plotCiCoverage: Create a confidence interval coverage plot

In EmpiricalCalibration: Routines for Performing Empirical Calibration of Observational Study Estimates

Create a confidence interval coverage plot

Description

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

Usage

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.

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.

Value

A Ggplot object. Use the ggsave function to save to file.

Examples

## 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)

EmpiricalCalibration documentation built on Aug. 9, 2022, 5:07 p.m.