Nothing
Armitage.TradPerm <-
function (genotypeLine, affectionLine, fromCol, naString, sep, repeatNum = 1000)
{
# if ( repeatNum<0 || repeatNum != round(repeatNum) ) {
# stop("'repeatNum' must be a positive integer.")
# }
# calculate the genotype frequency for case and control samples
obsStat = genotypeStat(genotypeLine = genotypeLine, affectionLine = affectionLine, fromCol = fromCol, naString = naString, sep = sep)
obsCount = obsStat$genotypeCount
trendValue = matrix(0, nrow = 1, ncol = repeatNum )
trendP = matrix(0, nrow = 1, ncol = repeatNum )
# calculate Armitage test for trend in true data
temp = Armitage(obsCount[[1]],obsCount[[2]],obsCount[[3]],obsCount[[5]],obsCount[[6]],obsCount[[7]])
obsTrendValue = temp$statistic
obsTrendP = temp$pValue
N = sum(obsCount)-obsCount[[4]]-obsCount[[8]]
R = obsCount[[1]]+obsCount[[2]]+obsCount[[3]]
S = N-R
n1=obsCount[[2]]+obsCount[[6]]
n2=obsCount[[3]]+obsCount[[7]]
temp2 = n1+2*n2
temp3 = n1+4*n2
low=S*R*(N*temp3-temp2*temp2)
colNum=ncol(genotypeLine)
for (i in 1:repeatNum ) {
randIndex=sample(colNum-fromCol+1)
randData=cbind(genotypeLine[,1:(fromCol-1),drop=FALSE],genotypeLine[,randIndex+fromCol-1,drop=FALSE])
expStat = genotypeStat(genotypeLine = randData, affectionLine = affectionLine, fromCol = fromCol, naString = naString, sep = sep)
expCount = expStat$genotypeCount
temp1=expCount[[2]]+2*expCount[[3]]
upp=N*(N*temp1-R*temp2)*(N*temp1-R*temp2)
trendValue[1,i]=upp/low
trendP[1,i]=pchisq(trendValue[1,i],df=1,lower.tail=FALSE)
}
maxExp = trendValue[trendValue > obsTrendValue]
pValue = length(maxExp)/repeatNum
list(pValue = pValue, obsStatistic = obsTrendValue, obsP = obsTrendP,
permStatistic = trendValue, permP = trendP)
}
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