bayesfactor_inclusion: Inclusion Bayes Factors for testing predictors across...

Description Usage Arguments Details Value Interpreting Bayes Factors Note Author(s) References See Also Examples

View source: R/bayesfactor_inclusion.R

Description

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

For more info, see the Bayes factors vignette.

Usage

1
2
3
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.

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

See Also

weighted_posteriors() for Bayesian parameter averaging.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
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)
## 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)

DominiqueMakowski/bayestestR documentation built on July 27, 2021, 4:12 p.m.