Author: Christian Thiele
Date: 2025-06-13

# ROC curves with cutpointr

#### Christian Thiele

#### 2025-06-13

## Calculating only the ROC curve

When running `cutpointr`, a ROC curve is by default returned in the column `roc_curve`. This ROC curve can be plotted using `plot_roc`. Alternatively, if only the ROC curve is desired and no cutpoint needs to be calculated, the ROC curve can be created using `roc()` and plotted using `plot_cutpointr`. The `roc` function, unlike `cutpointr`, does not determine `direction`, `pos_class` or `neg_class` automatically.

    library(cutpointr)
    roc_curve <- roc(data = suicide, x = dsi, class = suicide,
        pos_class = "yes", neg_class = "no", direction = ">=")
    auc(roc_curve)

    ## [1] 0.9237791

    head(roc_curve)

    ## # A tibble: 6 × 9
    ##   x.sorted    tp    fp    tn    fn    tpr   tnr     fpr   fnr
    ##      <dbl> <dbl> <dbl> <int> <int>  <dbl> <dbl>   <dbl> <dbl>
    ## 1      Inf     0     0   496    36 0      1     0       1
    ## 2       11     1     0   496    35 0.0278 1     0       0.972
    ## 3       10     2     1   495    34 0.0556 0.998 0.00202 0.944
    ## 4        9     3     1   495    33 0.0833 0.998 0.00202 0.917
    ## 5        8     4     1   495    32 0.111  0.998 0.00202 0.889
    ## 6        7     7     1   495    29 0.194  0.998 0.00202 0.806

    plot_roc(roc_curve)

## ROC curve and optimal cutpoint for multiple variables

Alternatively, we can map the standard evaluation version `cutpointr` to the column names. If `direction` and / or `pos_class` and `neg_class` are unspecified, these parameters will automatically be determined by cutpointr so that the AUC values for all variables will be \(> 0.5\).

We could do this manually, e.g. using `purrr::map`, but to make this task more convenient `multi_cutpointr` can be used to achieve the same result. It maps multiple predictor columns to `cutpointr`, by default all numeric columns except for the class column.

    mcp <- multi_cutpointr(suicide, class = suicide, pos_class = "yes",
                    use_midpoints = TRUE, silent = TRUE)
    summary(mcp)

    ## Method: maximize_metric
    ## Predictor: age, dsi
    ## Outcome: suicide
    ##
    ## Predictor: age
    ## --------------------------------------------------------------------------------
    ##  direction    AUC   n n_pos n_neg
    ##         <= 0.5257 532    36   496
    ##
    ##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn  fp tn
    ##              55.5        1.1154 0.1992      0.9722      0.1431 35  1 425 71
    ##
    ## Predictor summary:
    ##     Data Min. 5% 1st Qu. Median    Mean 3rd Qu.   95% Max.      SD NAs
    ##  Overall   18 19      24   28.0 34.1259   41.25 65.00   83 15.0542   0
    ##       no   18 19      24   28.0 34.2218   41.25 65.50   83 15.1857   0
    ##      yes   18 18      22   27.5 32.8056   41.25 54.25   69 13.2273   0
    ##
    ## Predictor: dsi
    ## --------------------------------------------------------------------------------
    ##  direction    AUC   n n_pos n_neg
    ##         >= 0.9238 532    36   496
    ##
    ##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
    ##               1.5        1.7518 0.8647      0.8889      0.8629 32  4 68 428
    ##
    ## Predictor summary:
    ##     Data Min.   5% 1st Qu. Median   Mean 3rd Qu.  95% Max.     SD NAs
    ##  Overall    0 0.00       0      0 0.9211       1 5.00   11 1.8527   0
    ##       no    0 0.00       0      0 0.6331       0 4.00   10 1.4122   0
    ##      yes    0 0.75       4      5 4.8889       6 9.25   11 2.5498   0

## Accessing `data`, `roc_curve`, and `boot`

The object returned by `cutpointr` is of the classes `cutpointr`, `tbl_df`, `tbl`, and `data.frame`. Thus, it can be handled like a usual data frame. The columns `data`, `roc_curve`, and `boot` consist of nested data frames, which means that these are list columns whose elements are data frames. They can either be accessed using `[` or by using functions from the tidyverse. If subgroups were given, the output contains one row per subgroup and the function that accesses the data should be mapped to every row or the data should be grouped by subgroup.

    set.seed(123)
    opt_cut_b_g <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 500)

    library(dplyr)
    library(tidyr)
    opt_cut_b_g |>
      group_by(subgroup) |>
      select(subgroup, boot) |>
      unnest(cols = boot) |>
      summarise(sd_oc_boot = sd(optimal_cutpoint),
                m_oc_boot  = mean(optimal_cutpoint),
                m_acc_oob  = mean(acc_oob))

    ## # A tibble: 2 × 4
    ##   subgroup sd_oc_boot m_oc_boot m_acc_oob
    ##   <chr>         <dbl>     <dbl>     <dbl>
    ## 1 female        0.766      2.17     0.880
    ## 2 male          1.51       2.92     0.806

## Adding metrics to the result of cutpointr() or roc()

By default, the output of `cutpointr` includes the optimized metric and several other metrics. The `add_metric` function adds further metrics. Here, we’re adding the negative predictive value (NPV) and the positive predictive value (PPV) at the optimal cutpoint per subgroup:

    cutpointr(suicide, dsi, suicide, gender, metric = youden, silent = TRUE) |>
        add_metric(list(ppv, npv)) |>
        select(subgroup, optimal_cutpoint, youden, ppv, npv)

    ## # A tibble: 2 × 5
    ##   subgroup optimal_cutpoint   youden      ppv      npv
    ##   <chr>               <dbl>    <dbl>    <dbl>    <dbl>
    ## 1 female                  2 0.808118 0.367647 0.993827
    ## 2 male                    3 0.625106 0.259259 0.982301

In the same fashion, additional metric columns can be added to a `roc_cutpointr` object:

    roc(data = suicide, x = dsi, class = suicide, pos_class = "yes",
        neg_class = "no", direction = ">=") |>
      add_metric(list(cohens_kappa, F1_score)) |>
      select(x.sorted, tp, fp, tn, fn, cohens_kappa, F1_score) |>
      head()

    ## # A tibble: 6 × 7
    ##   x.sorted    tp    fp    tn    fn cohens_kappa F1_score
    ##      <dbl> <dbl> <dbl> <int> <int>        <dbl>    <dbl>
    ## 1      Inf     0     0   496    36       0        0
    ## 2       11     1     0   496    35       0.0506   0.0541
    ## 3       10     2     1   495    34       0.0931   0.103
    ## 4        9     3     1   495    33       0.138    0.15
    ## 5        8     4     1   495    32       0.182    0.195
    ## 6        7     7     1   495    29       0.301    0.318