These functions calculate the spec() (specificity) of a measurement system
compared to a reference result (the "truth" or gold standard).
Highly related functions are sens(), ppv(), and npv().
spec(data, ...)
# S3 method for class 'data.frame'
spec(
data,
truth,
estimate,
estimator = NULL,
na_rm = TRUE,
case_weights = NULL,
event_level = yardstick_event_level(),
...
)
spec_vec(
truth,
estimate,
estimator = NULL,
na_rm = TRUE,
case_weights = NULL,
event_level = yardstick_event_level(),
...
)
specificity(data, ...)
# S3 method for class 'data.frame'
specificity(
data,
truth,
estimate,
estimator = NULL,
na_rm = TRUE,
case_weights = NULL,
event_level = yardstick_event_level(),
...
)
specificity_vec(
truth,
estimate,
estimator = NULL,
na_rm = TRUE,
case_weights = NULL,
event_level = yardstick_event_level(),
...
)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.
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.
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.
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.
A logical value indicating whether NA
values should be stripped before the computation proceeds.
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().
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".
A tibble with columns .metric, .estimator,
and .estimate and 1 row of values.
For grouped data frames, the number of rows returned will be the same as the number of groups.
For spec_vec(), a single numeric value (or NA).
The specificity measures the proportion of negatives that are correctly identified as negatives.
When the denominator of the calculation is 0, specificity is undefined.
This happens when both # true_negative = 0 and # false_positive = 0
are true, which mean that there were no true negatives. When computing binary
specificity, a NA value will be returned with a warning. When computing
multiclass specificity, the individual NA values will be removed, and the
computation will procede, with a warning.
There is no common convention on which factor level should
automatically be considered the "event" or "positive" result
when computing binary classification metrics. In yardstick, the default
is to use the first level. To alter this, change the argument
event_level to "second" to consider the last level of the factor the
level of interest. For multiclass extensions involving one-vs-all
comparisons (such as macro averaging), this option is ignored and
the "one" level is always the relevant result.
Macro, micro, and macro-weighted averaging is available for this metric.
The default is to select macro averaging if a truth factor with more
than 2 levels is provided. Otherwise, a standard binary calculation is done.
See vignette("multiclass", "yardstick") for more information.
Suppose a 2x2 table with notation:
| Reference | ||
| Predicted | Positive | Negative |
| Positive | A | B |
| Negative | C | D |
The formulas used here are:
$$Sensitivity = A/(A+C)$$ $$Specificity = D/(B+D)$$ $$Prevalence = (A+C)/(A+B+C+D)$$ $$PPV = (Sensitivity * Prevalence) / ((Sensitivity * Prevalence) + ((1-Specificity) * (1-Prevalence)))$$ $$NPV = (Specificity * (1-Prevalence)) / (((1-Sensitivity) * Prevalence) + ((Specificity) * (1-Prevalence)))$$
See the references for discussions of the statistics.
Altman, D.G., Bland, J.M. (1994) “Diagnostic tests 1: sensitivity and specificity,” British Medical Journal, vol 308, 1552.
# Two class
data("two_class_example")
spec(two_class_example, truth, predicted)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 spec binary 0.793
# Multiclass
library(dplyr)
data(hpc_cv)
hpc_cv %>%
filter(Resample == "Fold01") %>%
spec(obs, pred)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 spec macro 0.886
# Groups are respected
hpc_cv %>%
group_by(Resample) %>%
spec(obs, pred)
#> # A tibble: 10 × 4
#> Resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Fold01 spec macro 0.886
#> 2 Fold02 spec macro 0.882
#> 3 Fold03 spec macro 0.899
#> 4 Fold04 spec macro 0.879
#> 5 Fold05 spec macro 0.881
#> 6 Fold06 spec macro 0.873
#> 7 Fold07 spec macro 0.866
#> 8 Fold08 spec macro 0.884
#> 9 Fold09 spec macro 0.867
#> 10 Fold10 spec macro 0.875
# Weighted macro averaging
hpc_cv %>%
group_by(Resample) %>%
spec(obs, pred, estimator = "macro_weighted")
#> # A tibble: 10 × 4
#> Resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Fold01 spec macro_weighted 0.816
#> 2 Fold02 spec macro_weighted 0.815
#> 3 Fold03 spec macro_weighted 0.839
#> 4 Fold04 spec macro_weighted 0.803
#> 5 Fold05 spec macro_weighted 0.812
#> 6 Fold06 spec macro_weighted 0.795
#> 7 Fold07 spec macro_weighted 0.790
#> 8 Fold08 spec macro_weighted 0.814
#> 9 Fold09 spec macro_weighted 0.795
#> 10 Fold10 spec macro_weighted 0.801
# Vector version
spec_vec(
two_class_example$truth,
two_class_example$predicted
)
#> [1] 0.7933884
# Making Class2 the "relevant" level
spec_vec(
two_class_example$truth,
two_class_example$predicted,
event_level = "second"
)
#> [1] 0.879845