soybean.mod.qc.affy <- 
function (unnormalised, normalised = NULL, tau = 0.015, logged = TRUE, 
    cdfn = cleancdfname(cdfName(unnormalised))) 
{
    if (is.null(normalised)) {
        getAllSpikeProbes("hgu133acdf")
        normalised <- call.exprs(unnormalised, "mas5")
    }
###
### All lines that have been removed from Crispin Miller's 
### original qc.affy() function are delimited with a pound sign
###  
    #if (!haveQCParams(cleancdfname(cdfName(unnormalised)))) {
    #    stop(paste("I'm sorry, I do not know about chip type:", 
    #        cleancdfname(cdfName(unnormalised))))
    # }
###
###
    x <- exprs(normalised)
###
###
### Crispin Miller's orginal qc.affy() function contains the following statement:
### det <- detection.p.val(unnormalised, tau = tau, alpha1 = getAlpha1(cdfn), 
###        alpha2 = getAlpha2(cdfn))  
### 
### At this point, hardcode values of your choice for alpha1 and alpha2.
### Note: mas5calls() uses a generic values of 0.04 and 0.06, respectively,
### for alpha1 and alpha2.
### 
    det <- detection.p.val(unnormalised, tau = tau, alpha1 = .04, 
        alpha2 = .06)
    dpv <- apply(det$call, 2, function(x) {
        x[x != "P"] <- 0
        x[x == "P"] <- 1
        x <- as.numeric(x)
        return(100 * sum(x)/length(x))
    })
    sfs <- normalised@description@preprocessing$sfs
    target <- normalised@description@preprocessing$tgt
    if (!logged) {
        x <- log2(x)
    }
    bgsts <- .bg.stats(unnormalised)$zonebg
    meanbg <- apply(bgsts, 1, mean)
    minbg <- apply(bgsts, 1, min)
    maxbg <- apply(bgsts, 1, max)
    stdvbg <- sqrt(apply(bgsts, 1, var))
###
### More code to be commented out...
###
    # qc.probenames <- getAllQCProbes(cdfn)
    # qc.probe.vals <- rbind(c(), (sapply(qc.probenames, function(y) {
    #    x[y, ]
    # })))
    # rownames(qc.probe.vals) <- colnames(x)
    # spike.probenames <- getAllSpikeProbes(cdfn)
    # spike.vals <- rbind(c(), (sapply(spike.probenames, function(y) {
    #    x[y, ]
    # })))
    # rownames(spike.vals) <- colnames(x)
    # bb <- getBioB(cdfn)
    # if (!is.na(bb)) {
    #    biobcalls <- det$call[bb, ]
    # }
    # else {
    #    biobcalls <- NULL
    # }
###
### The original return function from Crispin Miller's qc.affy() function is: 
### return(new("QCStats", scale.factors = sfs, target = target, 
###     percent.present = dpv, average.background = meanbg, minimum.background = minbg, 
###     maximum.background = maxbg, spikes = spike.vals, qc.probes = qc.probe.vals, 
###     bioBCalls = biobcalls))
###
### The return function has been modified as follows: 
### 
    return(new("QCStats", scale.factors = sfs, target = target, 
        percent.present = dpv, average.background = meanbg, minimum.background = minbg, 
        maximum.background = maxbg))
}