bayestestR (version 0.2.0)

bayesfactor_models: Bayes Factors (BF) for model comparison

Description

This function computes or extracts Bayes factors from fitted models.

Usage

bayesfactor_models(..., denominator = 1, verbose = TRUE)

Arguments

...

Fitted models (any models supported by insight), all fit on the same data, or a single BFBayesFactor object (see 'Details').

denominator

Either an integer indicating which of the models to use as the denominator, or a model to use as a denominator. Ignored for BFBayesFactor.

verbose

Toggle off warnings.

Value

A data frame containing the models' formulas (reconstructed fixed and random effects) and their BFs, that prints nicely.

Details

• For brmsfit or stanreg models, Bayes factors are computed using the bridgesampling package.

  • brmsfit models must have been fitted with save_all_pars = TRUE.

  • stanreg models must have been fitted with a defined diagnostic_file.

• For BFBayesFactor, bayesfactor_models() is mostly a wraparoud BayesFactor::extractBF().

• For all other model types (supported by insight), BIC approximations are used to compute Bayes factors.

In order to correctly and precisely estimate Bayes Factors, a rule of thumb are the 4 P's: Proper Priors and Plentiful Posterior (i.e. probably at leat 40,000 samples instead of the default of 4,000).

A Bayes factor greater than 1 can be interpereted as evidence against the compared-to model (the denominator). One convention is that a Bayes factor greater than 3 can be considered as "substantial" evidence against the denominator model (and vice versa, a Bayes factor smaller than 1/3 indicates substantial evidence in favor of the denominator model) (Wetzels et al. 2011).

References

• Gronau, Q. F., Wagenmakers, E. J., Heck, D. W., and Matzke, D. (2019). A simple method for comparing complex models: Bayesian model comparison for hierarchical multinomial processing tree models using Warp-III bridge sampling. Psychometrika, 84(1), 261-284.

• Kass, R. E., and Raftery, A. E. (1995). Bayes Factors. Journal of the American Statistical Association, 90(430), 773-795.

• Robert, C. P. (2016). The expected demise of the Bayes factor. Journal of Mathematical Psychology, 72, 33<U+2013>37.

• Wagenmakers, E. J. (2007). A practical solution to the pervasive problems of p values. Psychonomic bulletin & review, 14(5), 779-804.

• Wetzels, R., Matzke, D., Lee, M. D., Rouder, J. N., Iverson, G. J., and Wagenmakers, E.-J. (2011). Statistical Evidence in Experimental Psychology: An Empirical Comparison Using 855 t Tests. Perspectives on Psychological Science, 6(3), 291<U+2013>298. 10.1177/1745691611406923

See Also

update.BFGrid

Examples

# NOT RUN {
# With lm objects:
# ----------------
lm1 <- lm(Sepal.Length ~ 1, data = iris)
lm2 <- lm(Sepal.Length ~ Species, data = iris)
lm3 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
lm4 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
bayesfactor_models(lm1, lm2, lm3, lm4, denominator = 1)
bayesfactor_models(lm2, lm3, lm4, denominator = lm1) # same result
bayesfactor_models(lm1, lm2, lm3, lm4, denominator = lm1) # same result

# With lmerMod objects:
# ---------------------
library(lme4)
lmer1 <- lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
lmer2 <- lmer(Sepal.Length ~ Petal.Length + (Petal.Length | Species), data = iris)
lmer3 <- lmer(
  Sepal.Length ~ Petal.Length + (Petal.Length | Species) + (1 | Petal.Width),
  data = iris
)
bayesfactor_models(lmer1, lmer2, lmer3, denominator = 1)
bayesfactor_models(lmer1, lmer2, lmer3, denominator = lmer1)
# }
# NOT RUN {
# rstanarm models
# ---------------------
# (note that a unique diagnostic_file MUST be specified in order to work)
library(rstanarm)
stan_m0 <- stan_glm(Sepal.Length ~ 1,
  data = iris,
  family = gaussian(),
  diagnostic_file = file.path(tempdir(), "df0.csv")
)
stan_m1 <- stan_glm(Sepal.Length ~ Species,
  data = iris,
  family = gaussian(),
  diagnostic_file = file.path(tempdir(), "df1.csv")
)
stan_m2 <- stan_glm(Sepal.Length ~ Species + Petal.Length,
  data = iris,
  family = gaussian(),
  diagnostic_file = file.path(tempdir(), "df2.csv")
)
bayesfactor_models(stan_m1, stan_m2, denominator = stan_m0)


# brms models
# --------------------
# (note the save_all_pars MUST be set to TRUE in order to work)
library(brms)
brm1 <- brm(Sepal.Length ~ 1, data = iris, save_all_pars = TRUE)
brm2 <- brm(Sepal.Length ~ Species, data = iris, save_all_pars = TRUE)
brm3 <- brm(
  Sepal.Length ~ Species + Petal.Length,
  data = iris,
  save_all_pars = TRUE
)

bayesfactor_models(brm1, brm2, brm3, denominator = 1)


# BayesFactor
# ---------------------------
library(BayesFactor)
data(puzzles)
BF <- anovaBF(RT ~ shape * color + ID,
  data = puzzles,
  whichRandom = "ID", progress = FALSE
)
BF
bayesfactor_models(BF) # basically the same
# }
# NOT RUN {
# }