#statstab #334 Workflow Techniques for the Robust Use of Bayes Factors

Thoughts: "We outline a Bayes factor workflow that researchers can use to study whether Bayes factors are robust for their individual analysis"

#bayesfactors #bayesian #r #robust

https://arxiv.org/abs/2103.08744

#statstab #238 Bridging null hypothesis testing and estimation

Thoughts: An overview of the ways you can claim "no effect" under a bayesian framework.

#bayesian #bayesfactors #nullresults #noeffect #equivalencetests #equivalence #jasp #r

https://osf.io/preprints/psyarxiv/c7b45

#statstab #236 Using Bayes to get the most out of non-significant results

Thoughts: A bayesian way to investigate "no effect": Bayes Factors. Cool guide on how to think about priors (post hoc even).

#priors #bayesfactors #nullresults #equivalence #nhbt

https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2014.00781/full

#statstab #232 Bayesian Interval-Null Testing

Thoughts: @JASPStats has a module for Equivalence Tests that include Bayesian Overlapping and Non-Overlapping Hypothesis Testing.

#equivalencetests #bayesfactors #jasp #noeffect #bayes
https://jasp-stats.org/2020/06/02/frequentist-and-bayesian-equivalence-testing-in-jasp/

#statstab #196 JASP Bayesian ANOVA

Thoughts: @JASPStats is used by researchers to "add some bayes factors" to their results. But, do you know what those actually reflect? Here is what their team says:

#bayes #bayesfactors #anova #modelcomparison

https://static.jasp-stats.org/about-bayesian-anova.html

#statstab #166 Using Bayes to get the most out of non-significant results

Thoughts: Not sure how to set more meaningful priors for your Bayes Factors? This paper has a guidance. Great for simple designs.

#bayesian #bayes #bayesfactors #nullresults #nhst

https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2014.00781/full

#statstab #165 Approximate Objective Bayes Factors From P-Values and Sample Size: The 3p√n Rule

Thoughts: p-values can't quantify evidence, but maybe if we apply a transformation they can? Is JAB_01 the future? Debate!

#nhst #pvalues #bayesfactors

https://osf.io/preprints/psyarxiv/egydq

#statstab #148 Bayes Factors and how to use them, via {bayestestR} pkg

Thoughts: A go-to packages for bayesian model inference. Also has a great explanation of BFs, how to use them, and alternatives.

#r #rstats #bayes #BayesFactors #HypothesisTesting

https://easystats.github.io/bayestestR/articles/bayes_factors.html

#statstab #90 Improving the utility of non-significant results [...]

Thoughts: OK overview, but a fairly naive take on what to do with non-sig results. Also, plz don't just report #bayesfactors and call it a day!

#pvalues #NHST #frequentist #education

https://www.sciencedirect.com/science/article/pii/S1747938X23000830

#statstab #87 {bfrms} Bayes Factors for {brms} models

Thoughts: Pkg seems dead, but has insights into specifying #brms models to get similar results to #JASP and the need for more samples (24k min) for proper estimation.

#bayesian #bayesfactors #rstats

https://github.com/bayesstuff/bfrms

New on the blog: I explore the connection between Bayes factors and cross-validation and explain why I think it does not justify the use Bayes factors in most cases. https://www.martinmodrak.cz/2024/03/23/cross-validation-a-fourth-way-to-compute-a-bayes-factor/

#bayesian #BayesFactors #stats #CrossValidation

#statstab #60 Justifying Bayesian Priors

Thoughts: This blog post was extremely useful when I started using @JASPStats and had no idea what priors were. Recommend it.

#bayesian #bayesfactors #jasp

https://xeniaschmalz.blogspot.com/2019/09/justifying-bayesian-prior-parameters-in.html

#statstab #42 Bayes Factors in #brms

Thoughts: Bayes Factors are just the ratio of the height difference of two points on two lines, don't @ me! (seriously, that's how most packages commute it)

#rstats #jasp #bayesian #bayesfactors #SavageDickey #ResearchMethods #psychology #statstodon

https://mvuorre.github.io/posts/2017-03-21-bayes-factors-with-brms/

#statstab #30 Bayesian Model Averaged Marginal Effects by @ajordannafa

Thoughts: Why report AMEs when you can report BMAMEs? Cool (necessary?) way to capture uncertainty. Unsure it make sense for experiments.

#bayesian #uncertainty #rstats #BayesFactors

https://www.ajordannafa.com/blog/2022/being-less-wrong/

Was going through old repositories; found this nice document on estimating treatment effect of a medicine for a single patient with seizures #rstats :rstats:

It goes from simple to complex:
- #Visualization & summary statistics
- Poisson regression (glm) with a treatment dummy
- Penalized poisson regression to deal with 0s
- Correcting for autocorrelation
- #Bayesian Poisson model with #stan #rstanarm
- Correcting for autocorrelation and #BayesFactors #bridgesampling

https://github.com/vankesteren/single_subject