Title: Tools for Bayesian Analyses
Version: 0.2.19
Description: Provides tools for conducting Bayesian analyses and Bayesian model averaging (Kass and Raftery, 1995, <doi:10.1080/01621459.1995.10476572>, Hoeting et al., 1999, <doi:10.1214/ss/1009212519>). The package contains functions for creating a wide range of prior distribution objects, mixing posterior samples from 'JAGS' and 'Stan' models, plotting posterior distributions, and etc... The tools for working with prior distribution span from visualization, generating 'JAGS' and 'bridgesampling' syntax to basic functions such as rng, quantile, and distribution functions.
Maintainer: František Bartoš <f.bartos96@gmail.com>
URL: https://fbartos.github.io/BayesTools/
BugReports: https://github.com/FBartos/BayesTools/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
SystemRequirements: JAGS >= 4.3.0 (https://mcmc-jags.sourceforge.io/)
Depends: stats
Imports: graphics, extraDistr, mvtnorm, coda, bridgesampling, parallel, ggplot2, Rdpack, rlang
Suggests: scales, testthat, vdiffr, covr, knitr, rstan, rjags, runjags, BayesFactor, RoBMA, rmarkdown
RdMacros: Rdpack
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-06-08 15:35:08 UTC; fbart
Author: František Bartoš ORCID iD [aut, cre]
Repository: CRAN
Date/Publication: 2025-06-08 16:20:05 UTC

BayesTools

Description

BayesTools: Provides tools for conducting Bayesian analyses. The package contains functions for creating a wide range of prior distribution objects, mixing posterior samples from JAGS and Stan models, plotting posterior distributions, and etc... The tools for working with prior distribution span from visualization, generating JAGS and bridgesampling syntax to basic functions such as rng, quantile, and distribution functions.

Author(s)

František Bartoš f.bartos96@gmail.com

See Also

Useful links:

Create BayesTools ensemble summary tables

Description

Creates estimate summaries based on posterior distributions created by mix_posteriors, inference summaries based on inference created by ensemble_inference, or ensemble summary/diagnostics based on a list of models_inference models (or marginal_inference in case of marginal_estimates_table).

Usage

ensemble_estimates_table(
  samples,
  parameters,
  probs = c(0.025, 0.95),
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  transform_factors = FALSE,
  transform_orthonormal = FALSE,
  formula_prefix = TRUE
)

ensemble_inference_table(
  inference,
  parameters,
  logBF = FALSE,
  BF01 = FALSE,
  title = NULL,
  footnotes = NULL,
  warnings = NULL
)

ensemble_summary_table(
  models,
  parameters,
  logBF = FALSE,
  BF01 = FALSE,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  remove_spike_0 = TRUE,
  short_name = FALSE
)

ensemble_diagnostics_table(
  models,
  parameters,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  remove_spike_0 = TRUE,
  short_name = FALSE
)

ensemble_estimates_empty_table(
  probs = c(0.025, 0.95),
  title = NULL,
  footnotes = NULL,
  warnings = NULL
)

ensemble_inference_empty_table(title = NULL, footnotes = NULL, warnings = NULL)

ensemble_summary_empty_table(title = NULL, footnotes = NULL, warnings = NULL)

ensemble_diagnostics_empty_table(
  title = NULL,
  footnotes = NULL,
  warnings = NULL
)

marginal_estimates_table(
  samples,
  inference,
  parameters,
  probs = c(0.025, 0.95),
  logBF = FALSE,
  BF01 = FALSE,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  formula_prefix = TRUE
)

Arguments

samples

posterior samples created by mix_posteriors

parameters

character vector of parameters (or a named list with of character vectors for summary and diagnostics tables) specifying the parameters (and their grouping) for the summary table

probs

quantiles for parameter estimates

title

title to be added to the table

footnotes

footnotes to be added to the table

warnings

warnings to be added to the table

transform_factors

whether factors with orthonormal/meandif prior distribution should be transformed to differences from the grand mean

transform_orthonormal

(to be depreciated) whether factors with orthonormal prior distributions should be transformed to differences from the grand mean

formula_prefix

whether the parameter prefix from formula should be printed. Defaults to TRUE.

inference

model inference created by ensemble_inference

logBF

whether the Bayes factor should be on log scale

BF01

whether the Bayes factor should be inverted

models

list of models_inference model objects, each of which containing a list of priors and inference object, The inference must be a named list with information about the model: model number m_number, marginal likelihood marglik, prior and posterior probability prior_prob and post_prob, inclusion Bayes factor inclusion_BF, and fit summary generated by runjags_estimates_table for the diagnostics table

remove_spike_0

whether prior distributions equal to spike at 0 should be removed from the prior_list

short_name

whether the prior distribution names should be shortened. Defaults to FALSE.

Value

ensemble_estimates_table returns a table with the model-averaged estimates, ensemble_inference_table returns a table with the prior and posterior probabilities and inclusion Bayes factors, ensemble_summary_table returns a table with overview of the models included in the ensemble, and ensemble_diagnostics_table returns an overview of the MCMC diagnostics for the models included in the ensemble. All of the tables are objects of class 'BayesTools_table'.

See Also

ensemble_inference mix_posteriors BayesTools_model_tables

Create BayesTools model tables

Description

Creates model summary based on a model objects or provides estimates table for a runjags fit.

Usage

model_summary_table(
  model,
  model_description = NULL,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  remove_spike_0 = TRUE,
  short_name = FALSE,
  formula_prefix = TRUE,
  remove_parameters = NULL
)

runjags_estimates_table(
  fit,
  transformations = NULL,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  conditional = FALSE,
  remove_spike_0 = TRUE,
  transform_factors = FALSE,
  transform_orthonormal = FALSE,
  formula_prefix = TRUE,
  remove_inclusion = FALSE,
  remove_parameters = NULL,
  return_samples = FALSE
)

runjags_inference_table(
  fit,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  formula_prefix = TRUE
)

JAGS_estimates_table(
  fit,
  transformations = NULL,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  conditional = FALSE,
  remove_spike_0 = TRUE,
  transform_factors = FALSE,
  transform_orthonormal = FALSE,
  formula_prefix = TRUE,
  remove_inclusion = FALSE,
  remove_parameters = NULL,
  return_samples = FALSE
)

JAGS_inference_table(
  fit,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  formula_prefix = TRUE
)

JAGS_summary_table(
  model,
  model_description = NULL,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  remove_spike_0 = TRUE,
  short_name = FALSE,
  formula_prefix = TRUE,
  remove_parameters = NULL
)

model_summary_empty_table(
  model_description = NULL,
  title = NULL,
  footnotes = NULL,
  warnings = NULL
)

runjags_estimates_empty_table(title = NULL, footnotes = NULL, warnings = NULL)

runjags_inference_empty_table(title = NULL, footnotes = NULL, warnings = NULL)

JAGS_estimates_empty_table(title = NULL, footnotes = NULL, warnings = NULL)

JAGS_inference_empty_table(title = NULL, footnotes = NULL, warnings = NULL)

stan_estimates_table(
  fit,
  transformations = NULL,
  title = NULL,
  footnotes = NULL,
  warnings = NULL
)

Arguments

model

model object containing a list of priors and inference object, The inference must be a named list with information about the model: model number m_number, marginal likelihood marglik, prior and posterior probability prior_prob and post_prob, and model inclusion Bayes factor inclusion_BF

model_description

named list with additional description to be added to the table

title

title to be added to the table

footnotes

footnotes to be added to the table

warnings

warnings to be added to the table

remove_spike_0

whether prior distributions equal to spike at 0 should be removed from the prior_list

short_name

whether the prior distribution names should be shortened. Defaults to FALSE.

formula_prefix

whether the parameter prefix from formula should be printed. Defaults to TRUE.

remove_parameters

parameters to be removed from the summary. Defaults to NULL, i.e., including all parameters.

fit

runjags model fit

transformations

named list of transformations to be applied to specific parameters

conditional

summarizes estimates conditional on being included in the model for spike and slab priors. Defaults to FALSE.

transform_factors

whether factors with orthonormal/meandif prior distribution should be transformed to differences from the grand mean

transform_orthonormal

(to be depreciated) whether factors with orthonormal prior distributions should be transformed to differences from the grand mean

remove_inclusion

whether estimates of the inclusion probabilities should be excluded from the summary table. Defaults to FALSE.

return_samples

whether to return the transoformed and formated samples instead of the table. Defaults to FALSE.

Value

model_summary_table returns a table with overview of the fitted model, runjags_estimates_table returns a table with MCMC estimates, and runjags_estimates_empty_table returns an empty estimates table. All of the tables are objects of class 'BayesTools_table'.

See Also

BayesTools_ensemble_tables

Add 'JAGS' prior

Description

Adds priors to a 'JAGS' syntax.

Usage

JAGS_add_priors(syntax, prior_list)

Arguments

syntax

JAGS model syntax

prior_list

named list of prior distribution (names correspond to the parameter names)

Value

JAGS_add_priors returns a JAGS syntax.

Compute marginal likelihood of a 'JAGS' model

Description

A wrapper around bridge_sampler that automatically computes likelihood part dependent on the prior distribution and prepares parameter samples. log_posterior must specify a function that takes two arguments - a named list of samples from the prior distributions and the data, and returns log likelihood of the model part.

Usage

JAGS_bridgesampling(
  fit,
  log_posterior,
  data = NULL,
  prior_list = NULL,
  formula_list = NULL,
  formula_data_list = NULL,
  formula_prior_list = NULL,
  add_parameters = NULL,
  add_bounds = NULL,
  maxiter = 10000,
  silent = TRUE,
  ...
)

Arguments

fit

model fitted with either runjags posterior samples obtained with rjags-package

log_posterior

function that takes a named list of samples, the data, and additional list of parameters passed as ... as input and returns the log of the unnormalized posterior density of the model part

data

list containing data to fit the model (not including data for the formulas)

prior_list

named list of prior distribution (names correspond to the parameter names) of parameters not specified within the formula_list

formula_list

named list of formulas to be added to the model (names correspond to the parameter name created by each of the formula)

formula_data_list

named list of data frames containing data for each formula (names of the lists correspond to the parameter name created by each of the formula)

formula_prior_list

named list of named lists of prior distributions (names of the lists correspond to the parameter name created by each of the formula and the names of the prior distribution correspond to the parameter names) of parameters specified within the formula

add_parameters

vector of additional parameter names that should be used in bridgesampling but were not specified in the prior_list

add_bounds

list with two name vectors ("lb" and "up") containing lower and upper bounds of the additional parameters that were not specified in the prior_list

maxiter

maximum number of iterations for the bridge_sampler

silent

whether the progress should be printed, defaults to TRUE

...

additional argument to the bridge_sampler and log_posterior function

Value

JAGS_bridgesampling returns an object of class 'bridge'.

Examples

## Not run: 
# simulate data
set.seed(1)
data <- list(
  x = rnorm(10),
  N = 10
)
data$x

# define priors
priors_list <- list(mu = prior("normal", list(0, 1)))

# define likelihood for the data
model_syntax <-
  "model{
    for(i in 1:N){
      x[i] ~ dnorm(mu, 1)
    }
  }"

# fit the models
fit <- JAGS_fit(model_syntax, data, priors_list)

# define log posterior for bridge sampling
log_posterior <- function(parameters, data){
  sum(dnorm(data$x, parameters$mu, 1, log = TRUE))
}

# get marginal likelihoods
marglik <- JAGS_bridgesampling(fit, log_posterior, data, priors_list)

## End(Not run)

Prepare 'JAGS' posterior for 'bridgesampling'

Description

prepares posterior distribution for 'bridgesampling' by removing unnecessary parameters and attaching lower and upper bounds of parameters based on a list of prior distributions.

