EmpiricalCalibration (version 3.1.4)

fitMcmcNull: Fit the null distribution using MCMC

Description

fitNull fits the null distribution to a set of negative controls using Markov Chain Monte Carlo (MCMC).

Usage

fitMcmcNull(logRr, seLogRr, iter = 1e+05)

Value

An object of type mcmcNull containing the mean and standard deviation (both on the log scale) of the null distribution, as well as the MCMC trace.

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)

iter

Number of iterations of the MCMC.

Details

This is an experimental function for computing the 95 percent credible interval of a calibrated p-value using Markov-Chain Monte Carlo (MCMC).

Examples

if (FALSE) {
data(sccs)
negatives <- sccs[sccs$groundTruth == 0, ]
null <- fitMcmcNull(negatives$logRr, negatives$seLogRr)
null
plotMcmcTrace(null)
positive <- sccs[sccs$groundTruth == 1, ]
calibrateP(null, positive$logRr, positive$seLogRr)
}