Raw File
title: "Credible Intervals (CI)"
    toc: true
    fig_width: 10.08
    fig_height: 6
tags: [r, bayesian, posterior, test, ci, credible interval]
vignette: >
  %\VignetteIndexEntry{Credible Intervals (CI)}
  chunk_output_type: console
bibliography: bibliography.bib
csl: apa.csl

This vignette can be referred to by citing the package:

- Makowski, D., Ben-Shachar, M. S., \& L├╝decke, D. (2019). *bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework*. Journal of Open Source Software, 4(40), 1541. https://doi.org/10.21105/joss.01541


```{r message=FALSE, warning=FALSE, include=FALSE}
options(knitr.kable.NA = '')

if (!requireNamespace("ggplot2", quietly = TRUE) || 
    !requireNamespace("logspline", quietly = TRUE) ||
    !requireNamespace("dplyr", quietly = TRUE)) {
  knitr::opts_chunk$set(eval = FALSE)


# What is a *Credible* Interval?

Credible intervals are an important concept in Bayesian statistics. Its core
purpose is to describe and summarise **the uncertainty** related to the unknown
parameters you are trying to estimate. In this regard, it could appear as quite
similar to the frequentist **Confidence Intervals**. However, while their goal
is similar, **their statistical definition and meaning is very different**.
Indeed, while the latter is obtained through a complex algorithm full of
rarely-tested assumptions and approximations, the credible intervals are fairly
straightforward to compute.

As the Bayesian inference returns a **distribution** of possible effect values
(the posterior), the credible interval is just the range containing a particular
percentage of probable values. For instance, the 95\% credible interval is
simply the central portion of the posterior distribution that contains 95\% of
the values.

Note how this drastically improve the interpretability of the Bayesian interval
compared to the frequentist one. Indeed, the Bayesian framework allows us to say
*"given the observed data, the effect has 95% probability of falling within this range"*, compared to the less straightforward, frequentist alternative (the 95\%
**Confidence* Interval**) would be "*there is a 95\% probability that when
computing a confidence interval from data of this sort, the effect falls within
this range*".

# 89\% vs. 95\% CI

Using 89\% is another popular choice, and used to be the default for a long time
([read here the story of the change](https://github.com/easystats/bayestestR/discussions/250)). 
How did it start?

Naturally, when it came about choosing the CI level to report by default,
**people started using 95\%**, the arbitrary convention used in the
**frequentist** world. However, some authors suggested that 95\% might not be
the most appropriate for Bayesian posterior distributions, potentially lacking
stability if not enough posterior samples are drawn [@kruschke2014doing].

The proposition was to use 90\% instead of 95\%. However, recently, McElreath
(2014, 2018) suggested that if we were to use arbitrary thresholds in the first
place, why not use 89\%? Moreover, 89 is the highest **prime number** that
does not exceed the already unstable 95\% threshold. What does it have to do
with anything? *Nothing*, but it reminds us of the total arbitrariness of these
conventions [@mcelreath2018statistical].

Thus, CIs computed with 89\% intervals (`ci = 0.89`), are deemed
to be more stable than, for instance, 95\% intervals [@kruschke2014doing]. An
effective sample size (ESS; see
of at least 10.000 is recommended if one wants to compute precise 95\% intervals (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).

However, 95\% has some [**advantages too**](https://easystats.github.io/blog/posts/bayestestr_95/). 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.

To add to the mess, some other software use different default, such as for instance 90\%. Ultimately, **you as the user should make an informed decision**, based on your needs and goals, and justify your choice.

# Different types of CIs

The reader might notice that `bayestestR` provides **two methods** to compute
credible intervals, the **Highest Density Interval (HDI)** (`hdi()`) and the
**Equal-tailed Interval (ETI)** (`eti()`). These methods can also be changed via
the `method` argument of the `ci()` function. What is the difference? Let's see:

