bh: function to do ...
In mcp.project: Applies different False Discovery Rate controlling procedures

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

~~ A concise (1-5 lines) description of what the function does. ~~

1	bh(sorted, q, m, adjust = FALSE, m0 = m, pi0, constant = 1)

`sorted`	~~Describe `sorted` here~~
`q`	~~Describe `q` here~~
`m`	~~Describe `m` here~~
`adjust`	~~Describe `adjust` here~~
`m0`	~~Describe `m0` here~~
`pi0`	~~Describe `pi0` here~~
`constant`	~~Describe `constant` here~~

~~ If necessary, more details than the description above ~~

~Describe the value returned If it is a LIST, use

`comp1`	Description of 'comp1'
`comp2`	Description of 'comp2'

...

~~further notes~~

~Make other sections like Warning with Warning .... ~

~~who you are~~

~put references to the literature/web site here ~

~~objects to See Also as help, ~~~

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--    or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (sorted, q, m, adjust = F, m0 = m, pi0, constant = 1) 
{
    cat("Calling bh \n")
    flush.console
    if (missing(m0) & !missing(pi0)) 
        m0 = pi0 * m
    else {
        criticals = (1:m) * q/(m0 * constant)
        cutoff = reject(sorted, criticals)
        rejected = sorted <= cutoff$cutoff
        adjusted = rep(NA, m)
        if (adjust) 
            adjusted = bh.adjust(sorted, m = m, m0 = m0, constant = constant)
        multiple.pvals = data.frame(original.pvals = sorted, 
            criticals = criticals, rejected = rejected, adjusted.pvals = adjusted)
        output = list(Cutoff = cutoff, Pvals = multiple.pvals)
        return(output)
    }
  }