Usage

JAGS_bridgesampling_posterior(
  posterior,
  prior_list,
  add_parameters = NULL,
  add_bounds = NULL
)

Arguments

posterior

matrix of mcmc samples from the posterior distribution

prior_list

named list of prior distribution (names correspond to the parameter names) of parameters not specified within the formula_list

add_parameters

vector of additional parameter names that should be used in bridgesampling but were not specified in the prior_list

add_bounds

list with two name vectors ("lb" and "up") containing lower and upper bounds of the additional parameters that were not specified in the prior_list

Value

JAGS_bridgesampling_posterior returns a matrix of posterior samples with 'lb' and 'ub' attributes carrying the lower and upper boundaries.

Check and list 'JAGS' fitting settings

Description

Checks and lists settings for the JAGS_fit function.

Usage

JAGS_check_and_list_fit_settings(
  chains,
  adapt,
  burnin,
  sample,
  thin,
  autofit,
  parallel,
  cores,
  silent,
  seed,
  check_mins = list(chains = 1, adapt = 50, burnin = 50, sample = 100, thin = 1),
  call = ""
)

JAGS_check_and_list_autofit_settings(
  autofit_control,
  skip_sample_extend = FALSE,
  call = ""
)

Arguments

chains

number of chains to be run, defaults to 4

adapt

number of samples used for adapting the MCMC chains, defaults to 500

burnin

number of burnin iterations of the MCMC chains, defaults to 1000

sample

number of sampling iterations of the MCMC chains, defaults to 4000

thin

thinning interval for the MCMC samples, defaults to 1

autofit

whether the models should be refitted until convergence criteria specified in autofit_control. Defaults to FALSE.

parallel

whether the chains should be run in parallel FALSE

cores

number of cores used for multithreading if parallel = TRUE, defaults to chains

silent

whether the function should proceed silently, defaults to TRUE

seed

seed for random number generation

check_mins

named list of minimal values for which should some input be checked. Defaults to:

chains

1

adapt

50

burnin

50

sample

100

thin

1

call

string to be placed as a prefix to the error call.

autofit_control

a list of arguments controlling the autofit function. Possible options are:

max_Rhat

maximum R-hat error for the autofit function. Defaults to 1.05.

min_ESS

minimum effective sample size. Defaults to 500.

max_error

maximum MCMC error. Defaults to 1.01.

max_SD_error

maximum MCMC error as the proportion of standard deviation of the parameters. Defaults to 0.05.

max_time

list specifying the time time and units after which the automatic fitting function is stopped. The units arguments need to correspond to units passed to difftime function.

max_extend

number of times after which the automatic fitting function is stopped.

sample_extend

number of samples between each convergence check. Defaults to 1000.

restarts

number of times new initial values should be generated in case the model fails to initialize. Defaults to 10.

skip_sample_extend

whether sample_extend is allowed to be NULL and skipped in the check

Value

JAGS_check_and_list_fit_settings invisibly returns a list of checked fit settings. JAGS_check_and_list_autofit_settings invisibly returns a list of checked autofit settings. parameter names.

Assess convergence of a runjags model

Description

Checks whether the supplied runjags-package model satisfied convergence criteria.

Usage

JAGS_check_convergence(
  fit,
  prior_list,
  max_Rhat = 1.05,
  min_ESS = 500,
  max_error = 0.01,
  max_SD_error = 0.05,
  add_parameters = NULL,
  fail_fast = FALSE
)

Arguments

fit

a runjags model

prior_list

named list of prior distribution (names correspond to the parameter names)

max_Rhat

maximum R-hat error for the autofit function. Defaults to 1.05.

min_ESS

minimum effective sample size. Defaults to 500.

max_error

maximum MCMC error. Defaults to 1.01.

max_SD_error

maximum MCMC error as the proportion of standard deviation of the parameters. Defaults to 0.05.

add_parameters

vector of additional parameter names that should be used (only allows removing last, fixed, omega element if omega is tracked manually).

fail_fast

whether the function should stop after the first failed convergence check.

Value

JAGS_check_convergence returns a boolean indicating whether the model converged or not, with an attribute 'errors' carrying the failed convergence checks (if any).

See Also

JAGS_fit()

Examples

## Not run: 
# simulate data
set.seed(1)
data <- list(
  x = rnorm(10),
  N = 10
)
data$x

# define priors
priors_list <- list(mu = prior("normal", list(0, 1)))

# define likelihood for the data
model_syntax <-
  "model{
    for(i in 1:N){
      x[i] ~ dnorm(mu, 1)
    }
  }"

# fit the models
fit <- JAGS_fit(model_syntax, data, priors_list)
JAGS_check_convergence(fit, priors_list)

## End(Not run)

Plot diagnostics of a 'JAGS' model

Description

Creates density plots, trace plots, and autocorrelation plots for a given parameter of a JAGS model.

Usage

JAGS_diagnostics(
  fit,
  parameter,
  type,
  plot_type = "base",
  xlim = NULL,
  ylim = NULL,
  lags = 30,
  n_points = 1000,
  transformations = NULL,
  transform_factors = FALSE,
  transform_orthonormal = FALSE,
  short_name = FALSE,
  parameter_names = FALSE,
  formula_prefix = TRUE,
  ...
)

JAGS_diagnostics_density(
  fit,
  parameter,
  plot_type = "base",
  xlim = NULL,
  n_points = 1000,
  transformations = NULL,
  transform_factors = FALSE,
  transform_orthonormal = FALSE,
  short_name = FALSE,
  parameter_names = FALSE,
  formula_prefix = TRUE,
  ...
)

JAGS_diagnostics_trace(
  fit,
  parameter,
  plot_type = "base",
  ylim = NULL,
  transformations = NULL,
  transform_factors = FALSE,
  transform_orthonormal = FALSE,
  short_name = FALSE,
  parameter_names = FALSE,
  formula_prefix = TRUE,
  ...
)

JAGS_diagnostics_autocorrelation(
  fit,
  parameter,
  plot_type = "base",
  lags = 30,
  transformations = NULL,
  transform_factors = FALSE,
  transform_orthonormal = FALSE,
  short_name = FALSE,
  parameter_names = FALSE,
  formula_prefix = TRUE,
  ...
)

Arguments

fit

a JAGS model fitted via JAGS_fit()

parameter

parameter to be plotted

type

what type of model diagnostic should be plotted. The available options are "density", "trace", and "autocorrelation"

plot_type

whether to use a base plot "base" or ggplot2 "ggplot" for plotting.

xlim

x plotting range

ylim

y plotting range

lags

number of lags to be shown for the autocorrelation plot. Defaults to 30.

n_points

number of equally spaced points in the x_range if x_seq is unspecified

transformations

named list of transformations to be applied to specific parameters

transform_factors

whether factors with orthonormal/meandif prior distribution should be transformed to differences from the grand mean

transform_orthonormal

(to be depreciated) whether factors with orthonormal prior distributions should be transformed to differences from the grand mean

short_name

whether prior distribution names should be shorted

parameter_names

whether parameter names should be printed

formula_prefix

whether the parameter prefix from formula should be printed. Defaults to TRUE.

...

additional arguments

Value

diagnostics returns either NULL if plot_type = "base" or an object/list of objects (depending on the number of parameters to be plotted) of class 'ggplot2' if plot_type = "ggplot2".

See Also

JAGS_fit() JAGS_check_convergence()

Evaluate JAGS formula using posterior samples

Description

Evaluates a JAGS formula on a posterior distribution obtained from a fitted model.

Usage

JAGS_evaluate_formula(fit, formula, parameter, data, prior_list)

Arguments

fit

model fitted with either runjags posterior samples obtained with rjags-package

formula

formula specifying the right hand side of the assignment (the left hand side is ignored)

parameter

name of the parameter created with the formula

data

data.frame containing predictors included in the formula

prior_list

named list of prior distribution of parameters specified within the formula

Value

JAGS_evaluate_formula returns a matrix of the evaluated posterior samples on the supplied data.

See Also

JAGS_fit() JAGS_formula()

Fits a 'JAGS' model

Description

A wrapper around run.jags that simplifies fitting 'JAGS' models with usage with pre-specified model part of the 'JAGS' syntax, data and list of prior distributions.

Usage

JAGS_fit(
  model_syntax,
  data = NULL,
  prior_list = NULL,
  formula_list = NULL,
  formula_data_list = NULL,
  formula_prior_list = NULL,
  chains = 4,
  adapt = 500,
  burnin = 1000,
  sample = 4000,
  thin = 1,
  autofit = FALSE,
  autofit_control = list(max_Rhat = 1.05, min_ESS = 500, max_error = 0.01, max_SD_error =
    0.05, max_time = list(time = 60, unit = "mins"), sample_extend = 1000, restarts = 10,
    max_extend = 10),
  parallel = FALSE,
  cores = chains,
  silent = TRUE,
  seed = NULL,
  add_parameters = NULL,
  required_packages = NULL,
  ...
)

JAGS_extend(
  fit,
  autofit_control = list(max_Rhat = 1.05, min_ESS = 500, max_error = 0.01, max_SD_error =
    0.05, max_time = list(time = 60, unit = "mins"), sample_extend = 1000, restarts = 10,
    max_extend = 10),
  parallel = FALSE,
  cores = NULL,
  silent = TRUE,
  seed = NULL
)

Arguments

model_syntax

jags syntax for the model part

data

list containing data to fit the model (not including data for the formulas)

prior_list

named list of prior distribution (names correspond to the parameter names) of parameters not specified within the formula_list

formula_list

named list of formulas to be added to the model (names correspond to the parameter name created by each of the formula)

formula_data_list

named list of data frames containing data for each formula (names of the lists correspond to the parameter name created by each of the formula)

formula_prior_list

named list of named lists of prior distributions (names of the lists correspond to the parameter name created by each of the formula and the names of the prior distribution correspond to the parameter names) of parameters specified within the formula

chains

number of chains to be run, defaults to 4

adapt

number of samples used for adapting the MCMC chains, defaults to 500

burnin

number of burnin iterations of the MCMC chains, defaults to 1000

sample

number of sampling iterations of the MCMC chains, defaults to 4000

thin

thinning interval for the MCMC samples, defaults to 1

autofit

whether the models should be refitted until convergence criteria specified in autofit_control. Defaults to FALSE.

autofit_control

a list of arguments controlling the autofit function. Possible options are:

max_Rhat

maximum R-hat error for the autofit function. Defaults to 1.05.

min_ESS

minimum effective sample size. Defaults to 500.

max_error

maximum MCMC error. Defaults to 1.01.

max_SD_error

maximum MCMC error as the proportion of standard deviation of the parameters. Defaults to 0.05.

max_time

list specifying the time time and units after which the automatic fitting function is stopped. The units arguments need to correspond to units passed to difftime function.

max_extend

number of times after which the automatic fitting function is stopped.

sample_extend

number of samples between each convergence check. Defaults to 1000.

restarts

number of times new initial values should be generated in case the model fails to initialize. Defaults to 10.

parallel

whether the chains should be run in parallel FALSE

cores

number of cores used for multithreading if parallel = TRUE, defaults to chains

silent

whether the function should proceed silently, defaults to TRUE

seed

seed for random number generation

add_parameters

vector of additional parameter names that should be used monitored but were not specified in the prior_list

required_packages

character vector specifying list of packages containing JAGS models required for sampling (in case that the function is run in parallel or in detached R session). Defaults to NULL.

...

additional hidden arguments

fit

a 'BayesTools_fit' object (created by JAGS_fit() function) to be extended

Value

JAGS_fit returns an object of class 'runjags' and 'BayesTools_fit'.

See Also

JAGS_check_convergence()

Examples

## Not run: 
# simulate data
set.seed(1)
data <- list(
  x = rnorm(10),
  N = 10
)
data$x

# define priors
priors_list <- list(mu = prior("normal", list(0, 1)))

# define likelihood for the data
model_syntax <-
  "model{
    for(i in 1:N){
      x[i] ~ dnorm(mu, 1)
    }
  }"

# fit the models
fit <- JAGS_fit(model_syntax, data, priors_list)

## End(Not run)

Create JAGS formula syntax and data object

Description

Creates a JAGS formula syntax, prepares data input, and returns modified prior list for further processing in the JAGS_fit function

Usage

JAGS_formula(formula, parameter, data, prior_list)

Arguments

formula

formula specifying the right hand side of the assignment (the left hand side is ignored)

parameter

name of the parameter to be created with the formula

data

data.frame containing predictors included in the formula

prior_list

named list of prior distribution of parameters specified within the formula

Value

JAGS_formula returns a list containing the formula JAGS syntax, JAGS data object, and modified prior_list.

