ssdBF01 {BayesRepDesign}

Sample size determination for replication success based on Bayes factor

Description

This function computes the standard error required to achieve replication success with a certain probability and based on the Bayes factor under normality. The Bayes factor is oriented so that values above one indicate evidence for the null hypothesis of the effect size being zero, whereas values below one indicate evidence for the hypothesis of the effect size being non-zero (with normal prior assigned to it).

Usage

ssdBF01(
  level,
  dprior,
  power,
  priormean = 0,
  priorvar = 1,
  searchInt = c(.Machine$double.eps^0.5, 2)
)

Arguments

level

Bayes factor level below which replication success is achieved

dprior

Design prior object

power

Desired probability of replication success

priormean

Mean of the normal prior under the alternative. Defaults to 0

priorvar

Variance of the normal prior under the alternative. Defaults to 1

searchInt

Interval for numerical search over replication standard errors

Value

Returns an object of class "ssdRS". See ssd for details.

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

Examples

## specify design prior
to1 <- 0.2
so1 <- 0.05
dprior <- designPrior(to = to1, so = so1, tau = 0.03)
ssdBF01(level = 1/10, dprior = dprior, power = 0.8)