powerReplicationSuccess {ReplicationSuccess} | R Documentation |
Computes the power for replication success with the sceptical p-value
Description
Computes the power for replication success with the sceptical p-value 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 = c("conditional", "predictive", "EB"),
alternative = c("one.sided", "two.sided"),
type = c("golden", "nominal", "controlled"),
shrinkage = 0,
h = 0,
strict = FALSE
)
Arguments
zo |
Numeric vector of z-values from original studies. |
c |
Numeric 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 |
Threshold for the calibrated sceptical p-value. Default is 0.025. |
designPrior |
Either "conditional" (default), "predictive", or "EB". If "EB", the power is computed under a predictive distribution, where the contribution of the original study is shrunken towards zero based on the evidence in the original study (with an empirical Bayes shrinkage estimator). |
alternative |
Specifies if |
type |
Type of recalibration. Can be either "golden" (default), "nominal" (no recalibration),
or "controlled". "golden" ensures that for an original study just significant at
the specified |
shrinkage |
Numeric vector with values in [0,1). Defaults to 0.
Specifies the shrinkage of the original effect estimate towards zero,
e.g., the effect is shrunken by a factor of 25% for
|
h |
Numeric vector of relative heterogeneity variances i.e., the ratios
of the heterogeneity variance to the variance of the original effect
estimate. Default is 0 (no heterogeneity). Is only taken into account
when |
strict |
Logical vector indicating whether the probability for
replication success in the opposite direction of the original effect
estimate should also be taken into account. Default is |
Details
powerReplicationSuccess
is the vectorized version of
the internal function .powerReplicationSuccess_
.
Vectorize
is used to vectorize the function.
Value
The power for replication success with the sceptical p-value
Author(s)
Leonhard Held, Charlotte Micheloud, Samuel Pawel
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, 431-448. doi:10.1111/rssa.12493
Held, L., Micheloud, C., Pawel, S. (2022). The assessment of replication success based on relative effect size. The Annals of Applied Statistics. 16:706-720. doi:10.1214/21-AOAS1502
Micheloud, C., Balabdaoui, F., Held, L. (2023). Assessing replicability with the sceptical p-value: Type-I error control and sample size planning. Statistica Neerlandica. doi:10.1111/stan.12312
See Also
sampleSizeReplicationSuccess
, pSceptical
,
levelSceptical
Examples
## larger sample size in replication (c > 1)
powerReplicationSuccess(zo = p2z(0.005), c = 2, level = 0.025, designPrior = "conditional")
powerReplicationSuccess(zo = p2z(0.005), c = 2, level = 0.025, designPrior = "predictive")
## smaller sample size in replication (c < 1)
powerReplicationSuccess(zo = p2z(0.005), c = 1/2, level = 0.025, designPrior = "conditional")
powerReplicationSuccess(zo = p2z(0.005), c = 1/2, level = 0.025, designPrior = "predictive")
powerReplicationSuccess(zo = p2z(0.00005), c = 2, level = 0.05,
alternative = "two.sided", strict = TRUE, shrinkage = 0.9)
powerReplicationSuccess(zo = p2z(0.00005), c = 2, level = 0.05,
alternative = "two.sided", strict = FALSE, shrinkage = 0.9)