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 cross 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 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. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

tidy(), AUC::roc()

## 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


library(AUC)#> AUC 0.3.0#> 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, specificitydata(churn)
r <- roc(churn$predictions,churn$labels)

td <- tidy(r)
td#> # A tibble: 220 x 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
#> # … with 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 %>%
gather(algorithm, value, -labels) %>%
nest(-algorithm) %>%
mutate(tidy_roc = purrr::map(data, ~tidy(roc(.x$value, .x$labels)))) %>%
unnest(tidy_roc)#> Warning: All elements of ... must be named.
#> Did you want data = c(labels, value)?
ggplot(rocs, aes(fpr, tpr, color = algorithm)) +
geom_line()