This method computes Bayes factors against the null (either a point or an interval), based on prior and posterior samples of a single parameter. This Bayes factor indicates the degree by which the mass of the posterior distribution has shifted further away from or closer to the null value(s) (relative to the prior distribution), thus indicating if the null value has become less or more likely given the observed data.

When the null is an interval, the Bayes factor is computed by comparing the prior and posterior odds of the parameter falling within or outside the null interval (Morey & Rouder, 2011; Liao et al., 2020); When the null is a point, a Savage-Dickey density ratio is computed, which is also an approximation of a Bayes factor comparing the marginal likelihoods of the model against a model in which the tested parameter has been restricted to the point null (Wagenmakers et al., 2010; Heck, 2019).

Note that the logspline package is used for estimating densities and probabilities, and must be installed for the function to work.

bayesfactor_pointnull() and bayesfactor_rope() are wrappers around bayesfactor_parameters with different defaults for the null to be tested against (a point and a range, respectively). Aliases of the main functions are prefixed with bf_*, like bf_parameters() or bf_pointnull().

For more info, in particular on specifying correct priors for factors with more than 2 levels, see the Bayes factors vignette.

## Usage

bayesfactor_parameters(
posterior,
prior = NULL,
direction = "two-sided",
null = 0,
verbose = TRUE,
...
)

bayesfactor_pointnull(
posterior,
prior = NULL,
direction = "two-sided",
null = 0,
verbose = TRUE,
...
)

bayesfactor_rope(
posterior,
prior = NULL,
direction = "two-sided",
null = rope_range(posterior),
verbose = TRUE,
...
)

bf_parameters(
posterior,
prior = NULL,
direction = "two-sided",
null = 0,
verbose = TRUE,
...
)

bf_pointnull(
posterior,
prior = NULL,
direction = "two-sided",
null = 0,
verbose = TRUE,
...
)

bf_rope(
posterior,
prior = NULL,
direction = "two-sided",
null = rope_range(posterior),
verbose = TRUE,
...
)

# S3 method for numeric
bayesfactor_parameters(
posterior,
prior = NULL,
direction = "two-sided",
null = 0,
verbose = TRUE,
...
)

# S3 method for stanreg
bayesfactor_parameters(
posterior,
prior = NULL,
direction = "two-sided",
null = 0,
verbose = TRUE,
effects = c("fixed", "random", "all"),
component = c("conditional", "location", "smooth_terms", "sigma", "zi",
"zero_inflated", "all"),
parameters = NULL,
...
)

# S3 method for brmsfit
bayesfactor_parameters(
posterior,
prior = NULL,
direction = "two-sided",
null = 0,
verbose = TRUE,
effects = c("fixed", "random", "all"),
component = c("conditional", "location", "smooth_terms", "sigma", "zi",
"zero_inflated", "all"),
parameters = NULL,
...
)

# S3 method for blavaan
bayesfactor_parameters(
posterior,
prior = NULL,
direction = "two-sided",
null = 0,
verbose = TRUE,
...
)

# S3 method for data.frame
bayesfactor_parameters(
posterior,
prior = NULL,
direction = "two-sided",
null = 0,
verbose = TRUE,
...
)

## Arguments

posterior

A numerical vector, stanreg / brmsfit object, emmGrid or a data frame - representing a posterior distribution(s) from (see 'Details').

prior

An object representing a prior distribution (see 'Details').

direction

Test type (see 'Details'). One of 0, "two-sided" (default, two tailed), -1, "left" (left tailed) or 1, "right" (right tailed).

null

Value of the null, either a scalar (for point-null) or a range (for a interval-null).

verbose

Toggle off warnings.

...

Arguments passed to and from other methods. (Can be used to pass arguments to internal logspline::logspline().)

effects

Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.

component

Should results for all parameters, parameters for the conditional model or the zero-inflated part of the model be returned? May be abbreviated. Only applies to brms-models.

parameters

Regular expression pattern that describes the parameters that should be returned. Meta-parameters (like lp__ or prior_) are filtered by default, so only parameters that typically appear in the summary() are returned. Use parameters to select specific parameters for the output.

## Value

A data frame containing the (log) Bayes factor representing evidence against the null (Use as.numeric() to extract the non-log Bayes factors; see examples).

## Details

This method is used to compute Bayes factors based on prior and posterior distributions.

### One-sided & Dividing Tests (setting an order restriction)

One sided tests (controlled by direction) are conducted by restricting the prior and posterior of the non-null values (the "alternative") to one side of the null only (Morey & Wagenmakers, 2014). For example, if we have a prior hypothesis that the parameter should be positive, the alternative will be restricted to the region to the right of the null (point or interval). For example, for a Bayes factor comparing the "null" of 0-0.1 to the alternative >0.1, we would set bayesfactor_parameters(null = c(0, 0.1), direction = ">").

It is also possible to compute a Bayes factor for dividing hypotheses - that is, for a null and alternative that are complementary, opposing one-sided hypotheses (Morey & Wagenmakers, 2014). For example, for a Bayes factor comparing the "null" of <0 to the alternative >0, we would set bayesfactor_parameters(null = c(-Inf, 0)).

