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 data.frame
  prevalence = NULL,
  estimator = NULL,
  na_rm = TRUE,

  prevalence = NULL,
  estimator = NULL,
  na_rm = TRUE,



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.


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.


A numeric value for the rate of the "positive" class of the data.


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.


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).


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. In yardstick, the default is to use the first level. To change this, a global option called yardstick.event_first is set to TRUE when the package is loaded. This can be changed to FALSE if the last level of the factor is considered the level of interest by running: options(yardstick.event_first = FALSE). 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:


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 2: predictive values,'' British Medical Journal, vol 309, 102.

See also

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)
#> # A tibble: 1 x 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 x 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 x 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 x 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 options(yardstick.event_first = FALSE) ppv_vec(two_class_example$truth, two_class_example$predicted)
#> [1] 0.8609865
options(yardstick.event_first = TRUE) # 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 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 ppv binary 0.740