multibridge (version 1.1.0)

mult_bf_equality: Computes Bayes Factors For Equality Constrained Multinomial Parameters

Description

Computes Bayes factor for equality constrained multinomial parameters using the standard Bayesian multinomial test. Null hypothesis \(H_0\) states that category proportions are exactly equal to those specified in p. Alternative hypothesis \(H_e\) states that category proportions are free to vary.

Usage

mult_bf_equality(x, a, p = rep(1/length(a), length(a)))

Value

Returns a data.frame containing the Bayes factors LogBFe0, BFe0, and BF0e

Arguments

x

numeric. Vector with data

a

numeric. Vector with concentration parameters of Dirichlet distribution. Must be the same length as x. Default sets all concentration parameters to 1

p

numeric. A vector of probabilities of the same length as x. Its elements must be greater than 0 and less than 1. Default is 1/K

Details

The model assumes that data follow a multinomial distribution and assigns a Dirichlet distribution as prior for the model parameters (i.e., underlying category proportions). That is: $$x ~ Multinomial(N, \theta)$$ $$\theta ~ Dirichlet(\alpha)$$

References

damien2001samplingmultibridge

gronau2017tutorialmultibridge

fruhwirth2004estimatingmultibridge

sarafoglou2020evaluatingPreprintmultibridge

See Also

Other functions to evaluate informed hypotheses: binom_bf_equality(), binom_bf_inequality(), binom_bf_informed(), mult_bf_inequality(), mult_bf_informed()

Examples

data(lifestresses)
x <- lifestresses$stress.freq
a <- rep(1, nrow(lifestresses))
mult_bf_equality(x=x, a=a)