These functions calculate the `pos()`

(number of estimated
positive cases) of a measurement system compared to the reference results
(the "truth").

## Usage

```
pos(data, ...)
# S3 method for data.frame
pos(
data,
truth,
estimate,
estimator = NULL,
na_rm = FALSE,
case_weights = NULL,
event_level = "first",
...
)
pos_vec(
truth,
estimate,
estimator = NULL,
na_rm = FALSE,
case_weights = NULL,
event_level = "first",
...
)
```

## Arguments

- data
Either a

`data.frame`

containing the columns specified by the`truth`

and`estimate`

arguments, or a`table`

/`matrix`

where the true class results should be in the columns of the table.- ...
Not currently used.

- truth
The column identifier for the true class results (that is a

`factor`

). This should be an unquoted column name although this argument is passed by expression and supports quasiquotation (you can unquote column names). For`_vec()`

functions, a`factor`

vector.- estimate
The column identifier for the predicted class results (that is also

`factor`

). As with`truth`

this can be specified different ways but the primary method is to use an unquoted variable name. For`_vec()`

functions, a`factor`

vector.- estimator
One of:

`"binary"`

,`"macro"`

,`"macro_weighted"`

, or`"micro"`

to specify the type of averaging to be done.`"binary"`

is only relevant for the two class case. The other three are general methods for calculating multiclass metrics. The default will automatically choose`"binary"`

or`"macro"`

based on`estimate`

.- na_rm
A

`logical`

value indicating whether`NA`

values should be stripped before the computation proceeds.- case_weights
The optional column identifier for case weights. This should be an unquoted column name that evaluates to a numeric column in

`data`

. For`_vec()`

functions, a numeric vector,`hardhat::importance_weights()`

, or`hardhat::frequency_weights()`

.- event_level
A single string. Either

`"first"`

or`"second"`

to specify which level of`truth`

to consider as the "event". This argument is only applicable when`estimator = "binary"`

. The default uses an internal helper that defaults to`"first"`

.

## Value

A `tibble`

with columns `.metric`

, `.estimator`

, and
`.estimate`

with 1 row of values.

For grouped data frames, the number of rows returned will be the same as the number of groups.

For `pos_vec()`

, a single `numeric`

value (or `NA`

).

## Examples

```
# Two class example data
two_class_example <- data.frame(
truth = as.factor(sample(c("Class1", "Class2"), 100, replace = TRUE)),
predicted = as.factor(sample(c("Class1", "Class2"), 100, replace = TRUE))
)
# Compute number of estimated "positive" classes
pos(two_class_example, truth = truth, estimate = predicted)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <int>
#> 1 pos binary 44
pos_vec(two_class_example$truth, two_class_example$predicted)
#> [1] 44
```