plotErrorModel: Plot the systematic error model

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

Plot the systematic error model

Description

plotErrorModel creates a plot showing the systematic error model.

Usage

plotErrorModel(
  logRr,
  seLogRr,
  trueLogRr,
  title,
  legacy = FALSE,
  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.
title	Optional: the main title for the plot
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.
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 with the true effect size on the x-axis, and the mean plus and minus the standard deviation shown on the y-axis. Also shown are simple error models fitted at each true relative risk in the input.

Value

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

Examples

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

EmpiricalCalibration documentation built on April 3, 2025, 10:16 p.m.