Tidy a(n) roc object

Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

# S3 method for class 'roc'
tidy(x, ...)

Arguments

x

An roc object returned from a call to AUC::roc().

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Two exceptions here are:

tidy() methods will warn when supplied an exponentiate argument if it will be ignored.
augment() methods will warn when supplied a newdata argument if it will be ignored.

Value

A tibble::tibble() with columns:

cutoff: The cutoff used for classification. Observations with predicted probabilities above this value were assigned class 1, and observations with predicted probabilities below this value were assigned class 0.
fpr: False positive rate.
tpr: The true positive rate at the given cutoff.

Examples


# load libraries for models and data
library(AUC)
#> AUC 0.3.2
#> Type AUCNews() to see the change log and ?AUC to get an overview.
#> 
#> Attaching package: ‘AUC’
#> The following objects are masked from ‘package:caret’:
#> 
#>     sensitivity, specificity

# load data
data(churn)

# fit model
r <- roc(churn$predictions, churn$labels)

# summarize with tidiers + visualization
td <- tidy(r)
td
#> # A tibble: 220 × 3
#>    cutoff     fpr   tpr
#>     <dbl>   <dbl> <dbl>
#>  1  1     0       0    
#>  2  1     0.00262 0.164
#>  3  0.972 0.00350 0.164
#>  4  0.968 0.00350 0.182
#>  5  0.964 0.00350 0.189
#>  6  0.96  0.00350 0.201
#>  7  0.932 0.00437 0.201
#>  8  0.91  0.00437 0.208
#>  9  0.908 0.00525 0.208
#> 10  0.902 0.00525 0.214
#> # ℹ 210 more rows

library(ggplot2)

ggplot(td, aes(fpr, tpr)) +
  geom_line()


# compare the ROC curves for two prediction algorithms
library(dplyr)
library(tidyr)

rocs <- churn %>%
  pivot_longer(contains("predictions"),
    names_to = "algorithm",
    values_to = "value"
  ) %>%
  nest(data = -algorithm) %>%
  mutate(tidy_roc = purrr::map(data, ~ tidy(roc(.x$value, .x$labels)))) %>%
  unnest(tidy_roc)

ggplot(rocs, aes(fpr, tpr, color = algorithm)) +
  geom_line()

Arguments

See also

Value

Examples