amcc: Adaptive Matthews Correlation Coefficient

In PharmacoGx: Analysis of Large-Scale Pharmacogenomic Data

Description

This function calculates an Adaptive Matthews Correlation Coefficient (AMCC) for two vectors of values of the same length. It assumes the entries in the two vectors are paired. The Adaptive Matthews Correlation Coefficient for two vectors of values is defined as the Maximum Matthews Coefficient over all possible binary splits of the ranks of the two vectors. In this way, it calculates the best possible agreement of a binary classifier on the two vectors of data. If the AMCC is low, then it is impossible to find any binary classification of the two vectors with a high degree of concordance.

Usage

amcc(x, y, step.prct = 0, min.cat = 3, nperm = 1000, nthread = 1)

Arguments

x	Two paired vectors of values. Could be replicates of observations for the same experiments for example.
y	Two paired vectors of values. Could be replicates of observations for the same experiments for example.
step.prct	Instead of testing all possible splits of the data, it is possible to test steps of a percentage size of the total number of ranks in x/y. If this variable is 0, function defaults to testing all possible splits.
min.cat	The minimum number of members per category. Classifications with less members fitting into both categories will not be considered.
nperm	The number of perumatation to use for estimating significance. If 0, then no p-value is calculated.
nthread	Number of threads to parallize over. Both the AMCC calculation and the permutation testing is done in parallel.

Value

Returns a list with two elements. $amcc contains the highest 'mcc' value over all the splits, the p value, as well as the rank at which the split was done.

Examples

x <- c(1,2,3,4,5,6,7)
y <- c(1,3,5,4,2,7,6)
amcc(x,y, min.cat=2)

Example output

$amcc
        mcc           p          n1          n2           n 
1.000000000 0.000999001 2.000000000 4.000000000 6.000000000 

$mcc
       estimate     p.value
[1,] -0.1666667          NA
[2,]  1.0000000 0.000999001
[3,]  0.4166667          NA
[4,]  0.4166667          NA
[5,]  0.3000000          NA
[6,]  1.0000000          NA

PharmacoGx documentation built on Feb. 28, 2021, 2 a.m.