Compare models with a Bayes factor — bayes

Estimates both marginal likelihoods with bridge sampling and compares the first model with the second model using bridgesampling::bridge_sampler() and bridgesampling::bf().

Usage

bayes_factor(
  model1,
  model2,
  log = FALSE,
  repetitions = 1L,
  ghk_draws = 500L,
  progress = interactive()
)

Arguments

model1, model2: [RprobitB_fit]
Fitted models to compare.
log: [logical(1)]
Return the logarithm of the Bayes factor?
repetitions: [integer(1)]
Number of independent bridge-sampling repetitions.
ghk_draws: [integer(1)]
Number of draws of the GHK simulator for multivariate normal probabilities of more than three dimensions, see oeli::pmvnorm().
progress: [logical(1)]
Show progress?

Value

A bf_bridge object from bridgesampling. Values greater than one favor model1; values below one favor model2.

References

Gronau QF, Singmann H, Wagenmakers E (2020). “bridgesampling: An R Package for Estimating Normalizing Constants.” Journal of Statistical Software, 92(10), 1–29. doi:10.18637/jss.v092.i10 .

Examples

### Simulate and fit the correctly specified model
set.seed(1)
correct_model <- fit(
  choice ~ x + z | 0,
  dgp_parameters = list(beta = c(x = 1, z = 0.5)),
  chains = 1
)
simulated_data <- as.data.frame(correct_model$data)

### Fit the same data again, but omit the relevant regressor z
misspecified_model <- fit(
  choice ~ x | 0,
  data = simulated_data,
  chains = 1
)

### A Bayes factor greater than one favors the correct first model
bayes_factor(correct_model, misspecified_model)
#> Estimated Bayes factor in favor of model1 over model2: 7.55958