pbf: Product Bayes Factor

In bain: Bayes Factors for Informative Hypotheses

Product Bayes Factor

Description

The product Bayes factor (PBF) aggregates evidence for an informative hypothesis across conceptual replication studies without imposing assumptions about heterogeneity.

Usage

pbf(...)

## Default S3 method:
pbf(x, ...)

## S3 method for class 'numeric'
pbf(yi, vi, ni, hypothesis = "y = 0", ...)

Arguments

...	Additional arguments passed to 'bain'.
x	An object for which a method exists, see Details.
yi	Numeric vector with the observed effect sizes.
vi	Numeric vector with the observed sampling variances.
ni	Integer vector with the sample sizes.
hypothesis	A character string containing the informative hypotheses to evaluate.

Details

Currently, the argument 'x' accepts either: * A list of 'bain' objects, resulting from a call to 'bain'. * A list of model objects for which a 'bain' method exists; in this case, 'pbf' will call 'bain' on these model objects before aggregating the Bayes factors.

Value

A 'data.frame' of class 'pbf'.

References

Van Lissa, C. J., Kuiper, R. M., & Clapper, E. (2023, April 25). Aggregating evidence from conceptual replication studies using the product Bayes factor. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.31234/osf.io/nvqpw")}

Examples

pbf(yi = c(-.33, .32, .39, .31),
    vi = c(.085, .034, .016, .071),
    ni = c(7, 10, 13, 20))

bain documentation built on Sept. 27, 2023, 5:06 p.m.