## Note

There is also a plot()-method implemented in the see-package.

## Setting the correct prior

For the computation of Bayes factors, the model priors must be proper priors (at the very least they should be not flat, and it is preferable that they be informative); As the priors for the alternative get wider, the likelihood of the null value(s) increases, to the extreme that for completely flat priors the null is infinitely more favorable than the alternative (this is called the Jeffreys-Lindley-Bartlett paradox). Thus, you should only ever try (or want) to compute a Bayes factor when you have an informed prior.

(Note that by default, brms::brm() uses flat priors for fixed-effects; See example below.)

It is important to provide the correct prior for meaningful results.

• When posterior is a numerical vector, prior should also be a numerical vector.

• When posterior is a data.frame, prior should also be a data.frame, with matching column order.

• When posterior is a stanreg, brmsfit or other supported Bayesian model:

• prior can be set to NULL, in which case prior samples are drawn internally.

• prior can also be a model equivalent to posterior but with samples from the priors only. See unupdate().

• Note: When posterior is a brmsfit_multiple model, prior must be provided.

• When posterior is an emmGrid / emm_list object:

• prior should also be an emmGrid / emm_list object equivalent to posterior but created with a model of priors samples only. See unupdate().

• prior can also be the original (posterior) model. If so, the function will try to update the emmGrid / emm_list to use the unupdate()d prior-model. (This cannot be done for brmsfit models.)

• Note: When the emmGrid has undergone any transformations ("log", "response", etc.), or regriding, then prior must be an emmGrid object, as stated above.

## 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).

## References

• Wagenmakers, E. J., Lodewyckx, T., Kuriyal, H., and Grasman, R. (2010). Bayesian hypothesis testing for psychologists: A tutorial on the Savage-Dickey method. Cognitive psychology, 60(3), 158-189.

• Heck, D. W. (2019). A caveat on the Savage–Dickey density ratio: The case of computing Bayes factors for regression parameters. British Journal of Mathematical and Statistical Psychology, 72(2), 316-333.

• Morey, R. D., & Wagenmakers, E. J. (2014). Simple relation between Bayesian order-restricted and point-null hypothesis tests. Statistics & Probability Letters, 92, 121-124.

• Morey, R. D., & Rouder, J. N. (2011). Bayes factor approaches for testing interval null hypotheses. Psychological methods, 16(4), 406.

• Liao, J. G., Midya, V., & Berg, A. (2020). Connecting and contrasting the Bayes factor and a modified ROPE procedure for testing interval null hypotheses. The American Statistician, 1-19.

• 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), 291–298. doi:10.1177/1745691611406923

## Author

Mattan S. Ben-Shachar

## Examples

library(bayestestR)
if (require("logspline")) {
prior <- distribution_normal(1000, mean = 0, sd = 1)
posterior <- distribution_normal(1000, mean = .5, sd = .3)
(BF_pars <- bayesfactor_parameters(posterior, prior, verbose = FALSE))

as.numeric(BF_pars)
}
#> [1] 1.212843
# \dontrun{
# rstanarm models
# ---------------
if (require("rstanarm") && require("emmeans") && require("logspline")) {
contrasts(sleep$group) <- contr.equalprior_pairs # see vingette stan_model <- suppressWarnings(stan_lmer( extra ~ group + (1 | ID), data = sleep, refresh = 0 )) bayesfactor_parameters(stan_model) bayesfactor_parameters(stan_model, null = rope_range(stan_model)) # emmGrid objects # --------------- group_diff <- pairs(emmeans(stan_model, ~group)) bayesfactor_parameters(group_diff, prior = stan_model) # Or group_diff_prior <- pairs(emmeans(unupdate(stan_model), ~group)) bayesfactor_parameters(group_diff, prior = group_diff_prior) } #> Loading required package: emmeans #> Sampling priors, please wait... #> Warning: Bayes factors might not be precise. #> For precise Bayes factors, sampling at least 40,000 posterior samples is #> recommended. #> Sampling priors, please wait... #> Sampling priors, please wait... #> Warning: Bayes factors might not be precise. #> For precise Bayes factors, sampling at least 40,000 posterior samples is #> recommended. #> Sampling priors, please wait... #> Warning: Bayes factors might not be precise. #> For precise Bayes factors, sampling at least 40,000 posterior samples is #> recommended. #> Bayes Factor (Savage-Dickey density ratio) #> #> Parameter | BF #> ---------------------- #> group1 - group2 | 3.37 #> #> * Evidence Against The Null: 0 # brms models # ----------- if (require("brms")) { contrasts(sleep$group) <- contr.equalprior_pairs # see vingette
my_custom_priors <-
set_prior("student_t(3, 0, 1)", class = "b") +
set_prior("student_t(3, 0, 1)", class = "sd", group = "ID")

brms_model <- brm(extra ~ group + (1 | ID),
data = sleep,
prior = my_custom_priors
)
bayesfactor_parameters(brms_model)
}
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