plotCalibration creates a plot showing the calibration of our calibration procedure
plotCalibration( logRr, seLogRr, useMcmc = FALSE, legendPosition = "right", title, fileName = NULL )
A numeric vector of effect estimates on the log scale
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)
Use MCMC to estimate the calibrated P-value?
Where should the legend be positioned? ("none", "left", "right", "bottom", "top")
Optional: the main title for the plot
Name of the file where the plot should be saved, for example 'plot.png'. See
ggsave in the ggplot2 package for supported file
A Ggplot object. Use the
ggsave function to save to file.
Creates a calibration plot showing the number of effects with p < alpha for every level of alpha. The empirical calibration is performed using a leave-one-out design: The p-value of an effect is computed by fitting a null using all other negative controls. Ideally, the calibration line should approximate the diagonal. The plot shows both theoretical (traditional) and empirically calibrated p-values.
data(sccs) negatives <- sccs[sccs$groundTruth == 0, ] plotCalibration(negatives$logRr, negatives$seLogRr)