`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

## References

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

## 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 deltap estimate for two-class case
deltap(data = binomial_case, obs = labels, pred = predictions)
#> $deltap
#> [1] -0.008856683
#>
# }
```