nmbf01: Normal moment prior Bayes factor

In bfpwr: Power and Sample Size Calculations for Bayes Factor Analysis

Normal moment prior Bayes factor

Description

This function computes the Bayes factor that quantifies the evidence that the data (in the form of an asymptotically normally distributed parameter estimate with standard error) provide for a point null hypothesis with a normal moment prior assigned to the parameter under the alternative.

Usage

nmbf01(estimate, se, null = 0, psd, log = FALSE)

Arguments

estimate	Parameter estimate
se	Standard error of the parameter estimate
null	Parameter value under the point null hypothesis. Defaults to 0
psd	Spread of the normal moment prior assigned to the parameter under the alternative. The modes of the prior are located at \pm\sqrt{2}\,\code{psd}
log	Logical indicating whether the natural logarithm of the Bayes factor should be returned. Defaults to FALSE

Details

A normal moment prior has density f(x \mid \code{null}, \code{psd}) = N(x \mid \code{null}, \code{psd}^2) \times (x - \code{null})/ \code{psd}^2 with N(x \mid m, v) the normal density with mean m and variance v evaluated at x.

Value

Bayes factor in favor of the null hypothesis over the alternative (\text{BF}_{01} > 1 indicates evidence for the null hypothesis, whereas \text{BF}_{01} < 1 indicates evidence for the alternative)

Author(s)

Samuel Pawel

References

Johnson, V. E. and Rossell, D. (2010). On the use of non-local prior densities in Bayesian hypothesis tests. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(2):143–170. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.1467-9868.2009.00730.x")}

Pramanik, S. and Johnson, V. E. (2024). Efficient alternatives for Bayesian hypothesis tests in psychology. Psychological Methods, 29(2):243–261. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/met0000482")}

See Also

nmbf01, pnmbf01, nnmbf01, powernmbf01

Examples

nmbf01(estimate = 0.25, se = 0.05, null = 0, psd = 0.5/sqrt(2)) # mode at 0.5

bfpwr documentation built on June 8, 2025, 1:40 p.m.