Skip to contents

Calculate ROC curve points

roc_points.Rd

Calculates a series pairs of (FPR, TPR) which correspond to points displayed by ROC curve. "false positive ratio" will be represented on x axis, while "true positive ratio" on y one.

Usage

roc_points(data = NULL, response, predictor, .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.

.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:

  • "tpr". Containing values for "true positive ratio", or y axis.

  • "fpr". Containing values for "false positive ratio", or x axis.

Examples

# Calc ROC points of Sepal.Width as a classifier of setosa species
roc_points(iris, Species, Sepal.Width)
#> # A tibble: 151 × 2
#>      tpr   fpr
#>  * <dbl> <dbl>
#>  1  1     1   
#>  2  1     0.99
#>  3  1     0.96
#>  4  1     0.96
#>  5  1     0.96
#>  6  0.98  0.93
#>  7  0.98  0.93
#>  8  0.98  0.93
#>  9  0.98  0.93
#> 10  0.98  0.9 
#> # ℹ 141 more rows
# Change class to predict to virginica
roc_points(iris, Species, Sepal.Width, .condition = "virginica")
#> # A tibble: 151 × 2
#>      tpr   fpr
#>  * <dbl> <dbl>
#>  1  1     1   
#>  2  1     0.99
#>  3  0.98  0.97
#>  4  0.98  0.97
#>  5  0.98  0.97
#>  6  0.98  0.93
#>  7  0.98  0.93
#>  8  0.98  0.93
#>  9  0.98  0.93
#> 10  0.98  0.9 
#> # ℹ 141 more rows