mcc {mltools}

Matthews correlation coefficient

Description

Calculate Matthews correlation coefficient

Usage

mcc(preds = NULL, actuals = NULL, TP = NULL, FP = NULL, TN = NULL,
  FN = NULL, confusionM = NULL)

Arguments

preds

A vector of prediction values, or a data.frame or matrix of TRUE/FALSE or 1/0 whose columns correspond to the possible classes

actuals

A vector of actuals values, or a data.frame or matrix of TRUE/FALSE or 1/0 whose columns correspond to the possible classes

TP

Count of true positives (correctly predicted 1/TRUE)

FP

Count of false positives (predicted 1/TRUE, but actually 0/FALSE)

TN

Count of true negatives (correctly predicted 0/FALSE)

FN

Count of false negatives (predicted 0/FALSE, but actually 1/TRUE)

confusionM

Confusion matrix whose (i,j) element represents the number of samples with predicted class i and true class j

Details

Calculate Matthews correlation coefficient. Provide either

• preds and actuals or

• TP, FP, TN, and FN

• confusionM

References

https://en.wikipedia.org/wiki/Matthews_correlation_coefficient

Examples

preds <- c(1,1,1,0,1,1,0,0)
actuals <- c(1,1,1,1,0,0,0,0)
mcc(preds, actuals)
mcc(actuals, actuals)
mcc(TP=3, FP=2, TN=2, FN=1)

# Multiclass
preds <- data.frame(
  setosa = rnorm(n = 150), 
  versicolor = rnorm(n = 150), 
  virginica = rnorm(n = 150)
)
preds <- preds == apply(preds, 1, max)
actuals <- data.frame(
  setosa = rnorm(n = 150), 
  versicolor = rnorm(n = 150), 
  virginica = rnorm(n = 150)
)
actuals <- actuals == apply(actuals, 1, max)
mcc(preds = preds, actuals = actuals)

# Confusion matrix
mcc(confusionM = matrix(c(0,3,3,3,0,3,3,3,0), nrow = 3))
mcc(confusionM = matrix(c(1,0,0,0,1,0,0,0,1), nrow = 3))