Compute positive and negative predictive value

Usage

compute_predictive_value_from_rates(sensitivity, specificity, prevalence)

Arguments

sensitivity, specificity, prevalence: vectors of confusion matrix rates

Value

a tibble with the columns sensitivity, specificity, prevalence, ppv, npv where ppv and npv are the positive predictive value and the negative predictive value.

Details

These vectors passed into this function should be some common length and/or length 1. For example, 4 sensitivities, 4 specificities and 1 incidence will work because the sensitivities and specificities have a common length and we can safely recycle (reuse) the incidence value. But 4 sensitivities, 2 specificities, and 1 incidence will fail because there is not a common length.

Examples

compute_predictive_value_from_rates(
  sensitivity = .9,
  specificity = .8,
  prevalence = .05
)
#> # A tibble: 1 × 5
#>   sensitivity specificity prevalence   ppv   npv
#>         <dbl>       <dbl>      <dbl> <dbl> <dbl>
#> 1         0.9         0.8       0.05 0.191 0.993

compute_predictive_value_from_rates(
  sensitivity = .67,
  specificity = .53,
  prevalence = c(.15, .3)
)
#> # A tibble: 2 × 5
#>   sensitivity specificity prevalence   ppv   npv
#>         <dbl>       <dbl>      <dbl> <dbl> <dbl>
#> 1        0.67        0.53       0.15 0.201 0.901
#> 2        0.67        0.53       0.3  0.379 0.789