porsEqu: Probability of replication success based on effect size...

In BayesRepDesign: Bayesian Design of Replication Studies

Probability of replication success based on effect size equivalence

Description

This function computes the probability to achieve replication success on equivalence of original and replication effect size. Effect size equivalence is defined by the confidence interval for the difference between the original and replication effect sizes falling within an equivalence region around zero defined by the specified margin.

Usage

porsEqu(level, dprior, margin, sr)

Arguments

level	1 - confidence level of confidence interval for effect size difference
dprior	Design prior object
margin	The equivalence margin > 0 for the symmetric equivalence region around zero
sr	Replication standard error

Value

The probability to achieve replication success

Author(s)

Samuel Pawel

References

Pawel, S., Consonni, G., and Held, L. (2022). Bayesian approaches to designing replication studies. arXiv preprint. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2211.02552")}

Anderson, S. F. and Maxwell, S. E. (2016). There's more than one way to conduct a replication study: Beyond statistical significance. Psychological Methods, 21(1), 1-12. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/met0000051")}

Examples

## specify design prior
to1 <- 2
so1 <- 0.05
dprior <- designPrior(to = to1, so = so1, tau = 0.1)
porsEqu(level = 0.1, dprior = dprior, margin = 0.3, sr = c(0.05, 0.03))

BayesRepDesign documentation built on May 4, 2023, 1:07 a.m.