Computes Bayes factor for equality constrained binomial parameters.
Null hypothesis \(H_0\) states that binomial proportions are exactly equal or
exactly equal and equal to p
.
Alternative hypothesis \(H_e\) states that binomial proportions are free to vary.
binom_bf_equality(x, n = NULL, a, b, p = NULL)
Returns a data.frame
containing the Bayes factors LogBFe0
, BFe0
, and BF0e
a vector of counts of successes, or a two-dimensional table (or matrix) with 2 columns, giving the counts of successes and failures, respectively
numeric. Vector of counts of trials. Must be the same length as x
. Ignored if x
is a matrix or a table
numeric. Vector with alpha parameters. Must be the same length as x
. Default sets all alpha parameters to 1
numeric. Vector with beta parameters. Must be the same length as x
. Default sets all beta parameters to 1
numeric. Hypothesized probability of success. Must be greater than 0 and less than 1. Default sets all binomial proportions exactly equal without specifying a specific value.
The model assumes that the data in x
(i.e., \(x_1, ..., x_K\)) are the observations of \(K\) independent
binomial experiments, based on \(n_1, ..., n_K\) observations. Hence, the underlying likelihood is the product of the
\(k = 1, ..., K\) individual binomial functions:
$$(x_1, ... x_K) ~ \prod Binomial(N_k, \theta_k)$$
Furthermore, the model assigns a beta distribution as prior to each model parameter
(i.e., underlying binomial proportions). That is:
$$\theta_k ~ Beta(\alpha_k, \beta_k)$$
damien2001samplingmultibridge
gronau2017tutorialmultibridge
fruhwirth2004estimatingmultibridge
sarafoglou2020evaluatingPreprintmultibridge
Other functions to evaluate informed hypotheses:
binom_bf_inequality()
,
binom_bf_informed()
,
mult_bf_equality()
,
mult_bf_inequality()
,
mult_bf_informed()
data(journals)
x <- journals$errors
n <- journals$nr_NHST
a <- rep(1, nrow(journals))
b <- rep(1, nrow(journals))
binom_bf_equality(x=x, n=n, a=a, b=b)
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