Compute the **Highest Density Interval (HDI)** of posterior distributions.
All points within this interval have a higher probability density than points
outside the interval. The HDI can be used in the context of uncertainty
characterisation of posterior distributions as **Credible Interval (CI)**.

## Usage

```
hdi(x, ...)
# S3 method for class 'numeric'
hdi(x, ci = 0.95, verbose = TRUE, ...)
# S3 method for class 'data.frame'
hdi(x, ci = 0.95, rvar_col = NULL, verbose = TRUE, ...)
# S3 method for class 'stanreg'
hdi(
x,
ci = 0.95,
effects = c("fixed", "random", "all"),
component = c("location", "all", "conditional", "smooth_terms", "sigma",
"distributional", "auxiliary"),
parameters = NULL,
verbose = TRUE,
...
)
# S3 method for class 'brmsfit'
hdi(
x,
ci = 0.95,
effects = c("fixed", "random", "all"),
component = c("conditional", "zi", "zero_inflated", "all"),
parameters = NULL,
verbose = TRUE,
...
)
# S3 method for class 'get_predicted'
hdi(x, ci = 0.95, use_iterations = FALSE, verbose = TRUE, ...)
```

## Arguments

- x
Vector representing a posterior distribution, or a data frame of such vectors. Can also be a Bayesian model.

**bayestestR**supports a wide range of models (see, for example,`methods("hdi")`

) and not all of those are documented in the 'Usage' section, because methods for other classes mostly resemble the arguments of the`.numeric`

or`.data.frame`

methods.- ...
Currently not used.

- ci
Value or vector of probability of the (credible) interval - CI (between 0 and 1) to be estimated. Default to

`.95`

(`95%`

).- verbose
Toggle off warnings.

- rvar_col
A single character - the name of an

`rvar`

column in the data frame to be processed. See example in`p_direction()`

.- effects
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.

- component
Should results for all parameters, parameters for the conditional model or the zero-inflated part of the model be returned? May be abbreviated. Only applies to brms-models.

- parameters
Regular expression pattern that describes the parameters that should be returned. Meta-parameters (like

`lp__`

or`prior_`

) are filtered by default, so only parameters that typically appear in the`summary()`

are returned. Use`parameters`

to select specific parameters for the output.- use_iterations
Logical, if

`TRUE`

and`x`

is a`get_predicted`

object, (returned by`insight::get_predicted()`

), the function is applied to the iterations instead of the predictions. This only applies to models that return iterations for predicted values (e.g.,`brmsfit`

models).

## Value

A data frame with following columns:

`Parameter`

The model parameter(s), if`x`

is a model-object. If`x`

is a vector, this column is missing.`CI`

The probability of the credible interval.`CI_low`

,`CI_high`

The lower and upper credible interval limits for the parameters.

## Details

Unlike equal-tailed intervals (see `eti()`

) that typically exclude `2.5%`

from each tail of the distribution and always include the median, the HDI is
*not* equal-tailed and therefore always includes the mode(s) of posterior
distributions. While this can be useful to better represent the credibility
mass of a distribution, the HDI also has some limitations. See `spi()`

for
details.

The `95%`

or `89%`

Credible Intervals (CI)
are two reasonable ranges to characterize the uncertainty related to the
estimation (see here
for a discussion about the differences between these two values).

The `89%`

intervals (`ci = 0.89`

) are deemed to be more stable than, for
instance, `95%`

intervals (*Kruschke, 2014*). An effective sample size
of at least 10.000 is recommended if one wants to estimate `95%`

intervals
with high precision (*Kruschke, 2014, p. 183ff*). Unfortunately, the
default number of posterior samples for most Bayes packages (e.g., `rstanarm`

or `brms`

) is only 4.000 (thus, you might want to increase it when fitting
your model). Moreover, 89 indicates the arbitrariness of interval limits -
its only remarkable property is being the highest prime number that does not
exceed the already unstable `95%`

threshold (*McElreath, 2015*).

However, `95%`

has some advantages too. For instance, it
shares (in the case of a normal posterior distribution) an intuitive
relationship with the standard deviation and it conveys a more accurate image
of the (artificial) bounds of the distribution. Also, because it is wider, it
makes analyses more conservative (i.e., the probability of covering 0 is
larger for the `95%`

CI than for lower ranges such as `89%`

), which is a good
thing in the context of the reproducibility crisis.

A `95%`

equal-tailed interval (ETI) has `2.5%`

of the distribution on either
side of its limits. It indicates the 2.5th percentile and the 97.5h
percentile. In symmetric distributions, the two methods of computing credible
intervals, the ETI and the HDI, return similar results.

This is not the case for skewed distributions. Indeed, it is possible that parameter values in the ETI have lower credibility (are less probable) than parameter values outside the ETI. This property seems undesirable as a summary of the credible values in a distribution.

On the other hand, the ETI range does change when transformations are applied to the distribution (for instance, for a log odds scale to probabilities): the lower and higher bounds of the transformed distribution will correspond to the transformed lower and higher bounds of the original distribution. On the contrary, applying transformations to the distribution will change the resulting HDI.

