plotCalibrationEffect creates a plot showing the effect of the calibration.
A numeric vector of effect estimates of the negative controls on the log scale.
The standard error of the log of the effect estimates of the negative controls.
Optional: A numeric vector of effect estimates of the positive controls on the log scale.
Optional: The standard error of the log of the effect estimates of the positive controls.
An object representing the fitted null distribution as created by the
fitMcmcNull functions. If not provided, a null
will be fitted before plotting.
The alpha for the hypothesis test.
The label on the x-axis: the name of the effect estimate.
Optional: the main title for the plot
Show 95 percent credible intervals for the calibrated p = alpha boundary.
Show the expected absolute systematic error. If
null is of
mcmcNull the 95 percent credible interval will also be shown.
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
Vector of length 2 for limits of the plot x axis - defaults to 0.25, 10
Vector of length 2 for size limits of the y axis - defaults to 0, 1.5
A Ggplot object. Use the
ggsave function to save to file.
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.
data(sccs) negatives <- sccs[sccs$groundTruth == 0, ] positive <- sccs[sccs$groundTruth == 1, ] plotCalibrationEffect(negatives$logRr, negatives$seLogRr, positive$logRr, positive$seLogRr) #> Warning: Removed 1 rows containing missing values (geom_vline).