Documentation and cutpointr output suggestion about cutpointr

[sheilasaia]

@Thie1e Thanks so much for your through reply! I appreciate it and the code you sent along as well. That's very helpful.

[Thie1e]

Hi, thanks for taking the time to write this detailed comment.

Regarding columns in the output, I have to admit that I'm quite sure that I'm not going to change that in the near future, just because it would break a lot of older code and I would have to rewrite many of the internal functions. The way the main metric is reported now is consistent with the other metrics that also have their own columns (I know, their names are not dynamic). Another reason to do it this way was to not make the output any wider, as it is already very wide.

What you seem to be concerned with is binding rows. I agree that using base R methods this might be tricky, but with bind_rows it works quite well in both cases (different optimized metrics and with/without subgroups). I have included some examples below. Maybe you've already done it that way anyway, I just wanted to illustrate how I would do it. Or is there some specific kind of wrangling the output that still can't be done?

You're right about the documentation of boot_stratify, thanks. Of course I meant so say 'keeping the proportion of positives and negatives constant"..,

library(tidyverse)
library(cutpointr)

# Bind rows of cutpointr objects with and without subgroup ----------------

cp1 <- cutpointr(suicide, dsi, suicide, metric = accuracy)
#> Assuming the positive class is yes
#> Assuming the positive class has higher x values
cp2 <- cutpointr(suicide, dsi, suicide, gender, metric = accuracy)
#> Assuming the positive class is yes
#> Assuming the positive class has higher x values
bind_rows(cp1, cp2)
#> # A tibble: 3 x 18
#>   direction optimal_cutpoint method          accuracy      acc sensitivity
#>   <chr>                <dbl> <chr>              <dbl>    <dbl>       <dbl>
#> 1 >=                       6 maximize_metric 0.951128 0.951128    0.444444
#> 2 >=                       6 maximize_metric 0.956633 0.956633    0.444444
#> 3 >=                       8 maximize_metric 0.957143 0.957143    0.333333
#>   specificity      AUC pos_class neg_class prevalence outcome predictor
#>         <dbl>    <dbl> <fct>     <fct>          <dbl> <chr>   <chr>    
#> 1    0.987903 0.923779 yes       no         0.0676692 suicide dsi      
#> 2    0.994521 0.944647 yes       no         0.0688776 suicide dsi      
#> 3    1        0.861747 yes       no         0.0642857 suicide dsi      
#>   data               roc_curve                 boot  subgroup grouping
#>   <list>             <list>                    <lgl> <chr>    <chr>   
#> 1 <tibble [532 x 2]> <roc_cutpointr [13 x 10]> NA    <NA>     <NA>    
#> 2 <tibble [392 x 2]> <roc_cutpointr [11 x 10]> NA    female   gender  
#> 3 <tibble [140 x 2]> <roc_cutpointr [11 x 10]> NA    male     gender


# Bind rows if different metrics were maximized and avoid NAs -------------

cp1 <- cutpointr(suicide, dsi, suicide, metric = youden) %>% 
    add_metric(list(accuracy))
#> Assuming the positive class is yes
#> Assuming the positive class has higher x values
cp2 <- cutpointr(suicide, dsi, suicide, metric = accuracy) %>% 
    add_metric(list(youden))
#> Assuming the positive class is yes
#> Assuming the positive class has higher x values
bind_rows(cp1, cp2)
#> # A tibble: 2 x 17
#>   direction optimal_cutpoint method            youden      acc sensitivity
#>   <chr>                <dbl> <chr>              <dbl>    <dbl>       <dbl>
#> 1 >=                       2 maximize_metric 0.751792 0.864662    0.888889
#> 2 >=                       6 maximize_metric 0.432348 0.951128    0.444444
#>   specificity      AUC pos_class neg_class prevalence outcome predictor
#>         <dbl>    <dbl> <fct>     <fct>          <dbl> <chr>   <chr>    
#> 1    0.862903 0.923779 yes       no         0.0676692 suicide dsi      
#> 2    0.987903 0.923779 yes       no         0.0676692 suicide dsi      
#>   data               roc_curve                 boot  accuracy
#>   <list>             <list>                    <lgl>    <dbl>
#> 1 <tibble [532 x 2]> <roc_cutpointr [13 x 10]> NA    0.864662
#> 2 <tibble [532 x 2]> <roc_cutpointr [13 x 10]> NA    0.951128

^{Created on 2021-05-05 by the reprex package (v1.0.0)}

[Thie1e]

You're welcome, good to hear that the examples were helpful.