ribbonROE {bayesROE}R Documentation

Bayesian Regions of Evidence Ribbon Plot


Compute and visualize the Bayesian Regions of Evidence (Ribbon), that is, the set of normal priors for an effect size which - when combined with the observed data - lead to a specified posterior probability for the effect size being more extreme than a specified minimally relevant effect size.


  delta = 0,
  alpha = 0.025,
  type = "threshold",
  larger = TRUE,
  meanLim = c(pmin(2 * ee, 0), pmax(0, 2 * ee)),
  sdLim = c(0, 3 * se),
  nGrid = 500,
  relative = TRUE,
  cols = NULL,
  cols_alpha = 1,
  addRef = TRUE,
  addEst = FALSE



Effect estimate.


Standard error of effect estimate.


Minimally relevant effect size. Defaults to zero. Can also be a vector of numerical values to representing different regions.


Posterior probability that the effect size is less extreme than delta. Defaults to 0.025. Can also be a vector of numerical values representing different regions.


Character indicating if regions of evidence should be constructed for a non-inferiority claim using the first element of delta and all elements of alpha ("threshold") or for a non-inferiority claim using the all elements of delta and the first element of alpha ("probability"). Defaults to "threshold".


Logical indicating if effect size should be larger (TRUE) or smaller (FALSE) than delta. Defaults to TRUE.


Limits of prior mean axis. Defaults to interval between zero and two times the effect estimate.


Limits of prior standard deviation axis. Defaults to interval between zero and three times the standard error.


Number of grid points (on the standard error axis). Defaults to 500.


Logical indicating whether a second x-axis and y-axis with relative prior mean and relative prior variance should be displayed. Defaults to TRUE.


Character containing the HEX color code of the upper and lower region of evidence, respectively. Defaults to NULL, which triggers automated color picking by calling ggplot2:scale_fill_viridis_d()


Numeric value indicating the relative opacity of any region of evidence (alpha channel). Defaults to 1 (no transparency).


Logical indicating if a reference cross representing the minimum sceptical prior is added to the plot. If delta or alpha are vectors, only their first element(s) will be processed. Defaults to TRUE.


Logical indicating if a point symbol representing the mean and standard error of the effect estimate (ee, se) is added to the plot. Defaults to FALSE.


A bayesROE object (a list containing the ggplot object, the data for the plot, and the tipping point function)


Pawel, S., Matthews, R. and Held, L. (2021). Comment on "Bayesian additional evidence for decision making under small sample uncertainty". Manuscript submitted for publication. Code available at https://osf.io/ymx92/


## data with p < 0.025 for H0: delta < 0, but p > 0.025 for H0: delta < 0.3
d <- 0.4
d_se <- 0.1
delta <- c(0, 0.3)
ribbonROE(ee = d, se = d_se, delta = delta, meanLim = c(-1, 1))

## reproducing Figure 1 from Hoefler & Miller (2023)
ee <- 3.07
se <- 1.19
ribbonROE(ee = ee, se = se, delta = c(0,3), alpha = 0.025, 
  cols = c("#F5FF82", "#27CC1E"))$plot + 
  ggplot2::annotate(geom = "point", y = ee, x = se, shape = 4) +
  ggplot2::coord_flip(ylim = c(-5, 15))

[Package bayesROE version 0.1 Index]