This vignette can be referred to by citing the package:
The Bayesian framework for statistics is quickly gaining in popularity among scientists, associated with the general shift towards open and honest science. Reasons to prefer this approach are:
In general, the frequentist approach has been associated with the focus on the null hypothesis testing, and the misuse of p-values has been shown to critically contribute to the reproducibility crisis in social and psychological sciences (Chambers, Feredoes, Muthukumaraswamy, & Etchells, 2014; Szucs & Ioannidis, 2016). There is an emerging consensus that the generalization of the Bayesian approach is one way of overcoming these issues (Benjamin et al., 2018; Etz & Vandekerckhove, 2016).
Once we agree that the Bayesian framework is the right way to go, you might wonder what exactly is this framework.
What’s all the fuss about?
Adopting the Bayesian framework is more of a shift in the paradigm than a change in the methodology. Indeed, all the common statistical procedures (t-tests, correlations, ANOVAs, regressions, etc.) can be achieved using the Bayesian framework. The key difference is that in the frequentist framework (the “classical” approach to statistics, with p and t values, as well as some weird degrees of freedom), the effects are fixed (but unknown) and data are random. In other words, it assumes that the unknown parameter has a unique value that we are trying to estimate/guess using our sample data. On the other hand, in the Bayesian framework, instead of estimating the “true effect”, the probability of different effects given the observed data is computed, resulting in a distribution of possible values for the parameters, called the posterior distribution.
The uncertainty in Bayesian inference can be summarized, for instance, by the median of the distribution, as well as a range of values of the posterior distribution that includes the 95% most probable values (the 95% credible interval). Cum grano salis, these are considered the counterparts to the point-estimate and confidence interval in a frequentist framework. To illustrate the difference of interpretation, the Bayesian framework allows to say “given the observed data, the effect has 95% probability of falling within this range”, while the frequentist (less intuitive) alternative would be “when repeatedly computing confidence intervals from data of this sort, there is a 95% probability that the effect falls within a given range”. In essence, the Bayesian sampling algorithms (such as MCMC sampling) return a probability distribution (the posterior) of an effect that is compatible with the observed data. Thus, an effect can be described by characterizing its posterior distribution in relation to its centrality (point-estimates), uncertainty, as well as its existence and significance
In other words, putting the maths behind it aside for a moment, we can say that:
The frequentist approach tries to estimate the real effect. For instance, the “real” value of the correlation between x and y. Hence, the frequentist models return a point-estimate (i.e., a single value and not a distribution) of the “real” correlation (e.g., \(r = 0.42\)) estimated under a number of obscure assumptions (at a minimum, considering that the data is sampled at random from a “parent”, usually normal distribution).
The Bayesian framework assumes no such thing. The data are what they are. Based on the observed data (and a prior belief about the result), the Bayesian sampling algorithm (MCMC sampling is one example) returns a probability distribution (called the posterior) of the effect that is compatible with the observed data. For the correlation between x and y, it will return a distribution that says, for example, “the most probable effect is 0.42, but this data is also compatible with correlations of 0.12 and 0.74 with certain probabilities”.
To characterize statistical significance of our effects, we do not need p-values, or any other such indices. We simply describe the posterior distribution of the effect. For example, we can report the median, the 89% Credible Interval or other indices.
Note: Altough the very purpose of this package is to advocate for the use of Bayesian statistics, please note that there are serious arguments supporting frequentist indices (see for instance this thread). As always, the world is not black and white (p < .001).
So… how does it work?
You can install
bayestestR along with the whole easystats suite by running the following:
Let’s start by fitting a simple frequentist linear regression (the
lm() function stands for linear model) between two numeric variables,
Petal.Length from the famous
iris dataset, included by default in R.
Call: lm(formula = Sepal.Length ~ Petal.Length, data = iris) Residuals: Min 1Q Median 3Q Max -1.2468 -0.2966 -0.0152 0.2768 1.0027 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.3066 0.0784 54.9 <2e-16 *** Petal.Length 0.4089 0.0189 21.6 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.41 on 148 degrees of freedom Multiple R-squared: 0.76, Adjusted R-squared: 0.758 F-statistic: 469 on 1 and 148 DF, p-value: <2e-16
This analysis suggests that there is a statistically significant (whatever that means) and positive (with a coefficient of
0.41) linear relationship between the two variables.
Fitting and interpreting the frequentist models is so easy that it is obvious that people use it instead of the Bayesian framework… right?
model <- stan_glm(Sepal.Length ~ Petal.Length, data = iris) posteriors <- describe_posterior(model) # for a nicer table print_md(posteriors, digits = 2)
|Parameter||Median||95% CI||pd||ROPE||% in ROPE||Rhat||ESS|
|(Intercept)||4.30||[4.15, 4.46]||100%||[-0.08, 0.08]||0%||1.000||4057.00|
|Petal.Length||0.41||[0.37, 0.45]||100%||[-0.08, 0.08]||0%||1.000||4115.00|
The conclusion we draw, for this example, are very similar. The effect (the median of the effect’s posterior distribution) is about
0.41, and it can be also be considered as significant in the Bayesian sense (more on that later).
So, ready to learn more?
Check out the next tutorial!
And, if you want even more, you can check out other articles describing all the functionality the package has to offer!
Andrews, M., & Baguley, T. (2013). Prior approval: The growth of bayesian methods in psychology. British Journal of Mathematical and Statistical Psychology, 66(1), 1–7.
Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E.-J., Berk, R., … others. (2018). Redefine statistical significance. Nature Human Behaviour, 2(1), 6.
Chambers, C. D., Feredoes, E., Muthukumaraswamy, S. D., & Etchells, P. (2014). Instead of ’playing the game’ it is time to change the rules: Registered reports at aims neuroscience and beyond. AIMS Neuroscience, 1(1), 4–17.
Etz, A., & Vandekerckhove, J. (2016). A bayesian perspective on the reproducibility project: Psychology. PloS One, 11(2), e0149794.
Kruschke, J. K. (2010). What to believe: Bayesian methods for data analysis. Trends in Cognitive Sciences, 14(7), 293–300.
Kruschke, J. K., Aguinis, H., & Joo, H. (2012). The time has come: Bayesian methods for data analysis in the organizational sciences. Organizational Research Methods, 15(4), 722–752.
Szucs, D., & Ioannidis, J. P. (2016). Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature. BioRxiv, 071530.
Wagenmakers, E.-J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., … others. (2018). Bayesian inference for psychology. Part i: Theoretical advantages and practical ramifications. Psychonomic Bulletin & Review, 25(1), 35–57.