These functions calculate the `neg()`

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

## Usage

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

## Arguments

- data
Either a

`data.frame`

containing the`truth`

and`estimate`

columns, 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.- 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 generally defaults to`"first"`

, however, if the deprecated global option`yardstick.event_first`

is set, that will be used instead with a warning.