hdi: Highest density interval
In ggdistribute: A 'ggplot2' Extension for Plotting Unimodal Distributions

Description Usage Arguments Details Value Examples

This is a function that will calculate the highest density interval from a posterior sample.

1	hdi(x, prob = 0.95, warn = TRUE)

`x`	Numeric vector of a distribution of data, typically a posterior sample
`prob`	Width of the interval from some distribution. Defaults to `0.95`.
`warn`	Option to turn off multiple sample warning message Must be in the range of `[0,1]`.

The default is to calcualte the highest 95 percent interval. It can be used with any numeric vector instead of having to use one of the specific MCMC classes. This function has been adapted from John K. Kruschke (2011). Doing Bayesian Data Analaysis: A Tutorial with R and BUGS.

Numeric range

x <- qnorm(seq(1e-04, .9999, length.out=1001))
hdi_95 <- hdi(x, .95)
hdi_50 <- hdi(x, .50)

hist(x, br=50)
abline(v=hdi_95, col="red")
abline(v=hdi_50, col="green")

x <- exp(seq(pi * (1 - (1/16)), pi, len = 1000))
x <- c(x, rev(x)[-1])
x <- c(-x, x)
plot(sort(x), type="l")
plot(density(x, adjust=0.25))
abline(v=hdi(x, p=.49), col=2)
abline(v=hdi(x, p=.50), col=3)