Highest Density Intervals from Draws

Computes highest density intervals (HDIs) for a numeric sample. The HDI is defined as the shortest interval containing a target probability mass.

Usage

hdi(x, ...)

# Default S3 method
hdi(x, probs = c(0.5, 0.99), ...)

Arguments

x: A numeric vector of draws.
...: Unused.
probs: Numeric vector of target mass levels. Values in \((0, 1]\) or \([0, 100]\) are accepted. Default is c(0.5, 0.99).

Value

A list with class "koma_hdi" containing:

intervals: Named list of matrices with columns lower and upper, one matrix per probs level.
mode: Sample median of the draws (used as a center reference).
cutoff: Named numeric vector of NA values (not defined for HDI).
mass: Named numeric vector of achieved mass for each level.
probs: Numeric vector of target masses in (0, 1].

Examples

x <- rnorm(1000)
hdi(x, probs = c(0.5, 0.9))
#> ==========
#> level_50
#> --------
#> Median: 0.001
#> Intervals: [-0.626; 0.68]
#> 
#> level_90
#> --------
#> Median: 0.001
#> Intervals: [-1.55; 1.714]
#> 
#> ==========
#> Median, [HDI] 

data("simulated_sem")
set.seed(11)

fit <- estimate(
  ts_data = simulated_sem$ts_data,
  sys_eq = simulated_sem$sys_eq,
  dates = simulated_sem$dates,
  options = list(gibbs = list(ndraws = 10))
)
#> 
#> ── Gibbs Sampler Settings ──────────────────────────────────────────────────────
#> 
#> ── System Wide Settings ──
#> • Number of draws (`ndraws`): 10
#> • Burn-in ratio (`burnin_ratio`): 0.5
#> • Burn-in (`burnin`): 5
#> • Store frequency (`nstore`): 1
#> • Number of saved draws (`nsave`): 5
#> • Tau (`tau`): 1.1
#> 
#> 
#> ── Estimation ──────────────────────────────────────────────────────────────────
hdi_fit <- hdi(fit, probs = c(0.5, 0.9))
names(hdi_fit$intervals)
#> [1] "consumption"     "investment"      "current_account" "manufacturing"  
#> [5] "service"