summary.bmult {multibridge}

summary method for class bmult

Description

Summarizes results from Bayes factor analysis

Usage

## S3 method for class 'bmult'
summary(object, ...)

Arguments

object

object of class bmult as returned from mult_bf_informed or binom_bf_informed

...

additional arguments, currently ignored

Value

Invisibly returns a list which contains the Bayes factor and associated hypotheses for the full model, but also the separate for the independent equality and inequality constraints.

The summary method returns a list with the following elements:

$hyp

Vector containing the informed hypothesis as specified by the user

$bf

Contains Bayes factor

$logmlHe

Contains log marginal likelihood of the encompassing model

$logmlH0

Contains log marginal likelihood of the null model

$logmlHr

Contains log marginal likelihood of the informed model

$re2

Contains relative mean-square error for the Bayes factor

$bf_type

Contains Bayes factor type as specified by the user

$cred_level

Credible interval for the posterior point estimates.

$prior

List containing the prior parameters.

$data

List containing the data.

$nr_equal

Number of independent equality-constrained hypotheses.

$nr_inequal

Number of independent inequality-constrained hypotheses.

$estimates

Parameter estimates for the encompassing model

• factor_level: Vector with category names

• alpha: Vector with posterior concentration parameters of Dirichlet distribution (for multinomial models) or alpha parameters for independent beta distributions (for binomial models)

• beta: Vector with beta parameters for independent beta distributions (for binomial models)

• lower: Lower value of credible intervals of marginal beta distributions

• median: Posterior median of marginal beta distributions

• upper: Upper value of credible intervals of marginal beta distributions

Examples

# data
x <- c(3, 4, 10, 11)
n <- c(15, 12, 12, 12)
# priors
a <- c(1, 1, 1, 1)
b <- c(1, 1, 1, 1)
# informed hypothesis
factor_levels <- c('theta1', 'theta2', 'theta3', 'theta4')
Hr            <- c('theta1', '<',  'theta2', '<', 'theta3', '<', 'theta4')

## Binomial Case
out_binom  <- binom_bf_informed(x=x, n=n, Hr=Hr, a=a, b=b, niter=1e3,factor_levels, seed=2020)
summary(out_binom)
## Multinomial Case
out_mult  <- mult_bf_informed(x=x, Hr=Hr, a=a, niter=1e3,factor_levels, seed=2020)
summary(out_mult)