\name{calcFDR} \alias{calcFDR} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Estimate the FDR (false discovery rate) and related quantities for BGmix output.} \description{ Given a threshold on the posterior probabilities, genes are declared as null or differentially expressed. For any given threshold, the FDR (false discovery rate) and FNR (false non-discovery rate) can be estimated using the posterior probabilities. Estimated numbers of false positives and false negatives are also output. } \usage{ calcFDR(res, pcut = seq(0.01,0.5,0.01), true.z = NULL, q.print = F) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{res}{ list object output from ccParams (this includes the posterior classification probabilities) } \item{pcut}{ scalar or vector of thresholds for which to estimate FDR etc. } \item{true.z}{vector of true classifications (if known, eg. for simulated data) } \item{q.print}{ Print FDR etc. when pcut is a vector?} } \details{ If the true classification is known, it can be given as true.z, and the true FDR etc. for the threshold probability can be calculated. } \value{ \item{fdr.est, fnr.est}{scalars or vectors of estimated FDR, FNR} \item{fp.est, fn.est}{scalars or vectors of estimated no. false positives, no. false negatives} \item{fdr.true, fnr.true}{scalars or vectors of true FDR, FNR} \item{fp.true, fn.true}{scalars or vectors of true no. false positives, no. false negatives} \item{npos, nneg}{scalars or vectors of no. declared positives, no. declared negatives} \item{prob.class}{posterior classification probabilites (from the 'res' object input to this function)} \item{true.z}{argument to function is output} \item{pcut}{argument to function is output} } \author{ Alex Lewin} \keyword{ htest } \examples{ ## Note this is a very short MCMC run! ## For good analysis need proper burn-in period. data(ybar,ss) outdir <- BGmix(ybar, ss, c(8,8), nburn=0, niter=100, nthin=1) params <- ccParams(outdir) fdr <- calcFDR(params) }