```{r warning=FALSE, message=FALSE}

# Generate a normal distribution
posterior <- distribution_normal(1000)

# Compute HDI and ETI
ci_hdi <- ci(posterior, method = "HDI")
ci_eti <- ci(posterior, method = "ETI")

# Plot the distribution and add the limits of the two CIs
posterior %>% 
  estimate_density(extend=TRUE) %>% 
  ggplot(aes(x = x, y = y)) +
  geom_area(fill = "orange") +
  theme_classic() +
  # HDI in blue
  geom_vline(xintercept = ci_hdi$CI_low, color = "royalblue", size = 3) +
  geom_vline(xintercept = ci_hdi$CI_high, color = "royalblue", size = 3) +
  # Quantile in red
  geom_vline(xintercept = ci_eti$CI_low, color = "red", size = 1) +
  geom_vline(xintercept = ci_eti$CI_high, color = "red", size = 1)

> **These are exactly the same...**

But is it also the case for other types of distributions?

```{r warning=FALSE, message=FALSE}
# Generate a beta distribution
posterior <- distribution_beta(1000, 6, 2)

# Compute HDI and Quantile CI
ci_hdi <- ci(posterior, method = "HDI")
ci_eti <- ci(posterior, method = "ETI")

# Plot the distribution and add the limits of the two CIs
posterior %>% 
  estimate_density(extend = TRUE) %>% 
  ggplot(aes(x = x, y = y)) +
  geom_area(fill = "orange") +
  theme_classic() +
  # HDI in blue
  geom_vline(xintercept = ci_hdi$CI_low, color = "royalblue", size = 3) +
  geom_vline(xintercept = ci_hdi$CI_high, color = "royalblue", size = 3) +
  # ETI in red
  geom_vline(xintercept = ci_eti$CI_low, color = "red", size = 1) +
  geom_vline(xintercept = ci_eti$CI_high, color = "red", size = 1)

> **The difference is strong with this one.**

Contrary to the **HDI**, for which all points within the interval have a higher
probability density than points outside the interval, the **ETI** is
**equal-tailed**. This means that a 90\% interval has 5\% of the distribution on
either side of its limits. It indicates the 5th percentile and the 95th
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 *not* change when transformations are
applied to the distribution (for instance, for log-odds to probabilities
transformation): 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. Thus, for instance, if exponentiated credible
intervals are required, it is recommended to calculate the ETI.

# The Support Interval

Unlike the HDI and the ETI, which look at the posterior distribution, the
**Support Interval (SI)** provides information regarding the change in the
credibility of values from the prior to the posterior - in other words, it
indicates which values of a parameter have gained support by the observed
data by some factor greater or equal to *k* [@wagenmakers2018SI].

```{r warning=FALSE, message=FALSE}
prior <- distribution_normal(1000, mean = 0, sd = 1)
posterior <- distribution_normal(1000, mean = .5, sd = .3)

si_1 <- si(posterior, prior, BF = 1)
si_3 <- si(posterior, prior, BF = 3)

ggplot(mapping = aes(x = x, y = y)) +
  theme_classic() +
  # The posterior
  geom_area(fill = "orange",
            data = estimate_density(posterior, extend = TRUE)) +
  # The prior
  geom_area(color = "black", fill = NA, size = 1, linetype = "dashed",
            data = estimate_density(prior, extend = TRUE)) +
  # BF = 1 SI in blue
  geom_vline(xintercept = si_1$CI_low, color = "royalblue", size = 1) +
  geom_vline(xintercept = si_1$CI_high, color = "royalblue", size = 1) +
  # BF = 3 SI in red
  geom_vline(xintercept = si_3$CI_low, color = "red", size = 1) +
  geom_vline(xintercept = si_3$CI_high, color = "red", size = 1)

Between the blue lines are values that received *some* support by the data (this
is a $BF = 1~SI$), while between the red lines are values that received at least
*moderate* support ($BF = 3~SI$) by the data.

From the perspective of the Savage-Dickey Bayes factor, testing against a point
null hypothesis for any value within the Support Interval will yield a Bayes
factor smaller than 1/`BF`.

# References
back to top