predictionInterval: Prediction interval for effect estimate of replication study

In ReplicationSuccess: Design and Analysis of Replication Studies

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"
)

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).

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.

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.

Author(s)

Samuel Pawel

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/1745691616646366")}

Pawel, S., Held, L. (2020). Probabilistic forecasting of replication studies. PLOS ONE. 15, e0231416. \Sexpr[results=rd]{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)

ReplicationSuccess documentation built on Oct. 30, 2024, 9:14 a.m.