See Also

JAGS_fit()

Examples

# simulate data
set.seed(1)
df <- data.frame(
  y      = rnorm(60),
  x_cont = rnorm(60),
  x_bin  = rbinom(60, 1, .5),
  x_fac3 = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")),
  x_fac4 = factor(rep(c("A", "B", "C", "D"), 15), levels = c("A", "B", "C", "D"))
)

# specify priors
prior_list <- list(
"intercept"     = prior("normal", list(0, 1)),
"x_cont"        = prior("normal", list(0, .5)),
"x_fac3"        = prior_factor("normal",  list(0, 1),  contrast = "treatment"),
"x_fac4"        = prior_factor("mnormal", list(0, 1),  contrast = "orthonormal"),
"x_fac3:x_fac4" = prior_factor("mnormal", list(0, .5), contrast = "orthonormal")
)

# create the formula object
formula <- JAGS_formula(
  formula = ~ x_cont + x_fac3 * x_fac4,
  parameter = "mu", data = df, prior_list = prior_list)

Create initial values for 'JAGS' model

Description

Creates initial values for priors in a 'JAGS' model.

Usage

JAGS_get_inits(prior_list, chains, seed)

Arguments

prior_list

named list of prior distribution (names correspond to the parameter names)

chains

number of chains

seed

seed for random number generation

Value

JAGS_add_priors returns a list of JAGS initial values.

Extract parameters for 'JAGS' priors

Description

Extracts transformed parameters from the prior part of a 'JAGS' model inside of a 'bridgesampling' function (returns them as a named list)

Usage

JAGS_marglik_parameters(samples, prior_list)

JAGS_marglik_parameters_formula(
  samples,
  formula_data_list,
  formula_prior_list,
  prior_list_parameters
)

Arguments

samples

samples provided by bridgesampling function

prior_list

named list of prior distribution (names correspond to the parameter names) of parameters not specified within the formula_list

formula_data_list

named list of data frames containing data for each formula (names of the lists correspond to the parameter name created by each of the formula)

formula_prior_list

named list of named lists of prior distributions (names of the lists correspond to the parameter name created by each of the formula and the names of the prior distribution correspond to the parameter names) of parameters specified within the formula

prior_list_parameters

named list of prior distributions on model parameters (not specified within the formula but that might scale the formula parameters)

Value

JAGS_marglik_parameters returns a named list of (transformed) posterior samples.

Compute marginal likelihood for 'JAGS' priors

Description

Computes marginal likelihood for the prior part of a 'JAGS' model within 'bridgesampling' function

Usage

JAGS_marglik_priors(samples, prior_list)

JAGS_marglik_priors_formula(samples, formula_prior_list)

Arguments

samples

samples provided by bridgesampling function

prior_list

named list of prior distribution (names correspond to the parameter names) of parameters not specified within the formula_list

formula_prior_list

named list of named lists of prior distributions (names of the lists correspond to the parameter name created by each of the formula and the names of the prior distribution correspond to the parameter names) of parameters specified within the formula

Value

JAGS_marglik_priors returns a numeric value of likelihood evaluated at the current posterior sample.

Create list of monitored parameters for 'JAGS' model

Description

Creates a vector of parameter names to be monitored in a 'JAGS' model.

Usage

JAGS_to_monitor(prior_list)

Arguments

prior_list

named list of prior distribution (names correspond to the parameter names)

Value

JAGS_to_monitor returns a character vector of parameter names.

Compute Savage-Dickey inclusion Bayes factors

Description

Computes Savage-Dickey (density ratio) inclusion Bayes factors based the change of height from prior to posterior distribution at the test value.

Usage

Savage_Dickey_BF(
  posterior,
  null_hypothesis = 0,
  normal_approximation = FALSE,
  silent = FALSE
)

Arguments

posterior

marginal posterior distribution generated via the marginal_posterior function

null_hypothesis

point null hypothesis to test. Defaults to 0

normal_approximation

whether the height of prior and posterior density should be approximated via a normal distribution (rather than kernel density). Defaults to FALSE.

silent

whether warnings should be returned silently. Defaults to FALSE

Value

Savage_Dickey_BF returns a Bayes factor.

Adds column to BayesTools table

Description

Adds column to a BayesTools table while not breaking formatting, attributes, etc...

Usage

add_column(
  table,
  column_title,
  column_values,
  column_position = NULL,
  column_type = NULL
)

Arguments

table

BayesTools table

column_title

title of the new column

column_values

values of the new column

column_position

position of the new column (defaults to NULL which appends the column to the end)

column_type

type of values of the new column table (important for formatting, defaults to NULL = the function tries to guess numeric / character based on the column_values but many more specific types are available)

Value

returns an object of 'BayesTools_table' class.

Model-average marginal posterior distributions and marginal Bayes factors based on BayesTools JAGS model via marginal_inference

Description

Creates marginal model-averaged and conditional posterior distributions based on a BayesTools JAGS model, vector of parameters, formula, and a list of conditional specifications for each parameter. Computes inclusion Bayes factors for each marginal estimate via a Savage-Dickey density approximation.

Usage

as_marginal_inference(
  model,
  marginal_parameters,
  parameters,
  conditional_list,
  conditional_rule,
  formula,
  null_hypothesis = 0,
  normal_approximation = FALSE,
  n_samples = 10000,
  silent = FALSE,
  force_plots = FALSE
)

Arguments

model

model fit via the JAGS_fit function

marginal_parameters

parameters for which the the marginal summary should be created

parameters

all parameters included in the model_list that are relevant for the formula (all of which need to have specification of is_null_list)

conditional_list

list of conditional parameters for each marginal parameter

conditional_rule

a character string specifying the rule for conditioning. Either "AND" or "OR". Defaults to "AND".

formula

model formula (needs to be specified if parameter was part of a formula)

null_hypothesis

point null hypothesis to test. Defaults to 0

normal_approximation

whether the height of prior and posterior density should be approximated via a normal distribution (rather than kernel density). Defaults to FALSE.

n_samples

number of samples to be drawn for the model-averaged prior distribution

silent

whether warnings should be returned silently. Defaults to FALSE

force_plots

temporal argument allowing to generate conditional posterior samples suitable for prior and posterior plots. Only available when conditioning on a single parameter.

Value

as_marginal_inference returns an object of class 'marginal_inference'.

See Also

marginal_inference as_mixed_posteriors

Export BayesTools JAGS model posterior distribution as model-average posterior distributions via mix_posteriors

Description

Creates a model-averages posterior distributions on a single model that allows mimicking the mix_posteriors functionality. This function is useful when the model-averaged ensemble is based on prior_spike_and_slab or prior_mixture priors - the model-averaging is done within the model.

Usage

as_mixed_posteriors(
  model,
  parameters,
  conditional = NULL,
  conditional_rule = "AND",
  force_plots = FALSE
)

Arguments

model

model fit via the JAGS_fit function

parameters

vector of parameters names for which inference should be drawn

conditional

a character vector of parameters to be conditioned on

conditional_rule

a character string specifying the rule for conditioning. Either "AND" or "OR". Defaults to "AND".

force_plots

temporal argument allowing to generate conditional posterior samples suitable for prior and posterior plots. Only available when conditioning on a single parameter.

Value

as_mix_posteriors returns a named list of mixed posterior distributions (either a vector of matrix).

See Also

mix_posteriors

Create a 'bridgesampling' object

Description

prepares a 'bridgesampling' object with a given log marginal likelihood.

Usage

bridgesampling_object(logml = -Inf)

Arguments

logml

log marginal likelihood. Defaults to -Inf.

Value

JAGS_bridgesampling returns an object of class 'bridge'.

Check input

Description

A set of convenience functions for checking objects/arguments to a function passed by a user.

Usage

check_bool(
  x,
  name,
  check_length = 1,
  allow_NULL = FALSE,
  allow_NA = TRUE,
  call = ""
)

check_char(
  x,
  name,
  check_length = 1,
  allow_values = NULL,
  allow_NULL = FALSE,
  allow_NA = TRUE,
  call = ""
)

check_real(
  x,
  name,
  lower = -Inf,
  upper = Inf,
  allow_bound = TRUE,
  check_length = 1,
  allow_NULL = FALSE,
  allow_NA = TRUE,
  call = ""
)

check_int(
  x,
  name,
  lower = -Inf,
  upper = Inf,
  allow_bound = TRUE,
  check_length = 1,
  allow_NULL = FALSE,
  allow_NA = TRUE,
  call = ""
)

check_list(
  x,
  name,
  check_length = 0,
  check_names = NULL,
  all_objects = FALSE,
  allow_other = FALSE,
  allow_NULL = FALSE,
  call = ""
)

Arguments

x

object to be checked

name

name of the object that will be print in the error message.

check_length

length of the object to be checked. Defaults to 1. Set to 0 in order to not check object length.

allow_NULL

whether the object can be NULL. If so, no checks are executed.

allow_NA

whether the object can contain NA or NaN values.

call

string to be placed as a prefix to the error call.

allow_values

names of values allowed in a character vector. Defaults to NULL (do not check).

lower

lower bound of allowed values. Defaults to -Inf (do not check).

upper

upper bound of allowed values. Defaults to Inf (do not check).

allow_bound

whether the values at the boundary are allowed. Defaults to TRUE.

check_names

names of entries allowed in a list. Defaults to NULL (do not check).

all_objects

whether all entries in check_names must be present. Defaults to FALSE.

allow_other

whether additional entries then the specified in check_names might be present

Value

returns NULL, called for the input check.

Examples

# check whether the object is logical
check_bool(TRUE, name = "input")

# will throw an error on any other type
## Not run: 
  check_bool("TRUE", name = "input")

## End(Not run)

Independent contrast matrix

Description

Return a matrix of independent contrasts – a level for each term.

Usage

contr.independent(n, contrasts = TRUE)

Arguments

n

a vector of levels for a factor, or the number of levels

contrasts

logical indicating whether contrasts should be computed

Value

A matrix with n rows and k columns, with k = n if contrasts = TRUE and k = n if contrasts = FALSE.

References

There are no references for Rd macro ⁠\insertAllCites⁠ on this help page.

Examples

contr.independent(c(1, 2))
contr.independent(c(1, 2, 3))

Mean difference contrast matrix

Description

Return a matrix of mean difference contrasts. This is an adjustment to the contr.orthonormal that ascertains that the prior distributions on difference between the gran mean and factor level are identical independent of the number of factor levels (which does not hold for the orthonormal contrast). Furthermore, the contrast is re-scaled so the specified prior distribution exactly corresponds to the prior distribution on difference between each factor level and the grand mean – this is approximately twice the scale of contr.orthonormal.

Usage

contr.meandif(n, contrasts = TRUE)

Arguments

n

a vector of levels for a factor, or the number of levels

contrasts

logical indicating whether contrasts should be computed

Value

A matrix with n rows and k columns, with k = n - 1 if contrasts = TRUE and k = n if contrasts = FALSE.

References

There are no references for Rd macro ⁠\insertAllCites⁠ on this help page.

Examples

contr.meandif(c(1, 2))
contr.meandif(c(1, 2, 3))

Orthornomal contrast matrix

Description

Return a matrix of orthornomal contrasts. Code is based on stanova::contr.bayes and corresponding to description by Rouder et al. (2012)

Usage

contr.orthonormal(n, contrasts = TRUE)

Arguments

n

a vector of levels for a factor, or the number of levels

contrasts

logical indicating whether contrasts should be computed

Value

A matrix with n rows and k columns, with k = n - 1 if contrasts = TRUE and k = n if contrasts = FALSE.

