powerReplicationSuccess: Computes the power for replication success

In ReplicationSuccess: Design and Analysis of Replication Studies

Description

The power for replication success is computed based on the result of the original study, the corresponding variance ratio and the design prior.

Usage

powerReplicationSuccess(zo, 
                        c = 1, 
                        level = 0.025,
                        designPrior = "conditional", 
                        alternative = "one.sided",
                        type = "golden")

Arguments

zo	A vector of z-values from original studies.
c	A vector of variance ratios of the original and replication effect estimates. This is usually the ratio of the sample size of the replication study to the sample size of the original study.
level	Replication success level. The default is 0.025.
designPrior	Either "conditional" or "predictive". Defaults to "conditional".
alternative	either "one.sided" or "two.sided". Defaults to "one.sided". Specifies if the replication success level is one-sided or two-sided. If the replication success level level is one-sided, then power calculations are based on a one-sided assessment of replication success in the direction of the original effect estimate.
type	Type of recalibration. Can be either "nominal" (no recalibration), "liberal", "controlled" or "golden". The default is "golden" which ensures that for an original study just significant at the specified level, replication success is only possible if the replication effect estimate larger than the original one. See ?levelSceptical for details about recalibration types.

Value

The power for replication success.

Author(s)

Leonhard Held

References

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. https://doi.org/10.1111/rssa.12493

Held, L., Micheloud, C. & Pawel, S. (2020). The assessment of replication success based on relative effect size. https://arxiv.org/abs/2009.07782

See Also

sampleSizeReplicationSuccess, pSceptical, levelSceptical

Examples

powerReplicationSuccess(zo = p2z(0.005), c = 2)
powerReplicationSuccess(zo = p2z(0.005), c = 2, designPrior = "predictive")
thresNominal <- levelSceptical(0.025, type = "nominal")
powerReplicationSuccess(zo = p2z(0.005), c = 2, level = thresNominal)
powerReplicationSuccess(zo = p2z(0.005), c = 2, level = thresNominal, 
                        designPrior = "predictive")

powerReplicationSuccess(zo = p2z(0.005), c = 1/2)
powerReplicationSuccess(zo = p2z(0.005), c = 1/2, designPrior = "predictive")
powerReplicationSuccess(zo = p2z(0.005), c = 1/2, level = thresNominal)
powerReplicationSuccess(zo = p2z(0.005), c = 1/2, level = thresNominal, 
                        designPrior = "predictive")

ReplicationSuccess documentation built on Dec. 2, 2020, 3 p.m.