plotCiCoverage creates a plot showing the coverage before and after confidence interval
calibration at various widths of the confidence interval.
A numeric vector of effect estimates on the log scale.
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).
The true log relative risk.
Variable used to stratify the plot. Set
strata = NULL for no
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.
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.
A data frame as generated by the
function. If provided, the logRr, seLogRr, trueLogRr, strata, and legacy
arguments will be ignored.
Where should the legend be positioned? ("none", "left", "right", "bottom", "top").
Optional: the main title for the plot
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.
A Ggplot object. Use the
ggsave function to save to file.
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.