ReplicationSuccess (version 1.3.3)

predictionInterval: Prediction interval for effect estimate of replication study

Description

Computes a prediction interval for the effect estimate of the replication study.

Usage

predictionInterval(
  thetao,
  seo,
  ser,
  tau = 0,
  conf.level = 0.95,
  designPrior = "predictive"
)

Value

A data frame with the following columns

lower

Lower limit of prediction interval,

mean

Mean of predictive distribution,

upper

Upper limit of prediction interval.

Arguments

thetao

Numeric vector of effect estimates from original studies.

seo

Numeric vector of standard errors of the original effect estimates.

ser

Numeric vector of standard errors of the replication effect estimates.

tau

Between-study heterogeneity standard error. Default is 0 (no heterogeneity). Is only taken into account when designPrior is "predictive" or "EB".

conf.level

The confidence level of the prediction intervals. Default is 0.95.

designPrior

Either "predictive" (default), "conditional", or "EB". If "EB", the contribution of the original study to the predictive distribution is shrunken towards zero based on the evidence in the original study (with empirical Bayes).

Author

Samuel Pawel

Details

This function computes a prediction interval and a mean estimate under a specified predictive distribution of the replication effect estimate. Setting designPrior = "conditional" is not recommended since this ignores the uncertainty of the original effect estimate. See Patil, Peng, and Leek (2016) and Pawel and Held (2020) for details.

predictionInterval is the vectorized version of .predictionInterval_. Vectorize is used to vectorize the function.

References

Patil, P., Peng, R. D., Leek, J. T. (2016). What should researchers expect when they replicate studies? A statistical view of replicability in psychological science. Perspectives on Psychological Science, 11, 539-544. tools:::Rd_expr_doi("10.1177/1745691616646366")

Pawel, S., Held, L. (2020). Probabilistic forecasting of replication studies. PLOS ONE. 15, e0231416. tools:::Rd_expr_doi("10.1371/journal.pone.0231416")

Examples

predictionInterval(thetao = c(1.5, 2, 5), seo = 1, ser = 0.5, designPrior = "EB")

# compute prediction intervals for replication projects
data("RProjects", package = "ReplicationSuccess")
parOld <- par(mfrow = c(2, 2))
for (p in unique(RProjects$project)) {
  data_project <- subset(RProjects, project == p)
  PI <- predictionInterval(thetao = data_project$fiso, seo = data_project$se_fiso,
                           ser = data_project$se_fisr)
  PI <- tanh(PI) # transforming back to correlation scale
  within <- (data_project$rr < PI$upper) & (data_project$rr > PI$lower)
  coverage <- mean(within)
  color <- ifelse(within == TRUE, "#333333B3", "#8B0000B3")
  study <- seq(1, nrow(data_project))
  plot(data_project$rr, study, col = color, pch = 20,
       xlim = c(-0.5, 1), xlab = expression(italic(r)[r]),
       main = paste0(p, ": ", round(coverage*100, 1), "% coverage"))
  arrows(PI$lower, study, PI$upper, study, length = 0.02, angle = 90,
         code = 3, col = color)
  abline(v = 0, lty = 3)
}
par(parOld)