deltap estimates the Markedness or deltaP for a nominal/categorical predicted-observed dataset.

mk estimates the Markedness (equivalent to deltaP) for a nominal/categorical predicted-observed dataset.

## Usage

deltap(
data = NULL,
obs,
pred,
pos_level = 2,
atom = FALSE,
tidy = FALSE,
na.rm = TRUE
)

mk(
data = NULL,
obs,
pred,
pos_level = 2,
atom = FALSE,
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.

atom

Logical operator (TRUE/FALSE) to decide if the estimate is made for each class (atom = TRUE) or at a global level (atom = FALSE); Default : FALSE. When dataset is "binomial" atom does not apply.

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 deltap is also known as the markedness. It is a metric that quantifies the probability that a condition is marked by the predictor with respect to a random chance (Powers, 2011).

The deltap is related to precision (or positive predictive values -ppv-) and its inverse (the negative predictive value -npv-) as follows:

$$deltap = PPV + NPV - 1 = precision + npv - 1$$

The higher the deltap the better the classification performance.

For the formula and more details, see online-documentation

Powers, D.M.W. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation. Journal of Machine Learning Technologies 2(1): 37–63. doi:10.48550/arXiv.2010.16061