# cred.set: Calculate a credible set for the posterior distribution on... In Bioconductor-mirror/dks: The double Kolmogorov-Smirnov package for evaluating multiple testing procedures.

## Description

This function accepts a distribution calculated with pprob.dist and calculates a credible set of the specified level for the hyperparameters. If the credible set includes the value (1,1) the sample is likely to be uniform.

## Usage

 `1` ``` cred.set(dist,delta=NULL,level=0.95) ```

## Arguments

 `dist` The posterior distribution for the hyperparameters computed with pprob.dist. `delta` The grid size, must match the grid size from pprob.dist. `level` The level of the credible set.

## Details

The cred.set function calculates a credible set of the specified level based on the distribution calculated with pprob.dist. The grid size, delta, should match the grid size from the call to pprob.dist. The result is a matrix of the same size as dist which indicates whether each point is in the credible set.

## Value

 `cred` The credible set for the hyper-parameters of the beta distribution. `level` The user specified level of the set. `elevel` The empirical level of the set, the smaller delta is, the closer elevel will be to level.

## Author(s)

Jeffrey T. Leek [email protected]

## References

J.T. Leek and J.D. Storey, "The Joint Null Distribution of Multiple Hypothesis Tests."

`dks`, `dks.pvalue`, `pprob.dist`,`cred.set`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ``` ## Load data data(dksdata) ## Calculate the posterior distribution dist1 <- pprob.dist(P[,1]) delta = 0.1 ## Calculate a 95% credible set cred1 <- cred.set(dist1,delta=0.1) ## Plot the posterior and the credible set alpha <- seq(0.1,10,by=delta) beta <- seq(0.1,10,by=delta) par(mfrow=c(1,2)) image(log10(alpha),log10(beta),dist1,xaxt="n",yaxt="n",xlab="Alpha",ylab="Beta") axis(1,at=c(-2,-1,0,1,2),labels=c("10^-2","10^-1","10^0","10^1","10^2")) axis(2,at=c(-2,-1,0,1,2),labels=c("10^-2","10^-1","10^0","10^1","10^2")) points(0,0,col="blue",cex=1,pch=19) image(log10(alpha),log10(beta),cred1\$cred,xaxt="n",yaxt="n",xlab="Alpha",ylab="Beta") axis(1,at=c(-2,-1,0,1,2),labels=c("10^-2","10^-1","10^0","10^1","10^2")) axis(2,at=c(-2,-1,0,1,2),labels=c("10^-2","10^-1","10^0","10^1","10^2")) points(0,0,col="blue",cex=1,pch=19) ```