EstimatePi0: Proportion of the variables under the null hypothesis
In BGmix: Bayesian models for differential gene expression

Description Usage Arguments Details Value Author(s) References See Also Examples

Estimate of the proportion of the variables under the null hypothesis using tail posterior probabilities

1	EstimatePi0(tpp, pp0, plot = T)

`tpp`	observed tail posterior probability
`pp0`	a vector of tail posterior probability under H0
`plot`	if True, estimated pi0 at different locations and the median estimate is plotted

Use Storey (2002) approach to estimate pi0

estimate of pi0 = proportion of non-differentially expressed genes

Natalia Bochkina

Bochkina N., Richardson S. (2007) Tail posterior probability for inference in pairwise and multiclass gene expression data. Biometrics (in press).

TailPP, FDRplotTailPP,histTailPP

 data(ybar, ss)
 nreps <- c(8,8)

## Note this is a very short MCMC run!
## For good analysis need proper burn-in period.
 outdir <- BGmix(ybar, ss, nreps, jstar=-1, nburn=0, niter=100, nthin=1)

 params <- ccParams(outdir)  
 res <-  ccTrace(outdir)
  
 tpp.res <- TailPP(res, nreps, params, plots  = FALSE)
 pi0 <- EstimatePi0(tpp.res$tpp, tpp.res$pp0)