References

Rouder JN, Morey RD, Speckman PL, Province JM (2012). “Default Bayes factors for ANOVA designs.” Journal of Mathematical Psychology, 56(5), 356–374. doi:10.1016/j.jmp.2012.08.001.

Examples

contr.orthonormal(c(1, 2))
contr.orthonormal(c(1, 2, 3))

Prior density

Description

Computes density of a prior distribution across a range of values.

Usage

## S3 method for class 'prior'
density(
  x,
  x_seq = NULL,
  x_range = NULL,
  x_range_quant = NULL,
  n_points = 1000,
  n_samples = 10000,
  force_samples = FALSE,
  individual = FALSE,
  transformation = NULL,
  transformation_arguments = NULL,
  transformation_settings = FALSE,
  truncate_end = TRUE,
  ...
)

Arguments

x

a prior

x_seq

sequence of x coordinates

x_range

vector of length two with lower and upper range for the support (used if x_seq is unspecified)

x_range_quant

quantile used for automatically obtaining x_range if both x_range and x_seq are unspecified. Defaults to 0.005 for all but Cauchy, Student-t, Gamma, and Inverse-gamme distributions that use 0.010.

n_points

number of equally spaced points in the x_range if x_seq is unspecified

n_samples

number of samples from the prior distribution if the density cannot be obtained analytically (or if samples are forced with force_samples = TRUE)

force_samples

should prior be sampled instead of obtaining analytic solution whenever possible

individual

should individual densities be returned (e.g., in case of weightfunction)

transformation

transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations:

lin

linear transformation in form of a + b*x

tanh

also known as Fisher's z transformation

exp

exponential transformation

, or a list containing the transformation function fun, inverse transformation function inv, and the Jacobian of the transformation jac. See examples for details.

transformation_arguments

a list with named arguments for the transformation

transformation_settings

boolean indicating whether the settings the x_seq or x_range was specified on the transformed support

truncate_end

whether the density should be set to zero in for the endpoints of truncated distributions

...

additional arguments

Value

density.prior returns an object of class 'density'.

See Also

prior()

Compute posterior probabilities and inclusion Bayes factors

Description

Computes prior probabilities, posterior probabilities, and inclusion Bayes factors based either on (1) a list of models, vector of parameters, and a list of indicators the models represent the null or alternative hypothesis for each parameter, (2) on prior model odds, marginal likelihoods, and indicator whether the models represent the null or alternative hypothesis, or (3) list of models for each model.

Usage

compute_inference(prior_weights, margliks, is_null = NULL, conditional = FALSE)

ensemble_inference(model_list, parameters, is_null_list, conditional = FALSE)

models_inference(model_list)

Arguments

prior_weights

vector of prior model odds

margliks

vector of marginal likelihoods

is_null

logical vector of indicators specifying whether the model corresponds to the null or alternative hypothesis (or an integer vector indexing models corresponding to the null hypothesis)

conditional

whether prior and posterior model probabilities should be returned only for the conditional model. Defaults to FALSE

model_list

list of models, each of which contains marginal likelihood estimated with bridge sampling marglik and prior model odds prior_weights

parameters

vector of parameters names for which inference should be drawn

is_null_list

list with entries for each parameter carrying either logical vector of indicators specifying whether the model corresponds to the null or alternative hypothesis (or an integer vector indexing models corresponding to the null hypothesis)

Value

compute_inference returns a named list of prior probabilities, posterior probabilities, and Bayes factors, ppoint gives the distribution function, ensemble_inference gives a list of named lists of inferences for each parameter, and models_inference returns a list of models, each expanded by the inference list.

See Also

mix_posteriors BayesTools_ensemble_tables

Format Bayes factor

Description

Formats Bayes factor

Usage

format_BF(BF, logBF = FALSE, BF01 = FALSE, inclusion = FALSE)

Arguments

BF

Bayes factor(s)

logBF

log(BF)

BF01

1/BF

inclusion

whether the Bayes factor is an inclusion BF (for naming purposes)

Value

format_BF returns a formatted Bayes factor.

Add prior object to a ggplot

Description

Add prior object to a ggplot

Usage

geom_prior(
  x,
  xlim = NULL,
  x_seq = NULL,
  x_range_quant = NULL,
  n_points = 1000,
  n_samples = 10000,
  force_samples = FALSE,
  transformation = NULL,
  transformation_arguments = NULL,
  transformation_settings = FALSE,
  show_parameter = if (individual) 1 else NULL,
  individual = FALSE,
  rescale_x = FALSE,
  scale_y2 = 1,
  ...
)

Arguments

x

a prior

xlim

plotting range of the prior

x_seq

sequence of x coordinates

x_range_quant

quantile used for automatically obtaining x_range if both x_range and x_seq are unspecified. Defaults to 0.005 for all but Cauchy, Student-t, Gamma, and Inverse-gamme distributions that use 0.010.

n_points

number of equally spaced points in the x_range if x_seq is unspecified

n_samples

number of samples from the prior distribution if the density cannot be obtained analytically (or if samples are forced with force_samples = TRUE)

force_samples

should prior be sampled instead of obtaining analytic solution whenever possible

transformation

transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations:

lin

linear transformation in form of a + b*x

tanh

also known as Fisher's z transformation

exp

exponential transformation

, or a list containing the transformation function fun, inverse transformation function inv, and the Jacobian of the transformation jac. See examples for details.

transformation_arguments

a list with named arguments for the transformation

transformation_settings

boolean indicating whether the settings the x_seq or x_range was specified on the transformed support

show_parameter

which parameter should be returned in case of multiple parameters per prior. Useful when priors for the omega parameter are plotted and individual = TRUE.

individual

should individual densities be returned (e.g., in case of weightfunction)

rescale_x

allows to rescale x-axis in case a weightfunction is plotted.

scale_y2

scaling factor for a secondary axis

...

additional arguments

Value

geom_prior_list returns an object of class 'ggplot'.

See Also

plot.prior() lines.prior()

Add list of prior objects to a plot

Description

Add list of prior objects to a plot

Usage

geom_prior_list(
  prior_list,
  xlim = NULL,
  x_seq = NULL,
  x_range_quant = NULL,
  n_points = 500,
  n_samples = 10000,
  force_samples = FALSE,
  transformation = NULL,
  transformation_arguments = NULL,
  transformation_settings = FALSE,
  rescale_x = FALSE,
  scale_y2 = NULL,
  prior_list_mu = NULL,
  ...
)

Arguments

prior_list

list of prior distributions

xlim

x plotting range

x_seq

sequence of x coordinates

x_range_quant

quantile used for automatically obtaining x_range if both x_range and x_seq are unspecified. Defaults to 0.005 for all but Cauchy, Student-t, Gamma, and Inverse-gamme distributions that use 0.010.

n_points

number of equally spaced points in the x_range if x_seq is unspecified

n_samples

number of samples from the prior distribution if the density cannot be obtained analytically (or if samples are forced with force_samples = TRUE)

force_samples

should prior be sampled instead of obtaining analytic solution whenever possible

transformation

transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations:

lin

linear transformation in form of a + b*x

tanh

also known as Fisher's z transformation

exp

exponential transformation

, or a list containing the transformation function fun, inverse transformation function inv, and the Jacobian of the transformation jac. See examples for details.

transformation_arguments

a list with named arguments for the transformation

transformation_settings

boolean indicating whether the settings the x_seq or x_range was specified on the transformed support

rescale_x

allows to rescale x-axis in case a weightfunction is plotted.

scale_y2

scaling factor for a secondary axis

prior_list_mu

list of priors for the mu parameter required when plotting PET-PEESE

...

additional arguments

Value

geom_prior_list returns an object of class 'ggplot'.

See Also

plot_prior_list() lines_prior_list()

Compute inclusion Bayes factors

Description

Computes inclusion Bayes factors based on prior model probabilities, posterior model probabilities (or marginal likelihoods), and indicator whether the models represent the null or alternative hypothesis.

Usage

inclusion_BF(prior_probs, post_probs, margliks, is_null)

Arguments

prior_probs

vector of prior model probabilities

post_probs

vector of posterior model probabilities

margliks

vector of marginal likelihoods.

is_null

logical vector of indicators whether the model corresponds to the null or alternative hypothesis (or an integer vector indexing models corresponding to the null hypothesis)

Details

Supplying margliks as the input is preferred since it is better at dealing with under/overflow (posterior probabilities are very close to either 0 or 1). In case that both the post_probs and margliks are supplied, the results are based on margliks.

Value

inclusion_BF returns a Bayes factor.

Interpret ensemble inference and estimates

Description

Provides textual summary for posterior distributions created by mix_posteriors and ensemble inference created by ensemble_inference.

Usage

interpret(inference, samples, specification, method)

interpret2(specification, method = NULL)

Arguments

inference

model inference created by ensemble_inference

samples

posterior samples created by mix_posteriors

specification

list of lists specifying the generated text. Each inner list carries: (1) inference specifying the name of in the inference entry and optionally inference_name as a name to use in the text and inference_BF_name as a symbol to be used instead of the default "BF", (2) samples specifying the name of in the samples entry and optionally samples_name as a name to use in the text, samples_units as a unit text to be appended after the estimate, and samples_conditional specifying whether the estimate is conditional or model-averaged.

method

character specifying name of the method to be appended at the beginning of each sentence.

Value

interpret returns character.

See Also

ensemble_inference mix_posteriors BayesTools_model_tables BayesTools_ensemble_tables

Reports whether x is a a prior object

Description

Reports whether x is a a prior object. Note that point priors inherit the prior.simple property

Usage

is.prior(x)

is.prior.point(x)

is.prior.none(x)

is.prior.simple(x)

is.prior.discrete(x)

is.prior.vector(x)

is.prior.PET(x)

is.prior.PEESE(x)

is.prior.weightfunction(x)

is.prior.factor(x)

is.prior.orthonormal(x)

is.prior.treatment(x)

is.prior.independent(x)

is.prior.spike_and_slab(x)

is.prior.meandif(x)

is.prior.mixture(x)

Arguments

x

an object of test

Value

returns a boolean indicating whether the test object is a prior (of specific type).

Examples

# create some prior distributions
p0 <- prior(distribution = "point",  parameters = list(location = 0))
p1 <- prior_PET(distribution = "normal", parameters = list(mean = 0, sd = 1))

is.prior(p0)
is.prior.simple(p0)
is.prior.point(p0)
is.prior.PET(p0)

is.prior(p1)
is.prior.simple(p1)
is.prior.point(p1)
is.prior.PET(p1)

Kitchen Rolls data from Wagenmakers et al. (2015) replication study.

Description

The data set contains mean NEO PI-R scores for two groups of students. Each of them filled a personality questionnaire while rotating a kitchen roll either clock or counter-clock wise. See Wagenmakers et al. (2015) for more details about the replication study and the https://osf.io/uszvx/ for the original data.

Usage

kitchen_rolls

Format

A data.frame with 2 columns and 102 observations.

Value

a data.frame.

References

Wagenmakers E, Beek TF, Rotteveel M, Gierholz A, Matzke D, Steingroever H, Ly A, Verhagen J, Selker R, Sasiadek A, others (2015). “Turning the hands of time again: a purely confirmatory replication study and a Bayesian analysis.” Frontiers in Psychology, 6, 1–6. doi:10.3389/fpsyg.2015.00494.

