An example using the *_constrain metrics

[GuiAlDuS]

Hi Christian!

I've been trying to calculate thresholds with the spec_constrain metric but I've been unable to "understand" how or where I should put the sensitivity I want.
Can you please give an example of how to use those constrains metrics?

Thanks!

[Thie1e]

Hi,

thanks for reaching out. I've had a look at the documentation of the constrained metrics and I'll have to admit that the examples could probably be more helpful. Also, the code comment in the example is not quite correct.

I have written the following examples. Do they illustrate well enough how to use the functions? If so, these examples will replace the previous example in the help file.

## Maximum sensitivity when Positive Predictive Value (PPV) is at least 75%
cp <- cutpointr(data = suicide, x = dsi, class = suicide,
                method = maximize_metric,
                metric = sens_constrain, 
                constrain_metric = ppv, 
                min_constrain = 0.75)
## All metric values (m) where PPV < 0.75 are zero
plot_metric(cp)
cp$roc_curve
## We can confirm that PPV is indeed >= 0.75
cp %>% 
    add_metric(list(ppv))
## We can also do so for the complete ROC curve(s)
cp %>% 
    pull(roc_curve) %>% 
    map(~ add_metric(., list(sensitivity, ppv)))

## Use the metric_constrain function for a combination of any two metrics
## Estimate optimal cutpoint for precision given a recall of at least 70%
cp <- cutpointr(data = suicide, x = dsi, class = suicide,
                subgroup = gender,
                method = maximize_metric,
                metric = metric_constrain, 
                main_metric = precision,
                suffix = "_constrain",
                constrain_metric = recall, 
                min_constrain = 0.70)
## All metric values (m) where recall < 0.7 are zero
plot_metric(cp)
## We can confirm that recall is indeed >= 0.70 and that precision_constrain
## is identical to precision for the estimated cutpoint
cp %>% 
    add_metric(list(recall, precision))
## We can also do so for the complete ROC curve(s)
cp %>% 
    pull(roc_curve) %>% 
    map(~ add_metric(., list(recall, precision)))