## Note

There is also a `plot()`

-method implemented in the see-package.

## References

Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.

McElreath, R. (2015). Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC.

## Author

Credits go to **ggdistribute** and **HDInterval**.

## Examples

```
library(bayestestR)
posterior <- rnorm(1000)
hdi(posterior, ci = 0.89)
#> [,1] [,2]
#> [1,] -2.096071 1.796661
hdi(posterior, ci = c(0.80, 0.90, 0.95))
#> [,1] [,2]
#> [1,] -2.096071 1.796661
bayestestR::hdi(iris[1:4])
#> Identical densities found along different segments of the distribution,
#> choosing rightmost.
#> Highest Density Interval
#>
#> Parameter | 95% HDI
#> ---------------------------
#> Sepal.Length | [4.60, 7.70]
#> Sepal.Width | [2.20, 3.90]
#> Petal.Length | [1.00, 6.10]
#> Petal.Width | [0.10, 2.30]
bayestestR::hdi(iris[1:4], ci = c(0.80, 0.90, 0.95))
#> Identical densities found along different segments of the distribution,
#> choosing rightmost.
#> Identical densities found along different segments of the distribution,
#> choosing rightmost.
#> Identical densities found along different segments of the distribution,
#> choosing rightmost.
#> Highest Density Interval
#>
#> Parameter | 80% HDI | 90% HDI | 95% HDI
#> ---------------------------------------------------------
#> Sepal.Length | [4.90, 6.90] | [4.40, 6.90] | [4.60, 7.70]
#> Sepal.Width | [2.50, 3.60] | [2.40, 3.80] | [2.20, 3.90]
#> Petal.Length | [1.30, 5.50] | [1.10, 5.80] | [1.00, 6.10]
#> Petal.Width | [0.10, 1.90] | [0.20, 2.30] | [0.10, 2.30]
# \donttest{
model <- suppressWarnings(
rstanarm::stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0)
)
bayestestR::hdi(model)
#> Highest Density Interval
#>
#> Parameter | 95% HDI
#> ----------------------------
#> (Intercept) | [29.21, 49.50]
#> wt | [-6.99, -4.05]
#> gear | [-2.18, 1.68]
bayestestR::hdi(model, ci = c(0.80, 0.90, 0.95))
#> Highest Density Interval
#>
#> Parameter | 80% HDI | 90% HDI | 95% HDI
#> --------------------------------------------------------------
#> (Intercept) | [31.68, 46.67] | [29.21, 47.18] | [29.21, 49.50]
#> wt | [-6.30, -4.23] | [-6.70, -4.11] | [-6.99, -4.05]
#> gear | [-1.53, 1.08] | [-1.89, 1.41] | [-2.18, 1.68]
bayestestR::hdi(emmeans::emtrends(model, ~1, "wt", data = mtcars))
#> Highest Density Interval
#>
#> X1 | 95% HDI
#> ------------------------
#> overall | [-6.99, -4.05]
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 8e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.022 seconds (Warm-up)
#> Chain 1: 0.018 seconds (Sampling)
#> Chain 1: 0.04 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 3e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.022 seconds (Warm-up)
#> Chain 2: 0.019 seconds (Sampling)
#> Chain 2: 0.041 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 3e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.022 seconds (Warm-up)
#> Chain 3: 0.019 seconds (Sampling)
#> Chain 3: 0.041 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 3e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.021 seconds (Warm-up)
#> Chain 4: 0.02 seconds (Sampling)
#> Chain 4: 0.041 seconds (Total)
#> Chain 4:
bayestestR::hdi(model)
#> Highest Density Interval
#>
#> Parameter | 95% HDI
#> ----------------------------
#> (Intercept) | [36.15, 43.24]
#> wt | [-4.81, -1.60]
#> cyl | [-2.37, -0.66]
bayestestR::hdi(model, ci = c(0.80, 0.90, 0.95))
#> Highest Density Interval
#>
#> Parameter | 80% HDI | 90% HDI | 95% HDI
#> --------------------------------------------------------------
#> (Intercept) | [37.05, 41.78] | [36.79, 42.75] | [36.15, 43.24]
#> wt | [-4.22, -2.23] | [-4.53, -1.88] | [-4.81, -1.60]
#> cyl | [-2.07, -0.97] | [-2.20, -0.78] | [-2.37, -0.66]
bf <- BayesFactor::ttestBF(x = rnorm(100, 1, 1))
bayestestR::hdi(bf)
#> Highest Density Interval
#>
#> Parameter | 95% HDI
#> -------------------------
#> Difference | [0.77, 1.19]
bayestestR::hdi(bf, ci = c(0.80, 0.90, 0.95))
#> Highest Density Interval
#>
#> Parameter | 80% HDI | 90% HDI | 95% HDI
#> -------------------------------------------------------
#> Difference | [0.85, 1.12] | [0.82, 1.17] | [0.78, 1.20]
# }
```