bayesfactor_inclusion {bayestestR} R Documentation

## Inclusion Bayes Factors for testing predictors across Bayesian models

### Description

The ⁠bf_*⁠ function is an alias of the main function.

### Usage

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

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


### Arguments

 models An object of class bayesfactor_models() or BFBayesFactor. match_models See details. prior_odds Optional vector of prior odds for the models. See ⁠BayesFactor::priorOdds<-⁠. ... Arguments passed to or from other methods.

### Details

Inclusion Bayes factors answer the question: Are the observed data more probable under models with a particular effect, than they are under models without that particular effect? In other words, on average - are models with effect X more likely to have produced the observed data than models without effect X?

#### Match Models

If match_models=FALSE (default), Inclusion BFs are computed by comparing all models with a term against all models without that term. If TRUE, comparison is restricted to models that (1) do not include any interactions with the term of interest; (2) for interaction terms, averaging is done only across models that containe the main effect terms from which the interaction term is comprised.

### Value

a data frame containing the prior and posterior probabilities, and log(BF) for each effect (Use as.numeric() to extract the non-log Bayes factors; see examples).

### Interpreting Bayes Factors

A Bayes factor greater than 1 can be interpreted as evidence against the null, at which one convention is that a Bayes factor greater than 3 can be considered as "substantial" evidence against the null (and vice versa, a Bayes factor smaller than 1/3 indicates substantial evidence in favor of the null-model) (Wetzels et al. 2011).

### Note

Random effects in the lmer style are converted to interaction terms: i.e., (X|G) will become the terms 1:G and X:G.

### Author(s)

Mattan S. Ben-Shachar

### References

• Hinne, M., Gronau, Q. F., van den Bergh, D., and Wagenmakers, E. (2019, March 25). A conceptual introduction to Bayesian Model Averaging. doi: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.

weighted_posteriors() for Bayesian parameter averaging.

### Examples

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)
(bf_inc <- bayesfactor_inclusion(BFmodels))

as.numeric(bf_inc)

## Not run:
# BayesFactor
# -------------------------------
library(BayesFactor)

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

bayesfactor_inclusion(BF)

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

## End(Not run)


[Package bayestestR version 0.13.0 Index]