PowerUpR (version 1.1.0)

replication: Unambiguous Test of Replication for Ensemble of Studies

Description

Use power.rep() for the statistical power, mdh.rep() for the minimum detectable heterogeneity, and mrns.rep() for the minimum required number of studies. Functions implement methods designed to conduct unambiguous test of replication for ensemble of studies (Hedges & Schauer, 2019). mdh argument is the effect heterogeneity above and beyond sampling variability. An mdh = 0 specification means effects are same across subgroups or moderator levels in the population. Effects will vary from each other solely due to sampling error. In this case, with large samples, heterogeneity detected after ensample of studies are conducted will be equal to unity.

Usage

power.rep(k = 2L, mdh = 1/4, mdh.null = 0, alpha = .05)
mdh.rep(k = 2L, mdh.max = 15, alpha = .05, power = 0.80,
        mdh.null = 0, step = .001, plot = FALSE)
mrns.rep(power = .80, mdh = 1/4, mdh.null = 0, alpha = .05,
         tol = .001)

Arguments

k

number of replications.

power

statistical power \((1-\beta)\).

alpha

probability of type I error.

mdh

minimum detectable heterogeneity (MDH).

mdh.null

MDH for null hypothesis.

mdh.max

maximum of possible MDH values for grid search.

step

step size to generate possible MDH values.

plot

logical; if TRUE plots MDH - power curve.

tol

tolerance to end iterative process for finding k

Value

fun

function name.

parms

list of parameters used in the calculation.

df

degrees of freedom.

power

statistical power \((1-\beta)\).

mdh

minimum detectable heterogeneity (MDH).

k

minimum required number of studies.

df

degrees of freedom.

References

Hedges, L. V., & Schauer, J. (2019). Statistical analyses for studying replication: Meta-analytic perspectives. Psychological Methods, 24(5), 557-570. http://dx.doi.org/10.1037/met0000189

Examples

# NOT RUN {
# cross-checks
power.rep(k = 20L, mdh = 0.50)
mdh.rep(k = 20L, power = .39)
mrns.rep(power = .39, mdh = .50)
# }