calcFDR {BGmix} | R Documentation |
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.
calcFDR(res, pcut = seq(0.01,0.5,0.01), true.z = NULL, q.print = F)
res |
list object output from ccParams (this includes the posterior classification probabilities) |
pcut |
scalar or vector of thresholds for which to estimate FDR etc. |
true.z |
vector of true classifications (if known, eg. for simulated data) |
q.print |
Print FDR etc. when pcut is a vector? |
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.
fdr.est, fnr.est |
scalars or vectors of estimated FDR, FNR |
fp.est, fn.est |
scalars or vectors of estimated no. false positives, no. false negatives |
fdr.true, fnr.true |
scalars or vectors of true FDR, FNR |
fp.true, fn.true |
scalars or vectors of true no. false positives, no. false negatives |
npos, nneg |
scalars or vectors of no. declared positives, no. declared negatives |
prob.class |
posterior classification probabilites (from the 'res' object input to this function) |
true.z |
argument to function is output |
pcut |
argument to function is output |
Alex Lewin
## 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)