min_dist: Calculating the minimum distance

In rNeighborGWAS: Testing Neighbor Effects in Marker-Based Regressions

Description

A function to calculate a Euclidian distance including at least one neighbor for all individuals.

Usage

min_dist(smap, grouping = rep(1, nrow(smap)))

Arguments

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.
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.

Value

Return a scalar of the minimum Euclidian distance that allows all individuals to have at least one neighbor.

Author(s)

Yasuhiro Sato ([e-mail])

Examples

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))
pheno <- nei_simu(geno=g,smap=smap,scale=44,grouping=grouping,n_causal=50,pveB=0.4,pve=0.8)

fake_nei <- list()
fake_nei[[1]] <- g
fake_nei[[2]] <- gmap
fake_nei[[3]] <- smap
fake_nei[[4]] <- data.frame(pheno,grouping)
names(fake_nei) <- c("geno","gmap","smap","pheno")

min_s <- min_dist(fake_nei$smap, fake_nei$pheno$grouping)

rNeighborGWAS documentation built on May 10, 2021, 5:07 p.m.