = TRUE),100,1000)
gmap <- cbind(c(rep(1,nrow(g)/2),rep(2,nrow(g)/2)),c(1:ncol(g)))
x <- runif(nrow(g),1,100)
y <- runif(nrow(g),1,100)
smap <- cbind(x,y)
grouping <- c(rep(1,nrow(g)/2), rep(2,nrow(g)/2))

g_nei <- nei_coval(g,smap,44,grouping = grouping)