These functions calculate the npv() (negative predictive value) of a measurement system compared to a reference result (the "truth" or gold standard). Highly related functions are spec(), sens(), and ppv().

npv(data, ...)

# S3 method for data.frame
npv(
  data,
  truth,
  estimate,
  prevalence = NULL,
  estimator = NULL,
  na_rm = TRUE,
  ...
)

npv_vec(
  truth,
  estimate,
  prevalence = NULL,
  estimator = NULL,
  na_rm = TRUE,
  ...
)

Arguments

data

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.

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, a factor vector.

estimate

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.

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 on estimate.

na_rm

A logical value indicating whether NA values should be stripped before the computation proceeds.

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 npv_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. 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.

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
PredictedPositiveNegative
PositiveAB
NegativeCD

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.

See also

Other class metrics: accuracy(), bal_accuracy(), detection_prevalence(), f_meas(), j_index(), kap(), mcc(), ppv(), precision(), recall(), sens(), spec()

Other sensitivity metrics: ppv(), sens(), spec()

Examples

# Two class data("two_class_example") npv(two_class_example, truth, predicted)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 npv binary 0.861
# Multiclass library(dplyr) data(hpc_cv) hpc_cv %>% filter(Resample == "Fold01") %>% npv(obs, pred)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 npv macro 0.906
# Groups are respected hpc_cv %>% group_by(Resample) %>% npv(obs, pred)
#> # A tibble: 10 x 4 #> Resample .metric .estimator .estimate #> <chr> <chr> <chr> <dbl> #> 1 Fold01 npv macro 0.906 #> 2 Fold02 npv macro 0.901 #> 3 Fold03 npv macro 0.917 #> 4 Fold04 npv macro 0.897 #> 5 Fold05 npv macro 0.897 #> 6 Fold06 npv macro 0.892 #> 7 Fold07 npv macro 0.882 #> 8 Fold08 npv macro 0.902 #> 9 Fold09 npv macro 0.879 #> 10 Fold10 npv macro 0.890
# Weighted macro averaging hpc_cv %>% group_by(Resample) %>% npv(obs, pred, estimator = "macro_weighted")
#> # A tibble: 10 x 4 #> Resample .metric .estimator .estimate #> <chr> <chr> <chr> <dbl> #> 1 Fold01 npv macro_weighted 0.896 #> 2 Fold02 npv macro_weighted 0.890 #> 3 Fold03 npv macro_weighted 0.905 #> 4 Fold04 npv macro_weighted 0.878 #> 5 Fold05 npv macro_weighted 0.878 #> 6 Fold06 npv macro_weighted 0.871 #> 7 Fold07 npv macro_weighted 0.853 #> 8 Fold08 npv macro_weighted 0.885 #> 9 Fold09 npv macro_weighted 0.845 #> 10 Fold10 npv macro_weighted 0.864
# Vector version npv_vec(two_class_example$truth, two_class_example$predicted)
#> [1] 0.8609865
# Making Class2 the "relevant" level options(yardstick.event_first = FALSE) npv_vec(two_class_example$truth, two_class_example$predicted)
#> [1] 0.8194946
options(yardstick.event_first = TRUE)