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Calculate Positive Predictive Value (PPV) at Target Prevalence

Source: R/dx_metrics.R
dx_ppv_prevalence.Rd

Computes the Positive Predictive Value (PPV) 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_ppv_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 PPV 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.

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.

Details

This function calculates the PPV using the formula: $$PPV = \frac{Sensitivity \times Prevalence}{(Sensitivity \times Prevalence) + (1 - Specificity) \times (1 - Prevalence)}$$ where Sensitivity is the true positive rate and Specificity is the true negative rate. Adjusting PPV for prevalence allows a more accurate assessment of the classifier’s performance in different population settings.

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

See also

dx_cm(), dx_ppv() for the non-prevalence adjusted PPV.

Examples

cm <- dx_cm(dx_heart_failure$predicted, dx_heart_failure$truth,
  threshold = 0.5, poslabel = 1
)
# Calculate PPV at a prevalence of 0.1
dx_ppv_prevalence(cm, prevalence = 0.1)
#> # A tibble: 1 × 8
#>   measure           summary estimate conf_low conf_high fraction conf_type notes
#>   <chr>             <chr>      <dbl>    <dbl>     <dbl> <chr>    <chr>     <chr>
#> 1 Positive Predict… 49.2% …    0.492    0.361     0.623 ""       Simple L… Prev…