dx_npv_prevalence: Calculate Negative Predictive Value (NPV) at Target...

In overdodactyl/diagnosticSummary: Diagnostic Summaries

Calculate Negative Predictive Value (NPV) at Target Prevalence

Description

Computes the Negative Predictive Value (NPV) adjusted to a specified prevalence level. This function is useful for understanding classifier performance in settings where the actual prevalence of the condition may differ from that in the initial data.

Usage

dx_npv_prevalence(cm, prevalence, detail = "full", ...)

Arguments

cm	A dx_cm object created by dx_cm().
prevalence	Numeric value between 0 and 1, representing the target prevalence for adjusting the NPV calculation.
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

This function calculates the NPV using the formula:

NPV = \frac{Specificity \times (1 - Prevalence)}{(1 - Sensitivity) \times Prevalence + Specificity \times (1 - Prevalence)}

where Specificity is the true negative rate and Sensitivity is the true positive rate. Adjusting NPV for prevalence allows better estimation of the classifier’s performance in different population settings.

Confidence intervales are calucated using a simple logit (Mercaldo et al, 2007)

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(), dx_npv() for the non-prevalence adjusted NPV.

Examples

cm <- dx_cm(dx_heart_failure$predicted, dx_heart_failure$truth,
  threshold = 0.5, poslabel = 1
)
# Calculate NPV at a prevalence of 0.1
dx_npv_prevalence(cm, prevalence = 0.1)

overdodactyl/diagnosticSummary documentation built on Jan. 29, 2025, 11:11 a.m.