dx_npv: Calculate Negative Predictive Value (NPV)

In overdodactyl/diagnosticSummary: Diagnostic Summaries

Calculate Negative Predictive Value (NPV)

Description

Calculates the proportion of true negatives out of the total predicted negatives (true negatives + false negatives), known as the Negative Predictive Value (NPV). This metric is a measure of the classifier's ability to identify negatives correctly. Note that NPV, like other metrics, may not fully represent classifier performance in unbalanced datasets and should be used alongside other metrics.

Usage

dx_npv(cm, detail = "full", ...)

Arguments

cm	A dx_cm object created by dx_cm().
detail	Character specifying the level of detail in the output: "simple" for raw estimate, "full" for detailed estimate including 95% confidence intervals.
...	Additional arguments to pass to metric_binomial function, such as citype for type of confidence interval method.

Details

NPV is the ratio of true negatives to the sum of true and false negatives. It is an indicator of how well the classifier can identify negative instances. High NPV means that the classifier is reliable in its negative classifications. However, it may be influenced by the prevalence of the condition and is best used in conjunction with other metrics like PPV, sensitivity, and specificity for a comprehensive evaluation.

The formula for NPV is:

NPV = \frac{True Negatives}{True Negatives + False Negatives}

Value

Depending on the detail parameter, returns a numeric value representing the calculated metric or a data frame/tibble with detailed diagnostics including confidence intervals and possibly other metrics relevant to understanding the metric.

See Also

dx_cm() to understand how to create and interact with a 'dx_cm' object.

Examples

cm <- dx_cm(dx_heart_failure$predicted, dx_heart_failure$truth,
  threshold =
    0.5, poslabel = 1
)
simple_npv <- dx_npv(cm, detail = "simple")
detailed_npv <- dx_npv(cm)
print(simple_npv)
print(detailed_npv)

overdodactyl/diagnosticSummary documentation built on Jan. 28, 2024, 10:07 a.m.