porsPs: Probability of replication success based on the sceptical...

In BayesRepDesign: Bayesian Design of Replication Studies

Probability of replication success based on the sceptical p-value

Description

This function computes the probability to achieve replication success based on the sceptical p-value.

Usage

porsPs(level, dprior, sr)

Arguments

level	Threshold for the (one-sided) sceptical p-value below which replication success is achieved
dprior	Design prior object
sr	Replication standard error

Details

The sceptical p-value is assumed to be uncalibrated as in Held (2020). The package ReplicationSuccess allows for sample size and power calculations with the recalibrated sceptical p-value (https://CRAN.R-project.org/package=ReplicationSuccess).

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")}

Held, L. (2020). A new standard for the analysis and design of replication studies (with discussion). Journal of the Royal Statistical Society: Series A (Statistics in Society), 183(2), 431-448. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssa.12493")}

Examples

## specify design prior
to1 <- 0.2
so1 <- 0.05
dprior <- designPrior(to = to1, so = so1)
porsPs(level = 0.025, dprior = dprior, sr = c(0.05, 0.01))

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