evaluateCiCalibration performs a leave-one-out cross-validation to evaluate the calibration
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 specifying the coverage per strata (usually true effect size) for a wide range of widths of the confidence interval. The result also includes the fraction of estimates that was below and above 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.