| porsPs {BayesRepDesign} | R Documentation |
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. 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. 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))