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title: "2. Confirmation of Bayesian skills"
    toc: true
    fig_width: 10.08
    fig_height: 6
tags: [r, bayesian, posterior, test]
vignette: >
  %\VignetteIndexEntry{2. Confirmation of Bayesian skills}
  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 , include=FALSE}
if (!requireNamespace("see", quietly = TRUE) ||
  !requireNamespace("dplyr", quietly = TRUE) ||
  !requireNamespace("ggplot2", quietly = TRUE) ||
  !requireNamespace("performance", quietly = TRUE) ||
  !requireNamespace("BayesFactor", quietly = TRUE) ||
  !requireNamespace("rstanarm", quietly = TRUE)) {
  knitr::opts_chunk$set(eval = FALSE)

options(knitr.kable.NA = "")
  comment = ">",
  message = FALSE,
  warning = FALSE,
  out.width = "100%"

options(digits = 2)


Now that [**describing and understanding posterior distributions**](https://easystats.github.io/bayestestR/articles/example1.html)
of linear regressions is not that mysterious to you, we will take one step back
and study some simpler models: **correlations** and ***t*-tests**.

But before we do that, let us take a moment to remind ourselves and appreciate
the fact that **all basic statistical procedures** such as correlations,
*t*-tests, ANOVAs, or chi-square tests **are** linear regressions (we strongly
recommend  [this excellent demonstration](https://lindeloev.github.io/tests-as-linear/)). Nevertheless,
these simple models will provide a good pretext to introduce a few more complex
indices, such as the **Bayes factor**.

## Correlations

### Frequentist version 

Once again, let us begin with a **frequentist correlation** between two
continuous variables, the **width** and the **length** of the sepals of some
flowers. The data is available in `R` as the `iris`  dataset (the same that was
used in the [previous tutorial](https://easystats.github.io/bayestestR/articles/example1.html)).

We will compute a Pearson's correlation test, store the results in an object
called `result`, and then display it:

result <- cor.test(iris$Sepal.Width, iris$Sepal.Length)

As you can see in the output, the test actually compared **two** hypotheses: 
- the **null hypothesis** (*h0*; no correlation), 
- the **alternative hypothesis** (*h1*; a non-null correlation).

Based on the *p*-value, the null hypothesis cannot be rejected: the correlation
between the two variables is **negative but non-significant** ($r = -.12, p > .05$).

### Bayesian correlation 

To compute a Bayesian correlation test, we will need the
[`BayesFactor`](https://richarddmorey.github.io/BayesFactor/) package (you can
install it by running `install.packages("BayesFactor")`). We can then load this
package, compute the correlation using the `correlationBF()` function, and store
the result.
```{r, results='hide'}
result <- correlationBF(iris$Sepal.Width, iris$Sepal.Length)

Now, let us run our `describe_posterior()` function on that:

```{r }

We see again many things here, but the important indices for now are the
**median** of the posterior distribution, `-.11`. This is (again) quite close to
the frequentist correlation. We could, as previously, describe the [**credible interval**](https://easystats.github.io/bayestestR/articles/credible_interval.html),
or the [**ROPE percentage**](https://easystats.github.io/bayestestR/articles/region_of_practical_equivalence.html),
but we will focus here on another index provided by the Bayesian framework, the
**Bayes Factor (BF)**.

### Bayes Factor (BF)

We said previously that a correlation test actually compares two hypotheses, a
null (absence of effect) with an alternative one (presence of an effect). The
[**Bayes factor (BF)**](https://easystats.github.io/bayestestR/articles/bayes_factors.html)
allows the same comparison and determines **under which of these two models the observed data are more probable**: a model with the effect of interest, and a
null model without the effect of interest. So, in the context of our correlation
example, the null hypothesis would be no correlation between the two variables
($h0: \rho = 0$; where $\rho$ stands for Bayesian correlation coefficient),
while the alternative hypothesis would be that there is a correlation
**different** than 0 - positive or negative ($h1: \rho \neq 0$).

We can use `bayesfactor()` to specifically compute the Bayes factor comparing
those models:


We got a *BF* of `0.51`. What does it mean?

Bayes factors are **continuous measures of *relative* evidence**, with a Bayes
factor greater than 1 giving evidence in favour of one of the models (often
referred to as *the numerator*), and a Bayes factor smaller than 1 giving
evidence in favour of the other model (*the denominator*).

