Nothing
rank.partitionedRAD <-
function(radMat, minTrees = 10, min.overall.diff.lnL = 5,
threshold.lnL = 2, discardDoubleCounts = TRUE) {
radMat.preLnLDiff <- radMat.preMinTrees <- NA
if(minTrees > 1) {
nTrees <- apply(radMat, 1, function(x) length(unique(x)))
radMat.preMinTrees <- radMat
radMat <- radMat[nTrees >= minTrees, ]
}
if(min.overall.diff.lnL > 0) {
lnL.diff <- apply(radMat, 1, function(x) abs(diff(range(x))))
radMat.preLnLDiff <- radMat
radMat <- radMat[(lnL.diff >= min.overall.diff.lnL), ]
}
bestMat <- t(apply(radMat, 1, function(x) abs(x - max(x)) <= threshold.lnL))
worstMat <- t(apply(radMat, 1, function(x) abs(x - min(x)) <= threshold.lnL))
doubleCountMat <- bestMat & worstMat
if(discardDoubleCounts) {
doubleCounts <- apply(doubleCountMat, 1, sum) > 0
bestMat <- bestMat[!doubleCounts, ]
worstMat <- worstMat[!doubleCounts, ]
radMat.preDoubleCounts <- radMat
radMat <- radMat[!doubleCounts, ]
}
out <- list(bestMat = bestMat, worstMat = worstMat, doubleCountMat = doubleCountMat,
params = c(minTrees, min.overall.diff.lnL, threshold.lnL, discardDoubleCounts))
class(out) <- 'rankedPartitionedRAD'
out
}
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