`R/bayesfactor_parameters.R`

`bayesfactor_parameters.Rd`

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.**

bayesfactor_parameters( posterior, prior = NULL, direction = "two-sided", null = 0, verbose = TRUE, ... ) bayesfactor_pointull( 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_pointull( 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", "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", "zi", "zero_inflated", "all"), parameters = NULL, ... ) # S3 method for emmGrid 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, ... )

posterior | A numerical vector, |
---|---|

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

direction | Test type (see 'Details'). One of |

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 |

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 |

A data frame containing the Bayes factor representing evidence *against* the null.

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

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.)

`prior`

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`

or`brmsfit`

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*.

When

`posterior`

is an`emmGrid`

object:`prior`

should be the`stanreg`

or`brmsfit`

model used to create the`emmGrid`

objects.`prior`

can also be an`emmGrid`

object equivalent to`posterior`

but created with a model of priors samples*only*.**Note:**When the`emmGrid`

has undergone any transformations (`"log"`

,`"response"`

, etc.), or`regrid`

ing, then`prior`

must be an`emmGrid`

object, as stated above.

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).

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).

There is also a `plot()`

-method implemented in the see-package.

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

Mattan S. Ben-Shachar

library(bayestestR) prior <- distribution_normal(1000, mean = 0, sd = 1) posterior <- distribution_normal(1000, mean = .5, sd = .3) bayesfactor_parameters(posterior, prior)#> # Bayes Factor (Savage-Dickey density ratio) #> #> BF #> ----- #> 1.233 #> #> * Evidence Against The Null: [0] #>if (FALSE) { # rstanarm models # --------------- if (require("rstanarm") && require("emmeans")) { contrasts(sleep$group) <- contr.bayes # see vingette stan_model <- stan_lmer(extra ~ group + (1 | ID), data = sleep) bayesfactor_parameters(stan_model) bayesfactor_parameters(stan_model, null = rope_range(stan_model)) # emmGrid objects # --------------- group_diff <- pairs(emmeans(stan_model, ~group)) bayesfactor_parameters(group_diff, prior = stan_model) } # brms models # ----------- if (require("brms")) { contrasts(sleep$group) <- contr.bayes # 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) } }