`plotCiCoverage`

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

plotCiCoverage(
logRr,
seLogRr,
trueLogRr,
strata = as.factor(trueLogRr),
crossValidationGroup = 1:length(logRr),
legacy = FALSE,
evaluation,
legendPosition = "top",
title,
fileName = NULL
)

## Arguments

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 `strata = NULL` for no
stratification. |

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. |

legacy |
If true, a legacy error model will be fitted, meaning standard
deviation is linear on the log scale. If false, standard deviation
is assumed to be simply linear. |

evaluation |
A data frame as generated by the `evaluateCiCalibration`
function. If provided, the logRr, seLogRr, trueLogRr, strata, and legacy
arguments will be ignored. |

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 `ggsave` in the ggplot2 package for
supported file formats. |

## Value

A Ggplot object. Use the `ggsave`

function to save to file.

## Details

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.

## Examples