| plotCalibrationEffect {EmpiricalCalibration} | R Documentation | 
Plot the effect of the calibration
Description
plotCalibrationEffect creates a plot showing the effect of the calibration.
Usage
plotCalibrationEffect(
  logRrNegatives,
  seLogRrNegatives,
  logRrPositives = NULL,
  seLogRrPositives = NULL,
  null = NULL,
  alpha = 0.05,
  xLabel = "Relative risk",
  title,
  showCis = FALSE,
  showExpectedAbsoluteSystematicError = FALSE,
  fileName = NULL,
  xLimits = c(0.25, 10),
  yLimits = c(0, 1.5)
)
Arguments
| logRrNegatives | A numeric vector of effect estimates of the negative controls on the log scale. | 
| seLogRrNegatives | The standard error of the log of the effect estimates of the negative controls. | 
| logRrPositives | Optional: A numeric vector of effect estimates of the positive controls on the log scale. | 
| seLogRrPositives | Optional: The standard error of the log of the effect estimates of the positive controls. | 
| null | An object representing the fitted null distribution as created by the
 | 
| alpha | The alpha for the hypothesis test. | 
| xLabel | The label on the x-axis: the name of the effect estimate. | 
| title | Optional: the main title for the plot | 
| showCis | Show 95 percent credible intervals for the calibrated p = alpha boundary. | 
| showExpectedAbsoluteSystematicError | Show the expected absolute systematic error. If  | 
| fileName | Name of the file where the plot should be saved, for example 'plot.png'.
See the function  | 
| xLimits | Vector of length 2 for limits of the plot x axis - defaults to 0.25, 10 | 
| yLimits | Vector of length 2 for size limits of the y axis - defaults to 0, 1.5 | 
Details
Creates a plot with the effect estimate on the x-axis and the standard error on the y-axis. Negative controls are shown as blue dots, positive controls as yellow diamonds. The area below the dashed line indicated estimates with p < 0.05. The orange area indicates estimates with calibrated p < 0.05.
Value
A Ggplot object. Use the ggsave function to save to file.
Examples
data(sccs)
negatives <- sccs[sccs$groundTruth == 0, ]
positive <- sccs[sccs$groundTruth == 1, ]
plotCalibrationEffect(negatives$logRr, negatives$seLogRr, positive$logRr, positive$seLogRr)