Tidy a(n) confusionMatrix 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 confusionMatrix
tidy(x, by_class = TRUE, ...)

Arguments

x	An object of class `confusionMatrix` created by a call to `caret::confusionMatrix()`.
by_class	Logical indicating whether or not to show performance measures broken down by class. Defaults to `TRUE`. When `by_class = FALSE` only returns a tibble with accuracy, kappa, and McNemar statistics.
...	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.

Value

A tibble::tibble() with columns:

class

The class under consideration.

conf.high

Upper bound on the confidence interval for the estimate.

conf.low

Lower bound on the confidence interval for the estimate.

estimate

The estimated value of the regression term.

term

The name of the regression term.

p.value

P-value for accuracy and kappa statistics.

Examples


library(caret)
#> Loading required package: lattice
#> 
#> Attaching package: ‘lattice’
#> The following object is masked from ‘package:boot’:
#> 
#>     melanoma
#> Registered S3 method overwritten by 'lava':
#>   method            from      
#>   print.equivalence partitions
#> Registered S3 methods overwritten by 'pROC':
#>   method    from  
#>   print.roc btergm
#>   plot.roc  btergm
#> 
#> Attaching package: ‘caret’
#> The following object is masked from ‘package:survival’:
#> 
#>     cluster

set.seed(27)

two_class_sample1 <- as.factor(sample(letters[1:2], 100, TRUE))
two_class_sample2 <- as.factor(sample(letters[1:2], 100, TRUE))

two_class_cm <- caret::confusionMatrix(
  two_class_sample1,
  two_class_sample2
)

tidy(two_class_cm)
#> # A tibble: 14 x 6
#>    term                 class estimate conf.low conf.high p.value
#>    <chr>                <chr>    <dbl>    <dbl>     <dbl>   <dbl>
#>  1 accuracy             NA      0.52      0.418     0.621   0.619
#>  2 kappa                NA      0.0295   NA        NA      NA    
#>  3 mcnemar              NA     NA        NA        NA       0.470
#>  4 sensitivity          a       0.604    NA        NA      NA    
#>  5 specificity          a       0.426    NA        NA      NA    
#>  6 pos_pred_value       a       0.542    NA        NA      NA    
#>  7 neg_pred_value       a       0.488    NA        NA      NA    
#>  8 precision            a       0.542    NA        NA      NA    
#>  9 recall               a       0.604    NA        NA      NA    
#> 10 f1                   a       0.571    NA        NA      NA    
#> 11 prevalence           a       0.53     NA        NA      NA    
#> 12 detection_rate       a       0.32     NA        NA      NA    
#> 13 detection_prevalence a       0.59     NA        NA      NA    
#> 14 balanced_accuracy    a       0.515    NA        NA      NA    
tidy(two_class_cm, by_class = FALSE)
#> # A tibble: 3 x 5
#>   term     estimate conf.low conf.high p.value
#>   <chr>       <dbl>    <dbl>     <dbl>   <dbl>
#> 1 accuracy   0.52      0.418     0.621   0.619
#> 2 kappa      0.0295   NA        NA      NA    
#> 3 mcnemar   NA        NA        NA       0.470

# multiclass example

six_class_sample1 <- as.factor(sample(letters[1:6], 100, TRUE))
six_class_sample2 <- as.factor(sample(letters[1:6], 100, TRUE))

six_class_cm <- caret::confusionMatrix(
  six_class_sample1,
  six_class_sample2
)

tidy(six_class_cm)
#> # A tibble: 69 x 6
#>    term           class estimate conf.low conf.high p.value
#>    <chr>          <chr>    <dbl>    <dbl>     <dbl>   <dbl>
#>  1 accuracy       NA      0.2       0.127     0.292   0.795
#>  2 kappa          NA      0.0351   NA        NA      NA    
#>  3 mcnemar        NA     NA        NA        NA       0.873
#>  4 sensitivity    a       0.2      NA        NA      NA    
#>  5 specificity    a       0.888    NA        NA      NA    
#>  6 pos_pred_value a       0.308    NA        NA      NA    
#>  7 neg_pred_value a       0.816    NA        NA      NA    
#>  8 precision      a       0.308    NA        NA      NA    
#>  9 recall         a       0.2      NA        NA      NA    
#> 10 f1             a       0.242    NA        NA      NA    
#> # … with 59 more rows
tidy(six_class_cm, by_class = FALSE)
#> # A tibble: 3 x 5
#>   term     estimate conf.low conf.high p.value
#>   <chr>       <dbl>    <dbl>     <dbl>   <dbl>
#> 1 accuracy   0.2       0.127     0.292   0.795
#> 2 kappa      0.0351   NA        NA      NA    
#> 3 mcnemar   NA        NA        NA       0.873

Arguments

See also

Value

Examples