kfunc: estimate Ripley's K function

In kbroman/xoi: Tools for Analyzing Crossover Interference

estimate Ripley's K function

Description

estimate the 1-d version of Ripley's K function

Usage

kfunc(
  x,
  d = seq(0, 100, by = 0.1),
  lengths = NULL,
  exclude = 0,
  tol = 0.000001
)

Arguments

x	list with sorted locations of the data
d	values at which to calculate the function
lengths	lengths of segments studied
exclude	distance to exclude
tol	tolerance value

Value

data frame with d, k, and se

See Also

gammacoi(), stahlcoi(), coincidence()

Examples

L <- 103
n <- 2000
map1 <- sim.map(L, n.mar=104, anchor=TRUE, include.x=FALSE, eq=TRUE)
x <- sim.cross(map1, n.ind=n, m=6, type="bc")

xoloc <- find.breaks(x)

d <- seq(0, 100, by=0.1)[-1]
kf <- kfunc(xoloc, d=d, lengths=rep(L, n))

plot(k ~ d, data=kf, type="n", yaxs="i", xaxs="i", las=1,
     ylim=c(0, max(kf$k + kf$se)))
polygon(c(kf$d, rev(kf$d)), c(kf$k + kf$se, rev(kf$k-kf$se)),
        border=NA, col="#add8e650")
lines(k ~ d, data=kf)

kbroman/xoi documentation built on May 1, 2023, 9:35 p.m.