> **Yes, you heard that right, evidence in favour of the *null*!**

That's one of the reason why the Bayesian framework is sometimes considered as
superior to the frequentist framework. Remember from your stats lessons, that
the ***p*-value can only be used to reject *h0***, but not *accept* it. With the
**Bayes factor**, you can measure **evidence against - and in favour of - the null**. 
In other words, in the frequentist framework, if the *p*-value is not
significant, we can conclude that **evidence for the effect is absent**, but not
that there is **evidence for the absence of the effect**. In Bayesian framework,
we can do the latter. This is important since sometimes our hypotheses are about
no effect.
BFs representing evidence for the alternative against the null can be reversed
using $BF_{01}=1/BF_{10}$ (the *01* and *10* correspond to *h0* against *h1* and
*h1* against *h0*, respectively) to provide evidence of the null against the
alternative. This improves human readability^[If the effect is really strong,
the BF values can be extremely high. So don't be surprised if you see BF values
that have been log-transformed to make them more human readable.] in cases where
the BF of the alternative against the null is smaller than 1 (i.e., in support
of the null).

In our case, `BF = 1/0.51 = 2`, indicates that the data are **2 times more probable under the null compared to the alternative hypothesis**, which, though favouring the null, is considered only [anecdotal evidence against the null](https://easystats.github.io/effectsize/reference/interpret_bf.html).

We can thus conclude that there is **anecdotal evidence in favour of an absence of correlation between the two variables (r<sub>median</sub> = 0.11, BF = 0.51)**, which is a much more informative statement that what we can do with frequentist statistics.

**And that's not all!**

### Visualise the Bayes factor

In general, **pie charts are an absolute no-go in data visualisation**, as our
brain's perceptive system heavily distorts the information presented in such
way^[An exception would be when the pie slices are well-labeled so that our
brain's perception system does not have to do the decoding work.]. Nevertheless,
there is one exception: pizza charts.

It is an intuitive way of interpreting the strength of evidence provided by BFs as an amount of surprise. 

```{r echo=FALSE, fig.cap="Wagenmakers' pizza poking analogy. From the great <www.bayesianspectacles.org> blog.", fig.align='center', out.width="80%"}

Such "pizza plots" can be directly created through the
[`see`](https://github.com/easystats/see) visualisation companion package for
`easystats` (you can install it by running `install.packages("see")`):

```{r }

plot(bayesfactor(result)) +

So, after seeing this pizza, how much would you be surprised by the outcome of a blinded poke? 

## *t*-tests

> **"I know that I know nothing, and especially not if *versicolor* and *virginica* differ in terms of their Sepal.Width" - Socrates**. 

Time to finally answer this crucial question!

### Versicolor *vs.* virginica

Bayesian *t*-tests can be performed in a very similar way to correlations. As we
are particularly interested in two levels of the `Species` factor, *versicolor*
and *virginica*. We will start by filtering out from `iris` the non-relevant
observations corresponding to the *setosa* specie, and we will then visualise
the observations and the distribution of the `Sepal.Width` variable.

```{r }

# Select only two relevant species
data <- iris %>%
  filter(Species != "setosa") %>%

# Visualise distributions and observations
data %>%
  ggplot(aes(x = Species, y = Sepal.Width, fill = Species)) +
  geom_violindot(fill_dots = "black", size_dots = 1) +
  scale_fill_material() +

It *seems* (visually) that *virgnica* flowers have, on average, a slightly higer
width of sepals. Let's assess this difference statistically by using the
`ttestBF()` function in the `BayesFactor` package.

### Compute the Bayesian *t*-test

result <- BayesFactor::ttestBF(formula = Sepal.Width ~ Species, data = data)

From the indices, we can say that the difference of `Sepal.Width` between *virginica* and *versicolor* has a probability of **100% of being negative** [*from the pd and the sign of the median*] (median = -0.19, 89% CI [-0.29, -0.092]). The data provides a **strong evidence against the null hypothesis** (BF = 18).

Keep that in mind as we will see another way of investigating this question.

## Logistic Model

A hypothesis for which one uses a *t*-test can also be tested using a binomial
model (*e.g.*, a **logistic model**). Indeed, it is possible to reformulate the
following hypothesis, "*there is an important difference in this variable between the two groups*" with the hypothesis "*this variable is able to discriminate between (or classify) the two groups*". However, these models are
much more powerful than a *t*-test.

In the case of the difference of `Sepal.Width` between *virginica* and
*versicolor*, the question becomes, *how well can we classify the two species using only* `Sepal.Width`.

### Fit the model


model <- stan_glm(Species ~ Sepal.Width, data = data, family = "binomial", refresh = 0)

### Visualise the model

Using the [`modelbased`](https://github.com/easystats/modelbased) package.


vizdata <- estimate_relation(model)

ggplot(vizdata, aes(x = Sepal.Width, y = Predicted)) +
  geom_ribbon(aes(ymin = CI_low, ymax = CI_high), alpha = 0.5) +
  geom_line() + 
  ylab("Probability of being virginica") +

### Performance and Parameters

Once again, we can extract all indices of interest for the posterior
distribution using our old pal `describe_posterior()`.

describe_posterior(model, test = c("pd", "ROPE", "BF"))



### Visualise the indices


```{r }


### Diagnostic Indices

About diagnostic indices such as Rhat and ESS.
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