# R/Measure_colAUC.R In Najah-lshanableh/R-data-mining2: Machine Learning in R

```# colAUC calculates for a vector with true values the Area Under the ROC Curve (AUC) for a matrix of samples.
# Matrix rows contain samples while the columns contain features/variables.
# The function is used to calculate different multiclass AUC measures AU1P, AU1U, AUNP, AUNU,
# following the definition by Ferri et al.:
# https://www.math.ucdavis.edu/~saito/data/roc/ferri-class-perf-metrics.pdf

colAUC = function(samples, truth, maximum = TRUE) {
y = as.factor(truth)
X = as.matrix(samples)
if (nrow(X) == 1)
X = t(X)
nr = nrow(X)
nc = ncol(X)
ny = table(y)
ul = as.factor(rownames(ny))
nl = length(ny)
if (nl <= 1)
stop("colAUC: List of labels 'y' have to contain at least 2 class labels.")
if (!is.numeric(X))
stop("colAUC: 'X' must be numeric")
if (nr != length(y))
stop("colAUC: length(y) and nrow(X) must be the same")
per = t(utils::combn(1:nl, 2))
np = nrow(per)
auc = matrix(0.5, np, nc)
rownames(auc) = paste(ul[per[, 1]], " vs. ", ul[per[, 2]], sep = "")
colnames(auc) = colnames(X)
# Wilcoxon AUC
idxl = vector(mode = "list", length = nl)
for (i in 1:nl) idxl[[i]] = which(y == ul[i])
for (j in 1:nc) {
for (i in 1:np) {
c1 = per[i, 1]
c2 = per[i, 2]
n1 = as.numeric(ny[c1])
n2 = as.numeric(ny[c2])
if (n1 > 0 & n2 > 0) {
r = rank(c(X[idxl[[c1]], j], X[idxl[[c2]], j]))
auc[i, j] = (sum(r[1:n1]) - n1 * (n1 + 1) / 2) / (n1 * n2)
}
}
}
if (maximum == TRUE) {
auc = pmax(auc, 1 - auc)
}
return(auc)
}
```
Najah-lshanableh/R-data-mining2 documentation built on May 6, 2019, 10:11 a.m.