It estimates the mcc for a nominal/categorical predicted-observed dataset.
phi_coef estimates the Phi coefficient
(equivalent to the Matthews Correlation Coefficient mcc).
Usage
mcc(data = NULL, obs, pred, pos_level = 2, tidy = FALSE, na.rm = TRUE)
phi_coef(data = NULL, obs, pred, pos_level = 2, tidy = FALSE, na.rm = TRUE)Arguments
- data
(Optional) argument to call an existing data frame containing the data.
- obs
Vector with observed values (character | factor).
- pred
Vector with predicted values (character | factor).
- pos_level
Integer, for binary cases, indicating the order (1|2) of the level corresponding to the positive. Generally, the positive level is the second (2) since following an alpha-numeric order, the most common pairs are
(Negative | Positive),(0 | 1),(FALSE | TRUE). Default : 2.- tidy
Logical operator (TRUE/FALSE) to decide the type of return. TRUE returns a data.frame, FALSE returns a list; Default : FALSE.
- na.rm
Logic argument to remove rows with missing values (NA). Default is na.rm = TRUE.
Value
an object of class numeric within a list (if tidy = FALSE) or within a
data frame (if tidy = TRUE).
Details
The mcc it is also known as the phi-coefficient. It has gained
popularity within the machine learning community to summarize into a single
value the confusion matrix of a binary classification.
It is particularly useful when the number of observations belonging to each class is uneven or imbalanced. It is characterized for being symmetric (i.e. no class has more relevance than the other). It is bounded between -1 and 1. The closer to 1 the better the classification performance.
For the formula and more details, see online-documentation
References
Chicco, D., Jurman, G. (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21, 6 (2020). doi:10.1186/s12864-019-6413-7
Examples
# \donttest{
set.seed(123)
# Two-class
binomial_case <- data.frame(labels = sample(c("True","False"), 100, replace = TRUE),
predictions = sample(c("True","False"), 100, replace = TRUE))
# Get mcc estimate for two-class case
mcc(data = binomial_case, obs = labels, pred = predictions, tidy = TRUE)
#> mcc
#> 1 -0.008916106
# }