ppv: Calculate Positive Predictive Value (PPV, Precision)

In overdodactyl/diagnosticSummary: Diagnostic Summaries

Calculate Positive Predictive Value (PPV, Precision)

Description

Calculates the proportion of true positives out of the total predicted positives (true positives + false positives). PPV is also known as precision.Note that PPV can be influenced by the prevalence of the condition and should be used alongside other metrics.

Usage

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

dx_precision(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

PPV, also known as precision, is the ratio of true positives to the sum of true and false positives. It reflects the classifier's ability to identify only relevant instances. However, like accuracy, it may not be suitable for unbalanced datasets. For detailed diagnostics, including confidence intervals, specify detail = "full".

The formula for PPV is:

PPV = \frac{True Positives}{True Positives + False Positives}

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_ppv <- dx_ppv(cm, detail = "simple")
detailed_ppv <- dx_ppv(cm)
print(simple_ppv)
print(detailed_ppv)

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