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Calculate ROC curve partial points

calc_partial_roc_points.Rd

Calculates a series pairs of (FPR, TPR) which correspond to ROC curve points in a specified region.

Usage

calc_partial_roc_points(
  data = NULL,
  response = NULL,
  predictor = NULL,
  lower_threshold,
  upper_threshold,
  ratio,
  .condition = NULL
)

Arguments

data

A data.frame or extension (e.g. a tibble) containing values for predictors and response variables.

response

A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard).

If the variable presents more than two possible outcomes, classes or categories:

  • The outcome of interest (the one to be predicted) will remain distinct.

  • All other categories will be combined into a single category.

New combined category represents the "absence" of the condition to predict. See .condition for more information.

predictor

A data variable which must be numeric, representing values of a classifier or predictor for each observation.

lower_threshold, upper_threshold

Two numbers between 0 and 1, inclusive. These numbers represent lower and upper bounds of the region where to apply calculations.

ratio

Ratio or axis where to apply calculations.

  • If "tpr", only points within the specified region of TPR, y axis, will be considered for calculations.

  • If "fpr", only points within the specified region of FPR, x axis, will be considered for calculations.

.condition

A value from response that represents class, category or condition of interest which wants to be predicted.

If NULL, condition of interest will be selected automatically depending on response type.

Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted.

See vignette("selecting-condition") for further information on how automatic selection is performed and details on selecting the condition of interest.

Value

A tibble with two columns:

  • "partial_tpr". Containing "true positive ratio", or y, values of points within the specified region.

  • "partial_fpr". Containing "false positive ratio", or x, values of points within the specified region.

Examples

# Calc ROC points of Sepal.Width as a classifier of setosa species
# in TPR = (0.9, 1)
calc_partial_roc_points(
 iris,
 response = Species,
 predictor = Sepal.Width,
 lower_threshold = 0.9,
 upper_threshold = 1,
 ratio = "tpr"
)
#> ℹ Upper threshold 1 already included in points.
#> • Skipping upper threshold interpolation
#> # A tibble: 59 × 2
#>      tpr   fpr
#>  * <dbl> <dbl>
#>  1  0.9   0.35
#>  2  0.96  0.45
#>  3  0.96  0.45
#>  4  0.96  0.45
#>  5  0.96  0.45
#>  6  0.96  0.45
#>  7  0.96  0.45
#>  8  0.96  0.45
#>  9  0.96  0.45
#> 10  0.96  0.45
#> # ℹ 49 more rows

# Change class to virginica
calc_partial_roc_points(
 iris,
 response = Species,
 predictor = Sepal.Width,
 lower_threshold = 0.9,
 upper_threshold = 1,
 ratio = "tpr",
 .condition = "virginica"
)
#> ℹ Lower 0.9 and upper 1 thresholds already included in points
#> • Skipping lower and upper threshold interpolation
#> # A tibble: 20 × 2
#>      tpr   fpr
#>  * <dbl> <dbl>
#>  1  0.9   0.86
#>  2  0.9   0.86
#>  3  0.9   0.86
#>  4  0.9   0.86
#>  5  0.9   0.86
#>  6  0.9   0.86
#>  7  0.9   0.86
#>  8  0.9   0.86
#>  9  0.98  0.9 
#> 10  0.98  0.9 
#> 11  0.98  0.9 
#> 12  0.98  0.93
#> 13  0.98  0.93
#> 14  0.98  0.93
#> 15  0.98  0.93
#> 16  0.98  0.97
#> 17  0.98  0.97
#> 18  0.98  0.97
#> 19  1     0.99
#> 20  1     1