multiple.down: 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	multiple.down(sorted, q, m)

`sorted`	~~Describe `sorted` here~~
`q`	~~Describe `q` here~~
`m`	~~Describe `m` 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) 
{
    cat("Calling multiple step down \n")
    flush.console
    if (q > 0.5) 
        warning("q values over 0.5 are not recommended")
    criticals = sapply(1:m, function(i) q * i/(m - i * (1 - q) + 
        1))
    indicators = sorted < criticals
    if (!indicators[1]) 
        cutoff = list(cutoff = 0, cut.index = 0)
    else if (all(indicators)) 
        cutoff = list(cutoff = sorted[m], cut.index = m)
    else {
        cut.index = min((1:m)[!indicators]) - 1
        cutoff = list(cutoff = sorted[cut.index], cut.index = cut.index)
    }
    rejected = sorted <= cutoff$cutoff
    adjusted = multiple.down.adjust(sorted, m)
    multiple.pvals = data.frame(original.pvals = sorted, criticals = NA, 
        rejected = rejected, adjusted.pvals = adjusted)
    output = list(Cutoff = cutoff, Pvals = multiple.pvals)
    flush.console
    return(output)
  }