Rankings and Rank Correlations

Rankings and Rank Correlations

This package implements various strategies for computing ranks and rank correlations.

StatsBase.ordinalrankFunction.
ordinalrank(x; lt = isless, rev::Bool = false)

Return the ordinal ranking ("1234" ranking) of an array. The lt keyword allows providing a custom "less than" function; use rev=true to reverse the sorting order. All items in x are given distinct, successive ranks based on their position in sort(x; lt = lt, rev = rev). Missing values are assigned rank missing.

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StatsBase.competerankFunction.
competerank(x; lt = isless, rev::Bool = false)

Return the standard competition ranking ("1224" ranking) of an array. The lt keyword allows providing a custom "less than" function; use rev=true to reverse the sorting order. Items that compare equal are given the same rank, then a gap is left in the rankings the size of the number of tied items - 1. Missing values are assigned rank missing.

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StatsBase.denserankFunction.
denserank(x)

Return the dense ranking ("1223" ranking) of an array. The lt keyword allows providing a custom "less than" function; use rev=true to reverse the sorting order. Items that compare equal receive the same ranking, and the next subsequent rank is assigned with no gap. Missing values are assigned rank missing.

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StatsBase.tiedrankFunction.
tiedrank(x)

Return the tied ranking, also called fractional or "1 2.5 2.5 4" ranking, of an array. The lt keyword allows providing a custom "less than" function; use rev=true to reverse the sorting order. Items that compare equal receive the mean of the rankings they would have been assigned under ordinal ranking. Missing values are assigned rank missing.

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StatsBase.corspearmanFunction.
corspearman(x, y=x)

Compute Spearman's rank correlation coefficient. If x and y are vectors, the output is a float, otherwise it's a matrix corresponding to the pairwise correlations of the columns of x and y.

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StatsBase.corkendallFunction.
corkendall(x, y=x)

Compute Kendall's rank correlation coefficient, τ. x and y must both be either matrices or vectors.

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