BayesRepDesign (version 0.42)

porsPs: 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)

Value

The probability to achieve replication success

Arguments

level

Threshold for the (one-sided) sceptical p-value below which replication success is achieved

dprior

Design prior object

sr

Replication standard error

Author

Samuel Pawel

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).

References

Pawel, S., Consonni, G., and Held, L. (2022). Bayesian approaches to designing replication studies. arXiv preprint. 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. 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))