These functions calculate the fp()
(number of false positives)
of a measurement system compared to the reference results (the "truth").
Usage
fp(data, ...)
# S3 method for data.frame
fp(
data,
truth,
estimate,
estimator = NULL,
na_rm = FALSE,
event_level = "first",
...
)
fp_vec(
truth,
estimate,
estimator = NULL,
na_rm = FALSE,
event_level = "first",
...
)
Arguments
- data
Either a
data.frame
containing thetruth
andestimate
columns, or atable
/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, afactor
vector.- estimate
The column identifier for the predicted class results (that is also
factor
). As withtruth
this can be specified different ways but the primary method is to use an unquoted variable name. For_vec()
functions, afactor
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 onestimate
.- na_rm
A
logical
value indicating whetherNA
values should be stripped before the computation proceeds.- event_level
A single string. Either
"first"
or"second"
to specify which level oftruth
to consider as the "event". This argument is only applicable whenestimator = "binary"
. The default uses an internal helper that generally defaults to"first"
, however, if the deprecated global optionyardstick.event_first
is set, that will be used instead with a warning.