Add prior object to a plot

Description

Add prior object to a plot

Usage

## S3 method for class 'prior'
lines(
  x,
  xlim = NULL,
  x_seq = NULL,
  x_range_quant = NULL,
  n_points = 1000,
  n_samples = 10000,
  force_samples = FALSE,
  transformation = NULL,
  transformation_arguments = NULL,
  transformation_settings = FALSE,
  show_parameter = if (individual) 1 else NULL,
  individual = FALSE,
  rescale_x = FALSE,
  scale_y2 = 1,
  ...
)

Arguments

x

a prior

xlim

plotting range of the prior

x_seq

sequence of x coordinates

x_range_quant

quantile used for automatically obtaining x_range if both x_range and x_seq are unspecified. Defaults to 0.005 for all but Cauchy, Student-t, Gamma, and Inverse-gamme distributions that use 0.010.

n_points

number of equally spaced points in the x_range if x_seq is unspecified

n_samples

number of samples from the prior distribution if the density cannot be obtained analytically (or if samples are forced with force_samples = TRUE)

force_samples

should prior be sampled instead of obtaining analytic solution whenever possible

transformation

transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations:

lin

linear transformation in form of a + b*x

tanh

also known as Fisher's z transformation

exp

exponential transformation

, or a list containing the transformation function fun, inverse transformation function inv, and the Jacobian of the transformation jac. See examples for details.

transformation_arguments

a list with named arguments for the transformation

transformation_settings

boolean indicating whether the settings the x_seq or x_range was specified on the transformed support

show_parameter

which parameter should be returned in case of multiple parameters per prior. Useful when priors for the omega parameter are plotted and individual = TRUE.

individual

should individual densities be returned (e.g., in case of weightfunction)

rescale_x

allows to rescale x-axis in case a weightfunction is plotted.

scale_y2

scaling factor for a secondary axis

...

additional arguments

Value

lines.prior returns NULL.

See Also

plot.prior() geom_prior()

Add list of prior objects to a plot

Description

Add list of prior objects to a plot

Usage

lines_prior_list(
  prior_list,
  xlim = NULL,
  x_seq = NULL,
  x_range_quant = NULL,
  n_points = 500,
  n_samples = 10000,
  force_samples = FALSE,
  transformation = NULL,
  transformation_arguments = NULL,
  transformation_settings = FALSE,
  rescale_x = FALSE,
  scale_y2 = NULL,
  prior_list_mu = NULL,
  ...
)

Arguments

prior_list

list of prior distributions

xlim

x plotting range

x_seq

sequence of x coordinates

x_range_quant

quantile used for automatically obtaining x_range if both x_range and x_seq are unspecified. Defaults to 0.005 for all but Cauchy, Student-t, Gamma, and Inverse-gamme distributions that use 0.010.

n_points

number of equally spaced points in the x_range if x_seq is unspecified

n_samples

number of samples from the prior distribution if the density cannot be obtained analytically (or if samples are forced with force_samples = TRUE)

force_samples

should prior be sampled instead of obtaining analytic solution whenever possible

transformation

transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations:

lin

linear transformation in form of a + b*x

tanh

also known as Fisher's z transformation

exp

exponential transformation

, or a list containing the transformation function fun, inverse transformation function inv, and the Jacobian of the transformation jac. See examples for details.

transformation_arguments

a list with named arguments for the transformation

transformation_settings

boolean indicating whether the settings the x_seq or x_range was specified on the transformed support

rescale_x

allows to rescale x-axis in case a weightfunction is plotted.

scale_y2

scaling factor for a secondary axis

prior_list_mu

list of priors for the mu parameter required when plotting PET-PEESE

...

additional arguments

Value

lines_prior_list returns NULL.

See Also

plot_prior_list() geom_prior_list()

Model-average marginal posterior distributions and marginal Bayes factors

Description

Creates marginal model-averaged and conditional posterior distributions based on a list of models, vector of parameters, formula, and a list of indicators of the null or alternative hypothesis models for each parameter. Computes inclusion Bayes factors for each marginal estimate via a Savage-Dickey density approximation.

Usage

marginal_inference(
  model_list,
  marginal_parameters,
  parameters,
  is_null_list,
  formula,
  null_hypothesis = 0,
  normal_approximation = FALSE,
  n_samples = 10000,
  seed = NULL,
  silent = FALSE
)

Arguments

model_list

list of models, each of which contains marginal likelihood estimated with bridge sampling marglik and prior model odds prior_weights

marginal_parameters

parameters for which the the marginal summary should be created

parameters

all parameters included in the model_list that are relevant for the formula (all of which need to have specification of is_null_list)

is_null_list

list with entries for each parameter carrying either logical vector of indicators specifying whether the model corresponds to the null or alternative hypothesis (or an integer vector indexing models corresponding to the null hypothesis)

formula

model formula (needs to be specified if parameter was part of a formula)

null_hypothesis

point null hypothesis to test. Defaults to 0

normal_approximation

whether the height of prior and posterior density should be approximated via a normal distribution (rather than kernel density). Defaults to FALSE.

n_samples

number of samples to be drawn for the model-averaged prior distribution

seed

seed for random number generation

silent

whether warnings should be returned silently. Defaults to FALSE

Value

marginal_inference returns an object of class 'marginal_inference'.

See Also

ensemble_inference mix_posteriors BayesTools_ensemble_tables

Model-average marginal posterior distributions

Description

Creates marginal model-averages posterior distributions for a given parameter based on model-averaged posterior samples and parameter name (and formula with at specification).

Usage

marginal_posterior(
  samples,
  parameter,
  formula = NULL,
  at = NULL,
  prior_samples = FALSE,
  use_formula = TRUE,
  transformation = NULL,
  transformation_arguments = NULL,
  transformation_settings = FALSE,
  n_samples = 10000,
  ...
)

Arguments

samples

model-averaged posterior samples created by mix_posteriors()

parameter

parameter of interest

formula

model formula (needs to be specified if parameter was part of a formula)

at

named list with predictor levels of the formula for which marginalization should be performed. If a predictor level is missing, 0 is used for continuous predictors, the baseline factor level is used for factors with contrast = "treatment" prior distributions, and the parameter is completely omitted for for factors with contrast = "meandif",

prior_samples

whether marginal prior distributions should be generated contrast = "orthonormal", and contrast = "independent" levels

use_formula

whether the parameter should be evaluated as a part of supplied formula

transformation

transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations:

lin

linear transformation in form of a + b*x

tanh

also known as Fisher's z transformation

exp

exponential transformation

, or a list containing the transformation function fun, inverse transformation function inv, and the Jacobian of the transformation jac. See examples for details.

transformation_arguments

a list with named arguments for the transformation

transformation_settings

boolean indicating whether the settings the x_seq or x_range was specified on the transformed support

n_samples

number of samples to be drawn for the model-averaged prior distribution

...

additional arguments

Value

marginal_posterior returns a named list of mixed marginal posterior distributions (either a vector of matrix). #'

Prior mean

Description

Computes mean of a prior distribution. (In case of orthonormal prior distributions for factors, the mean of for the deviations from intercept is returned.)

Usage

## S3 method for class 'prior'
mean(x, ...)

Arguments

x

a prior

...

unused

Value

a mean of an object of class 'prior'.

See Also

prior()

Examples

# create a standard normal prior distribution
p1 <- prior(distribution = "normal", parameters = list(mean = 1, sd = 1))

# compute mean of the prior distribution
mean(p1)

Model-average posterior distributions

Description

Model-averages posterior distributions based on a list of models, vector of parameters, and a list of indicators of the null or alternative hypothesis models for each parameter.

Usage

mix_posteriors(
  model_list,
  parameters,
  is_null_list,
  conditional = FALSE,
  seed = NULL,
  n_samples = 10000
)

Arguments

model_list

list of models, each of which contains marginal likelihood estimated with bridge sampling marglik and prior model odds prior_weights

parameters

vector of parameters names for which inference should be drawn

is_null_list

list with entries for each parameter carrying either logical vector of indicators specifying whether the model corresponds to the null or alternative hypothesis (or an integer vector indexing models corresponding to the null hypothesis)

conditional

whether prior and posterior model probabilities should be returned only for the conditional model. Defaults to FALSE

seed

integer specifying seed for sampling posteriors for model averaging. Defaults to NULL.

n_samples

number of samples to be drawn for the model-averaged posterior distribution

Value

mix_posteriors returns a named list of mixed posterior distributions (either a vector of matrix).

See Also

ensemble_inference BayesTools_ensemble_tables as_mixed_posteriors

Multivariate point mass distribution

Description

Density, distribution function, quantile function and random generation for multivariate point distribution.

Usage

dmpoint(x, location, log = FALSE)

rmpoint(n, location)

pmpoint(q, location, lower.tail = TRUE, log.p = FALSE)

qmpoint(p, location, lower.tail = TRUE, log.p = FALSE)

Arguments

x, q

vector or matrix of quantiles.

location

vector of locations corresponding to the location of individual points. Alternatively, a matrix with rows corresponding to the location of individual samples and columns correspond to the location of individual points.

log, log.p

logical; if TRUE, probabilities p are given as log(p).

n

number of observations.

lower.tail

logical; if TRUE (default), probabilities are P[X \le x], otherwise, P[X \ge x].

p

vector of probabilities.

Value

dpoint gives the density, ppoint gives the distribution function, qpoint gives the quantile function, and rpoint generates random deviates.

Examples

# draw samples from a multivariate point distribution
rmpoint(10, location = c(0, 1))

Clean parameter names from JAGS

Description

Removes additional formatting from parameter names outputted from JAGS.

Usage

format_parameter_names(
  parameters,
  formula_parameters = NULL,
  formula_prefix = TRUE
)

JAGS_parameter_names(parameters, formula_parameter = NULL)

Arguments

parameters

a vector of parameter names

formula_parameters

a vector of formula parameter prefix names

formula_prefix

whether the formula_parameters names should be kept. Defaults to TRUE.

formula_parameter

a formula parameter prefix name

Value

A character vector with reformatted parameter names.

Examples

format_parameter_names(c("mu_x_cont", "mu_x_fac3t", "mu_x_fac3t__xXx__x_cont"),
                       formula_parameters = "mu")

Plots a prior object

Description

Plots a prior object

Usage

## S3 method for class 'prior'
plot(
  x,
  plot_type = "base",
  x_seq = NULL,
  xlim = NULL,
  x_range_quant = NULL,
  n_points = 1000,
  n_samples = 10000,
  force_samples = FALSE,
  transformation = NULL,
  transformation_arguments = NULL,
  transformation_settings = FALSE,
  show_figures = if (individual) -1 else NULL,
  individual = FALSE,
  rescale_x = FALSE,
  par_name = NULL,
  ...
)

Arguments

x

a prior

plot_type

whether to use a base plot "base" or ggplot2 "ggplot" for plotting.

x_seq

sequence of x coordinates

xlim

x plotting range

x_range_quant

quantile used for automatically obtaining x_range if both x_range and x_seq are unspecified. Defaults to 0.005 for all but Cauchy, Student-t, Gamma, and Inverse-gamme distributions that use 0.010.

n_points

number of equally spaced points in the x_range if x_seq is unspecified

n_samples

number of samples from the prior distribution if the density cannot be obtained analytically (or if samples are forced with force_samples = TRUE)

force_samples

should prior be sampled instead of obtaining analytic solution whenever possible

transformation

transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations:

lin

linear transformation in form of a + b*x

tanh

also known as Fisher's z transformation

exp

exponential transformation

, or a list containing the transformation function fun, inverse transformation function inv, and the Jacobian of the transformation jac. See examples for details.

transformation_arguments

a list with named arguments for the transformation

transformation_settings

boolean indicating whether the settings the x_seq or x_range was specified on the transformed support

show_figures

which figures should be returned in case of multiple plots are generated. Useful when priors for the omega parameter are plotted and individual = TRUE.

individual

should individual densities be returned (e.g., in case of weightfunction)

rescale_x

allows to rescale x-axis in case a weightfunction is plotted.

par_name

a type of parameter for which the prior is specified. Only relevant if the prior corresponds to a mu parameter that needs to be transformed.

...

additional arguments

Value

plot.prior returns either NULL or an object of class 'ggplot' if plot_type is plot_type = "ggplot".

