calc.hpd: Calculate HPD of aggregated waiting time distributions

In gilesjohnr/hmob: Tools for analyzing human mobility data

Description

This function calculates the highest posterior density (HPD) interval for the aggregated waiting time distributions returned by the calc.wait.time function. The function calculates the maximum of each aggregated probability distribution along with the HPD intervals given by ci. Code adapted from the hpd function in the TeachingDemos package.

Usage

calc.hpd(x, ci = 0.95)

Arguments

x	aggregated waiting time distributions for each district (output from calc.wait.time)
ci	a scalar or vector giving the HPD intervals to be calculated (default = 0.95)

Value

a dataframe

Author(s)

John Giles

See Also

Other simulation: calc.prop.inf(), calc.prop.remain(), calc.timing.magnitude(), calc.wait.time(), decay.func(), get.age.beta(), get.beta.params(), sim.TSIR.full(), sim.TSIR(), sim.combine.dual(), sim.combine(), sim.gravity.duration(), sim.gravity(), sim.lambda(), sim.pi(), sim.rho(), sim.tau()

Examples

load("./output/simulations/13/influenza.Rdata")

x <- sim$B$wait.time   # basic gravity model
#x <- sim$R$wait.time   # gravity model with duration 

agg <- calc.wait.time(x)
hpd <- calc.hpd(agg, ci=c(0.5, 0.95))

par(mfrow=c(3,4))
for (i in 1:nrow(hpd)) {
     plot(agg[i,], type='l', main=paste("district ", hpd$district[i]), ylab='Waiting time density')
     abline(v=hpd$lo95[i], lty=2, col='red')
     abline(v=hpd$hi95[i], lty=2, col='red')
     abline(v=hpd$max[i], col='red')
}

gilesjohnr/hmob documentation built on Aug. 8, 2020, 1:26 a.m.