get.tau.typed.bootstrap {IDSpatialStats} | R Documentation |
runs bootstrapping for get.tau.typed
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.
|
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()
,
get.tau.bootstrap()
,
get.tau.ci()
,
get.tau.permute()
,
get.tau.typed()
,
get.tau.typed.permute()
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)