See Also

prior() lines.prior() geom_prior()

Examples

# create some prior distributions
p0 <- prior(distribution = "point",  parameters = list(location = 0))
p1 <- prior(distribution = "normal", parameters = list(mean = 0, sd = 1))
p2 <- prior(distribution = "normal", parameters = list(mean = 0, sd = 1), truncation = list(0, Inf))

# a default plot
plot(p0)

# manipulate line thickness and color, change the parameter name
plot(p1, lwd = 2, col = "blue", par_name = bquote(mu))

# use ggplot
plot(p2, plot_type = "ggplot")

# utilize the ggplot prior geom
plot(p2, plot_type = "ggplot", xlim = c(-2, 2)) + geom_prior(p1, col = "red", lty = 2)

# apply transformation
plot(p1, transformation = "exp")

Plot samples from the marginal posterior distributions

Description

Plot samples from the marginal posterior distributions

Usage

plot_marginal(
  samples,
  parameter,
  plot_type = "base",
  prior = FALSE,
  n_points = 1000,
  transformation = NULL,
  transformation_arguments = NULL,
  transformation_settings = FALSE,
  rescale_x = FALSE,
  par_name = NULL,
  dots_prior = list(),
  ...
)

Arguments

samples

samples from a posterior distribution for a parameter generated by marginal_inference.

parameter

parameter name to be plotted.

plot_type

whether to use a base plot "base" or ggplot2 "ggplot" for plotting.

prior

whether prior distribution should be added to the figure

n_points

number of equally spaced points in the x_range if x_seq is unspecified

transformation

transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations:

lin

linear transformation in form of a + b*x

tanh

also known as Fisher's z transformation

exp

exponential transformation

, or a list containing the transformation function fun, inverse transformation function inv, and the Jacobian of the transformation jac. See examples for details.

transformation_arguments

a list with named arguments for the transformation

transformation_settings

boolean indicating whether the settings the x_seq or x_range was specified on the transformed support

rescale_x

allows to rescale x-axis in case a weightfunction is plotted.

par_name

a type of parameter for which the prior is specified. Only relevant if the prior corresponds to a mu parameter that needs to be transformed.

dots_prior

additional arguments for the prior distribution plot

...

additional arguments

Value

plot_marginal returns either NULL or an object of class 'ggplot' if plot_type is plot_type = "ggplot".

See Also

prior() marginal_inference() plot_posterior()

Plot estimates from models

Description

Plot estimates from models

Usage

plot_models(
  model_list,
  samples,
  inference,
  parameter,
  plot_type = "base",
  prior = FALSE,
  conditional = FALSE,
  order = NULL,
  transformation = NULL,
  transformation_arguments = NULL,
  transformation_settings = FALSE,
  par_name = NULL,
  formula_prefix = TRUE,
  ...
)

Arguments

model_list

list of models, each of which contains marginal likelihood estimated with bridge sampling marglik and prior model odds prior_weights

samples

samples from a posterior distribution for a parameter generated by mix_posteriors.

inference

object created by ensemble_inference function

parameter

parameter name to be plotted. Does not support PET-PEESE and weightfunction.

plot_type

whether to use a base plot "base" or ggplot2 "ggplot" for plotting.

prior

whether prior distribution should be added to the figure

conditional

whether conditional models should be displayed

order

list specifying ordering of the models. The first element describes whether the ordering should be "increasing" or "decreasing" and the second element describes whether the ordering should be based "model" order, "estimate" size, posterior "probability", or the inclusion "BF".

transformation

transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations:

lin

linear transformation in form of a + b*x

tanh

also known as Fisher's z transformation

exp

exponential transformation

, or a list containing the transformation function fun, inverse transformation function inv, and the Jacobian of the transformation jac. See examples for details.

transformation_arguments

a list with named arguments for the transformation

transformation_settings

boolean indicating whether the settings the x_seq or x_range was specified on the transformed support

par_name

a type of parameter for which the prior is specified. Only relevant if the prior corresponds to a mu parameter that needs to be transformed.

formula_prefix

whether the formula_parameters names should be kept. Defaults to TRUE.

...

additional arguments. E.g.:

"show_updating"

whether Bayes factors and change from prior to posterior odds should be shown on the secondary y-axis

"show_estimates"

whether posterior estimates and 95% CI should be shown on the secondary y-axis

"y_axis2"

whether the secondary y-axis should be shown

Details

Plots prior and posterior estimates of the same parameter across multiple models (prior distributions with orthonormal/meandif contrast are always plotted as differences from the grand mean).

Value

plot_models returns either NULL or an object of class 'ggplot' if plot_type is plot_type = "ggplot".

See Also

prior() lines_prior_list() geom_prior_list()

Plot samples from the mixed posterior distributions

Description

Plot samples from the mixed posterior distributions

Usage

plot_posterior(
  samples,
  parameter,
  plot_type = "base",
  prior = FALSE,
  n_points = 1000,
  n_samples = 10000,
  force_samples = FALSE,
  transformation = NULL,
  transformation_arguments = NULL,
  transformation_settings = FALSE,
  rescale_x = FALSE,
  par_name = NULL,
  dots_prior = list(),
  ...
)

Arguments

samples

samples from a posterior distribution for a parameter generated by mix_posteriors.

parameter

parameter name to be plotted. Use "PETPEESE" for PET-PEESE plot with parameters "PET" and "PEESE", and "weightfunction" for plotting a weightfunction with parameters "omega".

plot_type

whether to use a base plot "base" or ggplot2 "ggplot" for plotting.

prior

whether prior distribution should be added to the figure

n_points

number of equally spaced points in the x_range if x_seq is unspecified

n_samples

number of samples from the prior distribution if the density cannot be obtained analytically (or if samples are forced with force_samples = TRUE)

force_samples

should prior be sampled instead of obtaining analytic solution whenever possible

transformation

transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations:

lin

linear transformation in form of a + b*x

tanh

also known as Fisher's z transformation

exp

exponential transformation

, or a list containing the transformation function fun, inverse transformation function inv, and the Jacobian of the transformation jac. See examples for details.

transformation_arguments

a list with named arguments for the transformation

transformation_settings

boolean indicating whether the settings the x_seq or x_range was specified on the transformed support

rescale_x

allows to rescale x-axis in case a weightfunction is plotted.

par_name

a type of parameter for which the prior is specified. Only relevant if the prior corresponds to a mu parameter that needs to be transformed.

dots_prior

additional arguments for the prior distribution plot

...

additional arguments

Value

plot_posterior returns either NULL or an object of class 'ggplot' if plot_type is plot_type = "ggplot".

See Also

prior() lines_prior_list() geom_prior_list()

Plot a list of prior distributions

Description

Plot a list of prior distributions

Usage

plot_prior_list(
  prior_list,
  plot_type = "base",
  x_seq = NULL,
  xlim = NULL,
  x_range_quant = NULL,
  n_points = 500,
  n_samples = 10000,
  force_samples = FALSE,
  transformation = NULL,
  transformation_arguments = NULL,
  transformation_settings = FALSE,
  rescale_x = FALSE,
  par_name = NULL,
  prior_list_mu = NULL,
  ...
)

Arguments

prior_list

list of prior distributions

plot_type

whether to use a base plot "base" or ggplot2 "ggplot" for plotting.

x_seq

sequence of x coordinates

xlim

x plotting range

x_range_quant

quantile used for automatically obtaining x_range if both x_range and x_seq are unspecified. Defaults to 0.005 for all but Cauchy, Student-t, Gamma, and Inverse-gamme distributions that use 0.010.

n_points

number of equally spaced points in the x_range if x_seq is unspecified

n_samples

number of samples from the prior distribution if the density cannot be obtained analytically (or if samples are forced with force_samples = TRUE)

force_samples

should prior be sampled instead of obtaining analytic solution whenever possible

transformation

transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations:

lin

linear transformation in form of a + b*x

tanh

also known as Fisher's z transformation

exp

exponential transformation

, or a list containing the transformation function fun, inverse transformation function inv, and the Jacobian of the transformation jac. See examples for details.

transformation_arguments

a list with named arguments for the transformation

transformation_settings

boolean indicating whether the settings the x_seq or x_range was specified on the transformed support

rescale_x

allows to rescale x-axis in case a weightfunction is plotted.

par_name

a type of parameter for which the prior is specified. Only relevant if the prior corresponds to a mu parameter that needs to be transformed.

prior_list_mu

list of priors for the mu parameter required when plotting PET-PEESE

...

additional arguments

Value

plot_prior_list returns either NULL or an object of class 'ggplot' if plot_type is plot_type = "ggplot".

See Also

prior() lines_prior_list() geom_prior_list()

Point mass distribution

Description

Density, distribution function, quantile function and random generation for point distribution.

Usage

dpoint(x, location, log = FALSE)

rpoint(n, location)

ppoint(q, location, lower.tail = TRUE, log.p = FALSE)

qpoint(p, location, lower.tail = TRUE, log.p = FALSE)

Arguments

x, q

vector or matrix of quantiles.

location

vector of locations.

log, log.p

logical; if TRUE, probabilities p are given as log(p).

n

number of observations.

lower.tail

logical; if TRUE (default), probabilities are P[X \le x], otherwise, P[X \ge x].

p

vector of probabilities.

Value

dpoint gives the density, ppoint gives the distribution function, qpoint gives the quantile function, and rpoint generates random deviates.

Examples

# draw samples from a point distribution
rpoint(10, location = 1)

Print a BayesTools table

Description

Print a BayesTools table

Usage

## S3 method for class 'BayesTools_table'
print(x, ...)

Arguments

x

a BayesTools_values_tables

...

additional arguments.

Value

print.BayesTools_table returns NULL.

Prints a prior object

Description

Prints a prior object

Usage

## S3 method for class 'prior'
print(
  x,
  short_name = FALSE,
  parameter_names = FALSE,
  plot = FALSE,
  digits_estimates = 2,
  silent = FALSE,
  ...
)

Arguments

x

a prior

short_name

whether prior distribution names should be shorted

parameter_names

whether parameter names should be printed

plot

to return bquote formatted prior name for plotting.

digits_estimates

number of decimals to be displayed for printed parameters.

silent

to silently return the print message.

...

additional arguments

Value

print.prior invisibly returns the print statement.

See Also

prior()

Examples

# create some prior distributions
p0 <- prior(distribution = "point",  parameters = list(location = 0))
p1 <- prior(distribution = "normal", parameters = list(mean = 0, sd = 1))

# print them
p0
p1

# use short names
print(p1, short_name = TRUE)

# print parameter names
print(p1, parameter_names = TRUE)

# generate bquote plotting syntax
plot(0, main = print(p1, plot = TRUE))

Creates a prior distribution

Description

prior creates a prior distribution. The prior can be visualized by the plot function.

Usage

prior(
  distribution,
  parameters,
  truncation = list(lower = -Inf, upper = Inf),
  prior_weights = 1
)

prior_none(prior_weights = 1)

Arguments

distribution

name of the prior distribution. The possible options are

"point"

for a point density characterized by a location parameter.

"normal"

for a normal distribution characterized by a mean and sd parameters.

"lognormal"

for a lognormal distribution characterized by a meanlog and sdlog parameters.

"cauchy"

for a Cauchy distribution characterized by a location and scale parameters. Internally converted into a generalized t-distribution with df = 1.

"t"

for a generalized t-distribution characterized by a location, scale, and df parameters.

"gamma"

for a gamma distribution characterized by either shape and rate, or shape and scale parameters. The later is internally converted to the shape and rate parametrization

"invgamma"

for an inverse-gamma distribution characterized by a shape and scale parameters. The JAGS part uses a 1/gamma distribution with a shape and rate parameter.

"beta"

for a beta distribution characterized by an alpha and beta parameters.

"exp"

for an exponential distribution characterized by either rate or scale parameter. The later is internally converted to rate.

