ribbonROE {bayesROE} | R Documentation |

## Bayesian Regions of Evidence Ribbon Plot

### Description

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.

### Usage

```
ribbonROE(
ee,
se,
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,
sceptPrior = 0,
addEst = FALSE
)
```

### Arguments

`ee` |
Effect estimate. |

`se` |
Standard error of effect estimate. |

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

`alpha` |
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. |

`type` |
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". |

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

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

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

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

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

`cols` |
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() |

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

`addRef` |
Logical indicating if a reference cross representing the minimum sceptical prior is added to the plot. Defaults to TRUE. |

`sceptPrior` |
Numeric value indicating the effect location of the minimum sceptical prior. Defaults to 0. |

`addEst` |
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. |

### Value

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

### References

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/

### Examples

```
## 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))
```

*bayesROE*version 0.2 Index]