fitMcmcNull: Fit the null distribution using MCMC

In EmpiricalCalibration: Routines for Performing Empirical Calibration of Observational Study Estimates

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)

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).

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.

Examples

## Not run: 
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)

## End(Not run)

EmpiricalCalibration documentation built on Aug. 9, 2022, 5:07 p.m.