bayestestR (version 0.15.0)

bayesfactor_restricted: Bayes Factors (BF) for Order Restricted Models

Description

This method computes Bayes factors for comparing a model with an order restrictions on its parameters with the fully unrestricted model. Note that this method should only be used for confirmatory analyses.

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

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

Usage

bayesfactor_restricted(posterior, ...)
bf_restricted(posterior, ...)
# S3 method for stanreg
bayesfactor_restricted(
  posterior,
  hypothesis,
  prior = NULL,
  verbose = TRUE,
  effects = c("fixed", "random", "all"),
  component = c("conditional", "zi", "zero_inflated", "all"),
  ...
)
# S3 method for brmsfit
bayesfactor_restricted(
  posterior,
  hypothesis,
  prior = NULL,
  verbose = TRUE,
  effects = c("fixed", "random", "all"),
  component = c("conditional", "zi", "zero_inflated", "all"),
  ...
)
# S3 method for blavaan
bayesfactor_restricted(
  posterior,
  hypothesis,
  prior = NULL,
  verbose = TRUE,
  ...
)
# S3 method for emmGrid
bayesfactor_restricted(
  posterior,
  hypothesis,
  prior = NULL,
  verbose = TRUE,
  ...
)
# S3 method for data.frame
bayesfactor_restricted(
  posterior,
  hypothesis,
  prior = NULL,
  rvar_col = NULL,
  ...
)
# S3 method for bayesfactor_restricted
as.logical(x, which = c("posterior", "prior"), ...)

Value

A data frame containing the (log) Bayes factor representing evidence against the un-restricted model (Use as.numeric() to extract the non-log Bayes factors; see examples). (A bool_results attribute contains the results for each sample, indicating if they are included or not in the hypothesized restriction.)

Arguments

posterior

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

...

Currently not used.

hypothesis

A character vector specifying the restrictions as logical conditions (see examples below).

prior

An object representing a prior distribution (see Details).

verbose

Toggle off warnings.

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.

rvar_col

A single character - the name of an rvar column in the data frame to be processed. See example in p_direction().

x

An object of class bayesfactor_restricted

which

Should the logical matrix be of the posterior or prior distribution(s)?

Setting the correct <code>prior</code>

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, to match the posterior-type input:

• A numeric vector - prior should also be a numeric vector, representing the prior-estimate.

• A data frame - prior should also be a data frame, representing the prior-estimates, in matching column order.

  • If rvar_col is specified, prior should be the name of an rvar column that represents the prior-estimates.

• Supported Bayesian model (stanreg, brmsfit, etc.)

  • prior should be a model an equivalent model with MCMC samples from the priors only. See unupdate().

  • If prior is set to NULL, unupdate() is called internally (not supported for brmsfit_multiple model).

• Output from a {marginaleffects} function - prior should also be an equivalent output from a {marginaleffects} function based on a prior-model (See unupdate()).

• Output from an {emmeans} function

  • prior should also be an equivalent output from an {emmeans} function based on a prior-model (See unupdate()).

  • prior can also be the original (posterior) model, in which case the function will try to "unupdate" the estimates (not supported if the estimates have undergone any transformations -- "log", "response", etc. -- or any regriding).

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).

Details

This method is used to compute Bayes factors for order-restricted models vs un-restricted models by setting an order restriction on the prior and posterior distributions (Morey & Wagenmakers, 2013).

(Though it is possible to use bayesfactor_restricted() to test interval restrictions, it is more suitable for testing order restrictions; see examples).

References

• 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.

• Morey, R. D. (Jan, 2015). Multiple Comparisons with BayesFactor, Part 2 – order restrictions. Retrieved from https://richarddmorey.org/category/order-restrictions/.

Examples

set.seed(444)
library(bayestestR)
prior <- data.frame(
  A = rnorm(500),
  B = rnorm(500),
  C = rnorm(500)
)

posterior <- data.frame(
  A = rnorm(500, .4, 0.7),
  B = rnorm(500, -.2, 0.4),
  C = rnorm(500, 0, 0.5)
)

hyps <- c(
  "A > B & B > C",
  "A > B & A > C",
  "C > A"
)


(b <- bayesfactor_restricted(posterior, hypothesis = hyps, prior = prior))

bool <- as.logical(b, which = "posterior")
head(bool)

if (FALSE) { # require("see") && require("patchwork")

see::plots(
  plot(estimate_density(posterior)),
  # distribution **conditional** on the restrictions
  plot(estimate_density(posterior[bool[, hyps[1]], ])) + ggplot2::ggtitle(hyps[1]),
  plot(estimate_density(posterior[bool[, hyps[2]], ])) + ggplot2::ggtitle(hyps[2]),
  plot(estimate_density(posterior[bool[, hyps[3]], ])) + ggplot2::ggtitle(hyps[3]),
  guides = "collect"
)
}
if (FALSE) { # require("rstanarm")
# \donttest{
# rstanarm models
# ---------------
data("mtcars")

fit_stan <- rstanarm::stan_glm(mpg ~ wt + cyl + am,
  data = mtcars, refresh = 0
)
hyps <- c(
  "am > 0 & cyl < 0",
  "cyl < 0",
  "wt - cyl > 0"
)

bayesfactor_restricted(fit_stan, hypothesis = hyps)
# }
}
if (FALSE) { # require("rstanarm") && require("emmeans")
# \donttest{
# emmGrid objects
# ---------------
# replicating http://bayesfactor.blogspot.com/2015/01/multiple-comparisons-with-bayesfactor-2.html
data("disgust")
contrasts(disgust$condition) <- contr.equalprior_pairs # see vignette
fit_model <- rstanarm::stan_glm(score ~ condition, data = disgust, family = gaussian())

em_condition <- emmeans::emmeans(fit_model, ~condition, data = disgust)
hyps <- c("lemon < control & control < sulfur")

bayesfactor_restricted(em_condition, prior = fit_model, hypothesis = hyps)
# > # Bayes Factor (Order-Restriction)
# >
# >                          Hypothesis P(Prior) P(Posterior)   BF
# >  lemon < control & control < sulfur     0.17         0.75 4.49
# > ---
# > Bayes factors for the restricted model vs. the un-restricted model.
# }
}