ppv | R Documentation |
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()
.
ppv(data, ...)
## S3 method for class '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(),
...
)
data |
Either a |
... |
Not currently used. |
truth |
The column identifier for the true class results
(that is a |
estimate |
The column identifier for the predicted class
results (that is also |
prevalence |
A numeric value for the rate of the "positive" class of the data. |
estimator |
One of: |
na_rm |
A |
case_weights |
The optional column identifier for case weights.
This should be an unquoted column name that evaluates to a numeric column
in |
event_level |
A single string. Either |
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.
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
).
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.
Max Kuhn
Altman, D.G., Bland, J.M. (1994) “Diagnostic tests 2: predictive values,” British Medical Journal, vol 309, 102.
Other class metrics:
accuracy()
,
bal_accuracy()
,
detection_prevalence()
,
f_meas()
,
j_index()
,
kap()
,
mcc()
,
npv()
,
precision()
,
recall()
,
sens()
,
spec()
Other sensitivity metrics:
npv()
,
sens()
,
spec()
# Two class
data("two_class_example")
ppv(two_class_example, truth, predicted)
# Multiclass
library(dplyr)
data(hpc_cv)
hpc_cv %>%
filter(Resample == "Fold01") %>%
ppv(obs, pred)
# Groups are respected
hpc_cv %>%
group_by(Resample) %>%
ppv(obs, pred)
# Weighted macro averaging
hpc_cv %>%
group_by(Resample) %>%
ppv(obs, pred, estimator = "macro_weighted")
# Vector version
ppv_vec(
two_class_example$truth,
two_class_example$predicted
)
# Making Class2 the "relevant" level
ppv_vec(
two_class_example$truth,
two_class_example$predicted,
event_level = "second"
)
# But what if we think that Class 1 only occurs 40% of the time?
ppv(two_class_example, truth, predicted, prevalence = 0.40)
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