Given a vector of true_values and one or more vectors of test values
(passed to ...), computes sensitivity, specificity, positive predictive
value (PPV), negative predictive value (NPV), and Cohen's kappa.
Examples
# create example data w predictors with different properties:
ex_data <- tibble::tibble(
actual = rbinom(250, 1, .3), # 250 cases, p(outcome) = .3
prediction1 = ifelse(runif(250) <= .05, 1L - actual, actual), # 5% error rate
prediction2 = ifelse(runif(250) <= .15, 1L - actual, actual), # 15% error rate
prediction3 = ifelse(runif(250) <= .35, 1L - actual, actual), # 35% error rate
prediction4 = ifelse(runif(250) <= .15, 1L, actual), # 15% with positive bias
prediction5 = ifelse(runif(250) <= .15, 0L, actual) # 15% with negative bias
)
# testing predicted v actual values
ex_data %>%
accuracy_stats(actual, prediction1)
#> # A tibble: 1 × 7
#> Predictor n Kappa Sensitivity Specificity PPV NPV
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 prediction1 250 0.844 0.9 0.947 0.889 0.953
# can test multiple predictors simultaneously
ex_data %>%
accuracy_stats(actual, prediction1:prediction5)
#> # A tibble: 5 × 7
#> Predictor n Kappa Sensitivity Specificity PPV NPV
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 prediction1 250 0.844 0.9 0.947 0.889 0.953
#> 2 prediction2 250 0.636 0.85 0.824 0.694 0.921
#> 3 prediction3 250 0.214 0.6 0.635 0.436 0.771
#> 4 prediction4 250 0.820 1 0.876 0.792 1
#> 5 prediction5 250 0.845 0.8 1 1 0.914
# if `include_counts` = TRUE, will also return n of false positives,
# false negatives, etc., as well as and observed and expected % agreement
ex_data %>%
accuracy_stats(actual, prediction1:prediction5, include_counts = TRUE)
#> # A tibble: 5 × 13
#> Predictor n TP FP TN FN pAgreeObserved pAgreeExpected Kappa
#> <fct> <dbl> <int> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1 prediction1 250 72 9 161 8 0.932 0.563 0.844
#> 2 prediction2 250 68 30 140 12 0.832 0.539 0.636
#> 3 prediction3 250 48 62 108 32 0.624 0.522 0.214
#> 4 prediction4 250 80 21 149 0 0.916 0.535 0.820
#> 5 prediction5 250 64 0 170 16 0.936 0.588 0.845
#> # ℹ 4 more variables: Sensitivity <dbl>, Specificity <dbl>, PPV <dbl>,
#> # NPV <dbl>