Fit the null distribution using MCMC

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

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

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.

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.

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) { # \dontrun{
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)
} # }