get.tau.ci {IDSpatialStats}

Bootstrap confidence interval for the get.tau values

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(), get.tau.bootstrap(), get.tau.permute(), get.tau.typed(), get.tau.typed.bootstrap(), get.tau.typed.permute()

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")