Description Usage Arguments Details Value Examples

`plotCiCoverage`

creates a plot showing the coverage before and after confidence interval
calibration at various widths of the confidence interval.

1 2 3 | ```
plotCiCoverage(logRr, seLogRr, trueLogRr, strata = as.factor(trueLogRr),
crossValidationGroup = 1:length(logRr), evaluation,
legendPosition = "top", title, fileName = NULL)
``` |

`logRr` |
A numeric vector of effect estimates on the log scale. |

`seLogRr` |
The standard error of the log of the effect estimates. Hint: often the standard error = (log(<lower bound 95 percent confidence interval>) - log(<effect estimate>))/qnorm(0.025). |

`trueLogRr` |
The true log relative risk. |

`strata` |
Variable used to stratify the plot. Set |

`crossValidationGroup` |
What should be the unit for the cross-validation? By default the unit is a single control, but a different grouping can be provided, for example linking a negative control to synthetic positive controls derived from that negative control. |

`evaluation` |
A data frame as generated by the |

`legendPosition` |
Where should the legend be positioned? ("none", "left", "right", "bottom", "top"). |

`title` |
Optional: the main title for the plot |

`fileName` |
Name of the file where the plot should be saved, for example
'plot.png'. See the function |

Creates a plot showing the fraction of effects above, within, and below the confidence interval. The empirical calibration is performed using a leave-one-out design: The confidence interval of an effect is computed by fitting a null using all other controls. The plot shows the coverage for both theoretical (traditional) and empirically calibrated confidence intervals.

A Ggplot object. Use the `ggsave`

function to save to file.

1 2 3 4 5 | ```
## Not run:
data <- simulateControls(n = 50 * 3, mean = 0.25, sd = 0.25, trueLogRr = log(c(1, 2, 4)))
plotCiCoverage(data$logRr, data$seLogRr, data$trueLogRr)
## End(Not run)
``` |

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