get.tau.bootstrap: Bootstrap 'get.tau' values.

In IDSpatialStats: Estimate Global Clustering in Infectious Disease

Description

Runs get.tau on multiple bootstraps of the data. Is formulated such that the relationship between points and themselves will not be calculated

Usage

get.tau.bootstrap(
  posmat,
  fun,
  r = 1,
  r.low = rep(0, length(r)),
  boot.iter,
  comparison.type = "representative",
  data.frame = TRUE
)

Arguments

posmat	a matrix appropriate for input to get.tau
fun	a function appropriate as input to get.pi
r	the series of spatial distances wer are interested in
r.low	the low end of each range....0 by default
boot.iter	the number of bootstrap iterations
comparison.type	the comparison type to pass as input to get.pi
data.frame	logical indicating whether to return results as a data frame (default = TRUE)

Value

a matrix containing all bootstrapped values of tau for each distance interval

Author(s)

Justin Lessler and Henrik Salje

See Also

Other get.tau: get.tau.ci(), get.tau.permute(), get.tau.typed.bootstrap(), get.tau.typed.permute(), get.tau.typed(), get.tau()

Examples

#compare normally distributed with uniform points
x<-cbind(1,runif(100,-100,100), runif(100,-100,100))
x<-rbind(x, cbind(2,rnorm(100,0,20), rnorm(100,0,20)))
colnames(x) <- c("type","x","y")

fun<-function(a,b) {
    if(a[1]!=2) return(3)
    if (b[1]==2) return(1)
    return(2)
}

r.max<-seq(10,100,10)
r.min<-seq(0,90,10)
r.mid <- (r.max+r.min)/2

tau<-get.tau(x,fun,r=r.max,r.low=r.min)
tau.boot<-get.tau.bootstrap(x,fun,r=r.max,r.low=r.min,boot.iter=50)

tau.ci<-apply(tau.boot[,-(1:2)],1,quantile,probs=c(0.25,0.75))

plot(r.mid, tau$tau ,ylim=c(min(tau.ci),max(tau.ci)), type="l", log="y")
lines(c(0,100),c(1,1), lty=3, col="grey")
lines(r.mid, tau.ci[1,] , lty=2)
lines(r.mid, tau.ci[2,] , lty=2)

IDSpatialStats documentation built on Aug. 9, 2021, 9:08 a.m.