"uniform"

for a uniform distribution defined on a range from a to b

parameters

list of appropriate parameters for a given distribution.

truncation

list with two elements, lower and upper, that define the lower and upper truncation of the distribution. Defaults to list(lower = -Inf, upper = Inf). The truncation is automatically set to the bounds of the support.

prior_weights

prior odds associated with a given distribution. The value is passed into the model fitting function, which creates models corresponding to all combinations of prior distributions for each of the model parameters and sets the model priors odds to the product of its prior distributions.

Value

prior and prior_none return an object of class 'prior'. A named list containing the distribution name, parameters, and prior weights.

See Also

plot.prior(), Normal, Lognormal, Cauchy, Beta, Exponential, LocationScaleT, InvGamma.

Examples

# create a standard normal prior distribution
p1 <- prior(distribution = "normal", parameters = list(mean = 1, sd = 1))

# create a half-normal standard normal prior distribution
p2 <- prior(distribution = "normal", parameters = list(mean = 1, sd = 1),
truncation = list(lower = 0, upper = Inf))

# the prior distribution can be visualized using the plot function
# (see ?plot.prior for all options)
plot(p1)

Creates a prior distribution for PET or PEESE models

Description

prior creates a prior distribution for fitting a PET or PEESE style models in RoBMA. The prior distribution can be visualized by the plot function.

Usage

prior_PET(
  distribution,
  parameters,
  truncation = list(lower = 0, upper = Inf),
  prior_weights = 1
)

prior_PEESE(
  distribution,
  parameters,
  truncation = list(lower = 0, upper = Inf),
  prior_weights = 1
)

Arguments

distribution

name of the prior distribution. The possible options are

"point"

for a point density characterized by a location parameter.

"normal"

for a normal distribution characterized by a mean and sd parameters.

"lognormal"

for a lognormal distribution characterized by a meanlog and sdlog parameters.

"cauchy"

for a Cauchy distribution characterized by a location and scale parameters. Internally converted into a generalized t-distribution with df = 1.

"t"

for a generalized t-distribution characterized by a location, scale, and df parameters.

"gamma"

for a gamma distribution characterized by either shape and rate, or shape and scale parameters. The later is internally converted to the shape and rate parametrization

"invgamma"

for an inverse-gamma distribution characterized by a shape and scale parameters. The JAGS part uses a 1/gamma distribution with a shape and rate parameter.

"beta"

for a beta distribution characterized by an alpha and beta parameters.

"exp"

for an exponential distribution characterized by either rate or scale parameter. The later is internally converted to rate.

"uniform"

for a uniform distribution defined on a range from a to b

parameters

list of appropriate parameters for a given distribution.

truncation

list with two elements, lower and upper, that define the lower and upper truncation of the distribution. Defaults to list(lower = -Inf, upper = Inf). The truncation is automatically set to the bounds of the support.

prior_weights

prior odds associated with a given distribution. The value is passed into the model fitting function, which creates models corresponding to all combinations of prior distributions for each of the model parameters and sets the model priors odds to the product of its prior distributions.

Value

prior_PET and prior_PEESE return an object of class 'prior'.

See Also

plot.prior(), prior()

Examples

# create a half-Cauchy prior distribution
# (PET and PEESE specific functions automatically set lower truncation at 0)
p1 <- prior_PET(distribution = "Cauchy", parameters = list(location = 0, scale = 1))

plot(p1)

Creates a prior distribution for factors

Description

prior_factor creates a prior distribution for fitting models with factor predictors. (Note that results across different operating systems might vary due to differences in JAGS numerical precision.)

Usage

prior_factor(
  distribution,
  parameters,
  truncation = list(lower = -Inf, upper = Inf),
  prior_weights = 1,
  contrast = "meandif"
)

Arguments

distribution

name of the prior distribution. The possible options are

"point"

for a point density characterized by a location parameter.

"normal"

for a normal distribution characterized by a mean and sd parameters.

"lognormal"

for a lognormal distribution characterized by a meanlog and sdlog parameters.

"cauchy"

for a Cauchy distribution characterized by a location and scale parameters. Internally converted into a generalized t-distribution with df = 1.

"t"

for a generalized t-distribution characterized by a location, scale, and df parameters.

"gamma"

for a gamma distribution characterized by either shape and rate, or shape and scale parameters. The later is internally converted to the shape and rate parametrization

"invgamma"

for an inverse-gamma distribution characterized by a shape and scale parameters. The JAGS part uses a 1/gamma distribution with a shape and rate parameter.

"beta"

for a beta distribution characterized by an alpha and beta parameters.

"exp"

for an exponential distribution characterized by either rate or scale parameter. The later is internally converted to rate.

"uniform"

for a uniform distribution defined on a range from a to b

parameters

list of appropriate parameters for a given distribution.

truncation

list with two elements, lower and upper, that define the lower and upper truncation of the distribution. Defaults to list(lower = -Inf, upper = Inf). The truncation is automatically set to the bounds of the support.

prior_weights

prior odds associated with a given distribution. The value is passed into the model fitting function, which creates models corresponding to all combinations of prior distributions for each of the model parameters and sets the model priors odds to the product of its prior distributions.

contrast

type of contrast for the prior distribution. The possible options are

"meandif"

for contrast centered around the grand mean with equal marginal distributions, making the prior distribution exchangeable across factor levels. In contrast to "orthonormal", the marginal distributions are identical regardless of the number of factor levels and the specified prior distribution corresponds to the difference from grand mean for each factor level. Only supports distribution = "mnormal" and distribution = "mt" which generates the corresponding multivariate normal/t distributions.

"orthonormal"

for contrast centered around the grand mean with equal marginal distributions, making the prior distribution exchangeable across factor levels. Only supports distribution = "mnormal" and distribution = "mt" which generates the corresponding multivariate normal/t distributions.

"treatment"

for contrasts using the first level as a comparison group and setting equal prior distribution on differences between the individual factor levels and the comparison level.

"independent"

for contrasts specifying dependent prior distribution for each factor level (note that this leads to an overparameterized model if the intercept is included).

Value

return an object of class 'prior'.

See Also

prior()

Examples

# create an orthonormal prior distribution
p1 <- prior_factor(distribution = "mnormal", contrast = "orthonormal",
                   parameters = list(mean = 0, sd = 1))

Elementary prior related functions

Description

Density (pdf / lpdf), distribution function (cdf / ccdf), quantile function (quant), random generation (rng), mean, standard deviation (sd), and marginal variants of the functions (mpdf, mlpf, mcdf, mccdf, mquant) for prior distributions.

Usage

## S3 method for class 'prior'
rng(x, n, ...)

## S3 method for class 'prior'
cdf(x, q, ...)

## S3 method for class 'prior'
ccdf(x, q, ...)

## S3 method for class 'prior'
lpdf(x, y, ...)

## S3 method for class 'prior'
pdf(x, y, ...)

## S3 method for class 'prior'
quant(x, p, ...)

## S3 method for class 'prior'
mcdf(x, q, ...)

## S3 method for class 'prior'
mccdf(x, q, ...)

## S3 method for class 'prior'
mlpdf(x, y, ...)

## S3 method for class 'prior'
mpdf(x, y, ...)

## S3 method for class 'prior'
mquant(x, p, ...)

Arguments

x

prior distribution

n

number of observations

...

unused arguments

q

vector or matrix of quantiles

y

vector of observations

p

vector of probabilities

Value

pdf (mpdf) and lpdf (mlpdf) give the (marginal) density and the log of (marginal) density, cdf (mcdf) and ccdf (mccdf) give the (marginal) distribution and the complement of (marginal) distribution function, quant (mquant) give the (marginal) quantile function, and rng generates random deviates for an object of class 'prior'.

Examples

# create a standard normal prior distribution
p1 <- prior(distribution = "normal", parameters = list(mean = 1, sd = 1))

# generate a random sample from the prior
rng(p1, 10)

# compute cumulative density function
cdf(p1, 0)

# obtain quantile
quant(p1, .5)

# compute probability density
pdf(p1, c(0, 1, 2))

Creates generics for common statistical functions

Description

Density (pdf / lpdf), distribution function (cdf / ccdf), quantile function (quant), random generation (rng), mean, standard deviation (sd), and marginal variants of the functions (mpdf, mlpf, mcdf, mccdf, mquant).

Usage

rng(x, ...)

cdf(x, ...)

ccdf(x, ...)

quant(x, ...)

lpdf(x, ...)

pdf(x, ...)

mcdf(x, ...)

mccdf(x, ...)

mquant(x, ...)

mlpdf(x, ...)

mpdf(x, ...)

Arguments

x

main argument

...

unused arguments

Value

pdf (mpdf) and lpdf (mlpdf) give the (marginal) density and the log of (marginal) density, cdf (mcdf) and ccdf (mccdf) give the (marginal) distribution and the complement of (marginal) distribution function, quant (mquant) give the (marginal) quantile function, and rng generates random deviates for an object of class 'prior'.

The pdf function proceeds to PDF graphics device if x is a character.

Creates an informed prior distribution based on research

Description

prior_informed creates an informed prior distribution based on past research. The prior can be visualized by the plot function.

Usage

prior_informed(name, parameter = NULL, type = "smd")

Arguments

name

name of the prior distribution. There are many options based on prior psychological or medical research. For psychology, the possible options are

"van Erp"

for an informed prior distribution for the heterogeneity parameter tau of meta-analytic effect size estimates based on standardized mean differences (van Erp et al. 2017),

"Oosterwijk"

for an informed prior distribution for the effect sizes expected in social psychology based on prior elicitation with dr. Oosterwijk (Gronau et al. 2017).

For medicine, the possible options are based on Bartoš et al. (2021) and Bartoš et al. (2023) who developed empirical prior distributions for the effect size and heterogeneity parameters of the continuous outcomes (standardized mean differences), dichotomous outcomes (logOR, logRR, and risk differences), and time to event outcomes (logHR) based on the Cochrane database of systematic reviews. Use "Cochrane" for a prior distribution based on the whole database or call print(prior_informed_medicine_names) to inspect the names of all 46 subfields and set the appropriate parameter and type.

parameter

parameter name describing what prior distribution is supposed to be produced in cases where the name corresponds to multiple prior distributions. Relevant only for the empirical medical prior distributions.

type

prior type describing what prior distribution is supposed to be produced in cases where the name and parameter correspond to multiple prior distributions. Relevant only for the empirical medical prior distributions with the following options

"smd"

for standardized mean differences

"logOR"

for log odds ratios

"logRR"

for log risk ratios

"RD"

for risk differences

"logHR"

for hazard ratios

Value

prior_informed returns an object of class 'prior'.

References

Bartoš F, Gronau QF, Timmers B, Otte WM, Ly A, Wagenmakers E (2021). “Bayesian model-averaged meta-analysis in medicine.” Statistics in Medicine, 40(30), 6743–6761. doi:10.1002/sim.9170.

Bartoš F, Otte WM, Gronau QF, Timmers B, Ly A, Wagenmakers E (2023). “Empirical prior distributions for Bayesian meta-analyses of binary and time-to-event outcomes.” doi:10.48550/arXiv.2306.11468, preprint at https://doi.org/10.48550/arXiv.2306.11468.

Gronau QF, Van Erp S, Heck DW, Cesario J, Jonas KJ, Wagenmakers E (2017). “A Bayesian model-averaged meta-analysis of the power pose effect with informed and default priors: The case of felt power.” Comprehensive Results in Social Psychology, 2(1), 123–138. doi:10.1080/23743603.2017.1326760.

van Erp S, Verhagen J, Grasman RP, Wagenmakers E (2017). “Estimates of between-study heterogeneity for 705 meta-analyses reported in Psychological Bulletin from 1990–2013.” Journal of Open Psychology Data, 5(1). doi:10.5334/jopd.33.

