bayestestR (version 0.2.0)

bayesfactor_inclusion: Inclusion Bayes Factors for effects across Bayesian models

Description

Inclusion Bayes Factors for effects across Bayesian models

Usage

bayesfactor_inclusion(models, match_models = FALSE, prior_odds = NULL,
  ...)

Arguments

models

an object of class bayesfactor_models or BFBayesFactor.

match_models

If FALSE (default), Inclustion BFs are computed by comparing all models with an effect against all models without the effect. If TRUE, Inclusion BFs are computed by comparing all models with an effect against models without the effect AND without any higher-order interactions with the effect.

prior_odds

optional vector of prior odds for the models. See BayesFactor::priorOdds

...

Arguments passed to or from other methods.

Value

a data frame containing the prior and posterior probabilities, and BF for each effect.

Details

Inclusion Bayes factors answer the question: Given the observed data, how much more likely are models with a particular effect, compared to models without that particular effect? In other words, on average - do models with effect X better fit (or describe) the data compared to models without effect X? See also this vignette.

References

• Hinne, M., Gronau, Q. F., van den Bergh, D., and Wagenmakers, E. (2019, March 25). A conceptual introduction to Bayesian Model Averaging. 10.31234/osf.io/wgb64

• Clyde, M. A., Ghosh, J., & Littman, M. L. (2011). Bayesian adaptive sampling for variable selection and model averaging. Journal of Computational and Graphical Statistics, 20(1), 80-101.

• Mathot. S. (2017). Bayes like a Baws: Interpreting Bayesian Repeated Measures in JASP [Blog post]. Retrieved from https://www.cogsci.nl/blog/interpreting-bayesian-repeated-measures-in-jasp

Examples

# NOT RUN {
library(bayestestR)

# Using bayesfactor_models:
# ------------------------------
mo0 <- lm(Sepal.Length ~ 1, data = iris)
mo1 <- lm(Sepal.Length ~ Species, data = iris)
mo2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
mo3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)

BFmodels <- bayesfactor_models(mo1, mo2, mo3, denominator = mo0)
bayesfactor_inclusion(BFmodels)

# BayesFactor
# -------------------------------
library(BayesFactor)

BF <- generalTestBF(len ~ supp * dose, ToothGrowth, progress = FALSE)

bayesfactor_inclusion(BF)

# compare only matched models:
bayesfactor_inclusion(BF, match_models = TRUE)

# }