get.tau.ci: Bootstrap confidence interval for the 'get.tau' values

In IDSpatialStats: Estimate Global Clustering in Infectious Disease

Description

Wrapper to get.tau.bootstrap that takes care of calulating the confidence intervals based on the bootstrapped values

Usage

get.tau.ci(
  posmat,
  fun,
  r = 1,
  r.low = rep(0, length(r)),
  boot.iter = 1000,
  comparison.type = "representative",
  ci.low = 0.025,
  ci.high = 0.975,
  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 we 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 to get.tau
ci.low	the low end of the ci...0.025 by default
ci.high	the high end of the ci...0.975 by default
data.frame	logical indicating whether to return results as a data frame (default = TRUE)

Value

a data frame with the point estimate of tau and its low and high confidence interval at each distance

Author(s)

Justin Lessler and Henrik Salje

See Also

Other get.tau: get.tau.bootstrap(), 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.ci(x,fun,r=r.max,r.low=r.min,boot.iter=50)

plot(r.mid, tau$pt.est, ylim=c(1/max(tau[,3:5]), max(tau[,3:5])), type="l", log="y",
     xlab="Distance", ylab="Tau")
lines(r.mid, tau$ci.low , lty=2)
lines(r.mid, tau$ci.high, lty=2)
lines(c(0,100),c(1,1), lty=3, col="grey")

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