These functions calculate the ppv()
(positive predictive value) of a
measurement system compared to a reference result (the "truth" or gold standard).
Highly related functions are spec()
, sens()
, and npv()
.
Usage
ppv(data, ...)
# S3 method for data.frame
ppv(
data,
truth,
estimate,
prevalence = NULL,
estimator = NULL,
na_rm = TRUE,
case_weights = NULL,
event_level = yardstick_event_level(),
...
)
ppv_vec(
truth,
estimate,
prevalence = NULL,
estimator = NULL,
na_rm = TRUE,
case_weights = NULL,
event_level = yardstick_event_level(),
...
)
Arguments
- data
Either a
data.frame
containing the columns specified by thetruth
andestimate
arguments, 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.- prevalence
A numeric value for the rate of the "positive" class of the data.
- 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.- 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()
, orhardhat::frequency_weights()
.- 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 defaults to"first"
.
Value
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 ppv_vec()
, a single numeric
value (or NA
).
Details
The positive predictive value (ppv()
) is defined as the percent of
predicted positives that are actually positive while the
negative predictive value (npv()
) is defined as the percent of negative
positives that are actually negative.
Relevant Level
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.
Multiclass
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.
Implementation
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.
References
Altman, D.G., Bland, J.M. (1994) ``Diagnostic tests 2: predictive values,'' British Medical Journal, vol 309, 102.
Examples
# Two class
data("two_class_example")
ppv(two_class_example, truth, predicted)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 ppv binary 0.819
# Multiclass
library(dplyr)
data(hpc_cv)
hpc_cv %>%
filter(Resample == "Fold01") %>%
ppv(obs, pred)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 ppv macro 0.637
# Groups are respected
hpc_cv %>%
group_by(Resample) %>%
ppv(obs, pred)
#> # A tibble: 10 × 4
#> Resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Fold01 ppv macro 0.637
#> 2 Fold02 ppv macro 0.603
#> 3 Fold03 ppv macro 0.706
#> 4 Fold04 ppv macro 0.658
#> 5 Fold05 ppv macro 0.651
#> 6 Fold06 ppv macro 0.626
#> 7 Fold07 ppv macro 0.562
#> 8 Fold08 ppv macro 0.652
#> 9 Fold09 ppv macro 0.605
#> 10 Fold10 ppv macro 0.625
# Weighted macro averaging
hpc_cv %>%
group_by(Resample) %>%
ppv(obs, pred, estimator = "macro_weighted")
#> # A tibble: 10 × 4
#> Resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Fold01 ppv macro_weighted 0.697
#> 2 Fold02 ppv macro_weighted 0.690
#> 3 Fold03 ppv macro_weighted 0.752
#> 4 Fold04 ppv macro_weighted 0.690
#> 5 Fold05 ppv macro_weighted 0.705
#> 6 Fold06 ppv macro_weighted 0.682
#> 7 Fold07 ppv macro_weighted 0.649
#> 8 Fold08 ppv macro_weighted 0.702
#> 9 Fold09 ppv macro_weighted 0.661
#> 10 Fold10 ppv macro_weighted 0.683
# Vector version
ppv_vec(
two_class_example$truth,
two_class_example$predicted
)
#> [1] 0.8194946
# Making Class2 the "relevant" level
ppv_vec(
two_class_example$truth,
two_class_example$predicted,
event_level = "second"
)
#> [1] 0.8609865
# But what if we think that Class 1 only occurs 40% of the time?
ppv(two_class_example, truth, predicted, prevalence = 0.40)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 ppv binary 0.740