get.tau.typed.bootstrap: runs bootstrapping for 'get.tau.typed'

In IDSpatialStats: Estimate Global Clustering in Infectious Disease

Description

runs bootstrapping for get.tau.typed

Usage

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

Arguments

posmat	a matrix with columns type, x and y
typeA	the "from" type that we are interested in, -1 is wildcard
typeB	the "to" type that we are interested i, -1 is wildcard
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	what type of points are included in the comparison set. "representative" if comparison set is representative of the underlying population "independent" if comparison set is cases/events coming from an independent process
data.frame	logical indicating whether to return results as a data frame (default = TRUE)

Value

tau values for all the distances we looked at

Author(s)

Justin Lessler and Henrik Salje

See Also

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

Examples

data(DengueSimulationR02)

r.max<-seq(20,1000,20)
r.min<-seq(0,980,20)
r.mid<-(r.max+r.min)/2

# Lets see if there's a difference in spatial dependence between those that occurred 
# late versus early in the outbreak

type <- 2 - (DengueSimR02[,"time"] < 120)
tmp <- cbind(DengueSimR02, type=type)

typed.tau <- get.tau.typed(tmp, typeA=1, typeB=2, r=r.max, r.low=r.min, 
                           comparison.type = "independent")

typed.tau.type.bs <- get.tau.typed.bootstrap(tmp, typeA=1, typeB=2, r=r.max, r.low=r.min, 
                                             boot.iter=100, comparison.type = "independent")

ci <- apply(typed.tau.type.bs[,-(1:2)], 1, quantile, probs=c(0.025,0.975))

plot(r.mid, typed.tau$tau, log="y",
     ylim=c(0.1,4), cex.axis=1.25,
     xlab="Distance (m)", ylab="Tau",
     cex.main=0.9, lwd=2, type="n")
abline(h=1,lty=1)
lines(r.mid,typed.tau$tau,pch=20,col=1,lwd=3)
lines(r.mid, ci[1,] , lty=2)
lines(r.mid, ci[2,] , lty=2)

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