Create a confidence interval calibration plot — plotCiCalibration • EmpiricalCalibration

plotCalibration creates a plot showing the calibration of our confidence interval calibration procedure

plotCiCalibration(
  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 calibration plot showing the fraction of effects within 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. Ideally, the calibration line should approximate the diagonal. The plot shows the coverage for both theoretical (traditional) and empirically calibrated confidence intervals.

Examples

if (FALSE) {
data <- simulateControls(n = 50 * 3, mean = 0.25, sd = 0.25, trueLogRr = log(c(1, 2, 4)))
plotCiCalibration(data$logRr, data$seLogRr, data$trueLogRr)
}