See Also

prior(), prior_informed_medicine_names

Examples

# prior distribution representing expected effect sizes in social psychology
# based on prior elicitation with dr. Oosterwijk
p1 <- prior_informed("Oosterwijk")

# the prior distribution can be visualized using the plot function
# (see ?plot.prior for all options)
plot(p1)


# empirical prior distribution for the standardized mean differences from the oral health
# medical subfield based on meta-analytic effect size estimates from the
# Cochrane database of systematic reviews
p2 <- prior_informed("Oral Health", parameter ="effect", type ="smd")
print(p2)

Names of medical subfields from the Cochrane database of systematic reviews

Description

Contain names identifying the individual subfields from the Cochrane database of systematic reviews. The individual elements correspond to valid name arguments for the prior_informed() function.

Usage

prior_informed_medicine_names

Format

An object of class character of length 57.

Value

returns a character vector with names of medical subfields from Cochrane database of systematic reviews.

See Also

prior_informed()

Examples

print(prior_informed_medicine_names)

Creates a mixture of prior distributions

Description

prior_mixture creates a mixture of prior distributions. This is a more generic version of the prior_spike_and_slab function.

Usage

prior_mixture(
  prior_list,
  is_null = rep(FALSE, length(prior_list)),
  components = NULL
)

Arguments

prior_list

a list of prior distributions to be mixed.

is_null

a logical vector indicating which of the prior distributions should be considered as a null distribution. Defaults to rep(FALSE, length(prior_list)).

components

a character vector indicating which of the prior distributions belong to the same mixture component (this is an alternative specification to the is_null argument). Defaults to NULL (i.e., is_null is used.

See Also

prior()

Creates a spike and slab prior distribution

Description

prior_spike_and_slab creates a spike and slab prior distribution corresponding to the specification in Kuo and Mallick (1998) (see O'Hara and Sillanpää (2009) for further details). I.e., a prior distribution is multiplied by an independent indicator with values either zero or one.

Usage

prior_spike_and_slab(
  prior_parameter,
  prior_inclusion = prior(distribution = "spike", parameters = list(location = 0.5)),
  prior_weights = 1
)

Arguments

prior_parameter

a prior distribution for the parameter

prior_inclusion

a prior distribution for the inclusion probability. The inclusion probability must be bounded within 0 and 1 range. Defaults to prior("spike", parameters = list(location = 0.5)) which corresponds to 1/2 prior probability of including the slab prior distribution (but other prior distributions, like beta etc can be also specified).

prior_weights

prior odds associated with a given distribution. The value is passed into the model fitting function, which creates models corresponding to all combinations of prior distributions for each of the model parameters and sets the model priors odds to the product of its prior distributions.

Value

return an object of class 'prior'.

See Also

prior()

Examples

# create a spike and slab prior distribution
p1 <- prior_spike_and_slab(
   prior(distribution = "normal", parameters = list(mean = 0, sd = 1)),
   prior_inclusion = prior(distribution = "beta", parameters = list(alpha = 1, beta = 1))
)

Creates a prior distribution for a weight function

Description

prior_weightfunction creates a prior distribution for fitting a RoBMA selection model. The prior can be visualized by the plot function.

Usage

prior_weightfunction(distribution, parameters, prior_weights = 1)

Arguments

distribution

name of the prior distribution. The possible options are

"two.sided"

for a two-sided weight function characterized by a vector steps and vector alpha parameters. The alpha parameter determines an alpha parameter of Dirichlet distribution which cumulative sum is used for the weights omega.

"one.sided"

for a one-sided weight function characterized by either a vector steps and vector alpha parameter, leading to a monotonic one-sided function, or by a vector steps, vector alpha1, and vector alpha2 parameters leading non-monotonic one-sided weight function. The alpha / alpha1 and alpha2 parameters determine an alpha parameter of Dirichlet distribution which cumulative sum is used for the weights omega.

parameters

list of appropriate parameters for a given distribution.

prior_weights

prior odds associated with a given distribution. The model fitting function usually creates models corresponding to all combinations of prior distributions for each of the model parameters, and sets the model priors odds to the product of its prior distributions.

Details

Constrained cases of weight functions can be specified by adding ".fixed" after the distribution name, i.e., "two.sided.fixed" and "one.sided.fixed". In these cases, the functions are specified using steps and omega parameters, where the omega parameter is a vector of weights that corresponds to the relative publication probability (i.e., no parameters are estimated).

Value

prior_weightfunction returns an object of class 'prior'.

See Also

plot.prior()

Examples

p1 <- prior_weightfunction("one-sided", parameters = list(steps = c(.05, .10), alpha = c(1, 1, 1)))

# the prior distribution can be visualized using the plot function
# (see ?plot.prior for all options)
plot(p1)

Prior range

Description

Computes range of a prior distribution (if the prior distribution is unbounded range from quantiles to 1 -quantiles) is returned.

Usage

## S3 method for class 'prior'
range(x, quantiles = NULL, ..., na.rm = FALSE)

Arguments

x

a prior

quantiles

quantile to be returned in case of unbounded distribution.

...

additional arguments

na.rm

unused

Value

range.prior returns a numeric vector of length with a plotting range of a prior distribution.

See Also

prior()

Removes column to BayesTools table

Description

Removes column to a BayesTools table while not breaking formatting, attributes, etc...

Usage

remove_column(table, column_position = NULL)

Arguments

table

BayesTools table

column_position

position of the to be removed column (defaults to NULL which removes the last column)

Value

returns an object of 'BayesTools_table' class.

Creates generic for sd function

Description

Creates generic for sd function

Usage

sd(x, ...)

Arguments

x

main argument

...

additional arguments

Value

sd returns a standard deviation of the supplied object (if it is either a numeric vector or an object of class 'prior').

See Also

sd

Prior sd

Description

Computes standard deviation of a prior distribution.

Usage

## S3 method for class 'prior'
sd(x, ...)

Arguments

x

a prior

...

unused arguments

Value

a standard deviation of an object of class 'prior'.

See Also

prior()

Examples

# create a standard normal prior distribution
p1 <- prior(distribution = "normal", parameters = list(mean = 1, sd = 1))

# compute sd of the prior distribution
sd(p1)

Transform factor posterior samples into differences from the mean

Description

Transforms posterior samples from model-averaged posterior distributions based on meandif/orthonormal prior distributions into differences from the mean.

Usage

transform_factor_samples(samples)

Arguments

samples

(a list) of mixed posterior distributions created with mix_posteriors function

Value

transform_meandif_samples returns a named list of mixed posterior distributions (either a vector of matrix).

See Also

mix_posteriors transform_meandif_samples transform_meandif_samples transform_orthonormal_samples

Transform meandif posterior samples into differences from the mean

Description

Transforms posterior samples from model-averaged posterior distributions based on meandif prior distributions into differences from the mean.

Usage

transform_meandif_samples(samples)

Arguments

samples

(a list) of mixed posterior distributions created with mix_posteriors function

Value

transform_meandif_samples returns a named list of mixed posterior distributions (either a vector of matrix).

See Also

mix_posteriors contr.meandif

Transform orthonomal posterior samples into differences from the mean

Description

Transforms posterior samples from model-averaged posterior distributions based on orthonormal prior distributions into differences from the mean.

Usage

transform_orthonormal_samples(samples)

Arguments

samples

(a list) of mixed posterior distributions created with mix_posteriors function

Value

transform_orthonormal_samples returns a named list of mixed posterior distributions (either a vector of matrix).

See Also

mix_posteriors contr.orthonormal

Updates BayesTools table

Description

Updates BayesTools table while not breaking formatting, attributes, etc...

Usage

## S3 method for class 'BayesTools_table'
update(
  object,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  remove_parameters = NULL,
  logBF = FALSE,
  BF01 = FALSE,
  ...
)

Arguments

object

a BayesTools table

title

title of the table

footnotes

add footnotes to the table

warnings

add warnings of the table

remove_parameters

remove parameters from the table

logBF

whether to format Bayes factors as log(BF)

BF01

whether to format Bayes factors as 1/BF

...

additional arguments.

Value

returns an object of 'BayesTools_table' class.

Creates generic for var function

Description

Creates generic for var function

Usage

var(x, ...)

Arguments

x

main argument

...

additional arguments

Value

var returns a variance of the supplied object (if it is either a numeric vector or an object of class 'prior').

See Also

cor

Prior var

Description

Computes variance of a prior distribution.

Usage

## S3 method for class 'prior'
var(x, ...)

Arguments

x

a prior

...

unused arguments

Value

a variance of an object of class 'prior'.

See Also

prior()

Examples

# create a standard normal prior distribution
p1 <- prior(distribution = "normal", parameters = list(mean = 1, sd = 1))

# compute variance of the prior distribution
var(p1)

Weight functions

Description

Marginal density, marginal distribution function, marginal quantile function and random generation for weight functions.

Usage

mdone.sided(x, alpha = NULL, alpha1 = NULL, alpha2 = NULL, log = FALSE)

mdtwo.sided(x, alpha, log = FALSE)

mdone.sided_fixed(x, omega, log = FALSE)

mdtwo.sided_fixed(x, omega, log = FALSE)

rone.sided(n, alpha = NULL, alpha1 = NULL, alpha2 = NULL)

rtwo.sided(n, alpha)

rone.sided_fixed(n, omega)

rtwo.sided_fixed(n, omega)

mpone.sided(
  q,
  alpha = NULL,
  alpha1 = NULL,
  alpha2 = NULL,
  lower.tail = TRUE,
  log.p = FALSE
)

mptwo.sided(q, alpha, lower.tail = TRUE, log.p = FALSE)

mpone.sided_fixed(q, omega, lower.tail = TRUE, log.p = FALSE)

mptwo.sided_fixed(q, omega, lower.tail = TRUE, log.p = FALSE)

mqone.sided(
  p,
  alpha = NULL,
  alpha1 = NULL,
  alpha2 = NULL,
  lower.tail = TRUE,
  log.p = FALSE
)

mqtwo.sided(p, alpha, lower.tail = TRUE, log.p = FALSE)

mqone.sided_fixed(p, omega, lower.tail = TRUE, log.p = FALSE)

mqtwo.sided_fixed(p, omega, lower.tail = TRUE, log.p = FALSE)

Arguments

x, q

vector or matrix of quantiles.

alpha

vector or matrix with concentration parameters for the Dirichlet distribution for a monotonic one.sided or a two.sided weight function.

alpha1

vector or matrix with concentration parameters for the Dirichlet distribution for the expected direction of non-monotonic one.sided of weight function.

alpha2

vector or matrix with concentration parameters for the Dirichlet distribution for the unexpected direction of non-monotonic one.sided of weight function.

log, log.p

logical; if TRUE, probabilities p are given as log(p).

omega

vector or matrix of fixed probabilities for a one.sided or a two.sided weight function.

n

number of observations.

lower.tail

logical; if TRUE (default), probabilities are P[X \le x], otherwise, P[X \ge x].

p

vector of probabilities.

Value

mdone.sided, mdtwo.sided, mdone.sided_fixed, and mdtwo.sided_fixed give the marginal density, mpone.sided, mptwo.sided, mpone.sided_fixed, and mptwo.sided_fixed give the marginal distribution function, mqone.sided, mqtwo.sided, mqone.sided_fixed, and mqtwo.sided_fixed give the marginal quantile function, and rone.sided, rtwo.sided, rone.sided_fixed, and rtwo.sided_fixed generate random deviates.

Examples

# draw samples from a two-sided weight function
rtwo.sided(10, alpha = c(1, 1))

# draw samples from a monotone one-sided weight function
rone.sided(10, alpha = c(1, 1, 1))

# draw samples from a non-monotone one-sided weight function
rone.sided(10, alpha1 = c(1, 1), alpha2 = c(1, 1))

Create coefficient mapping between multiple weightfunctions

Description

Creates coefficients mapping between multiple weightfunctions.

Usage

weightfunctions_mapping(prior_list, cuts_only = FALSE, one_sided = FALSE)

Arguments

prior_list

list of prior distributions

cuts_only

whether only p-value cuts should be returned

one_sided

force one-sided output

Value

weightfunctions_mapping returns a list of indices mapping the publication weights omega from the individual weightfunctions into a joint weightfunction.

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