dx_prevalence: Calculate Prevalence

In overdodactyl/diagnosticSummary: Diagnostic Summaries

Calculate Prevalence

Description

Calculates Prevalence, which is the proportion of cases that are positive for the condition of interest over the total number of cases. Prevalence provides a measure of how widespread a condition is within the population at a given time.

Usage

dx_prevalence(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

Prevalence is a measure of the burden of a condition or disease in a population. It is an important measure in epidemiology and health service planning as it helps to understand the level of disease in a population at a given time. Unlike other metrics that are based on the classifier's performance, prevalence is a measure of the actual condition being tested.

The formula for Prevalence is:

Prevalence = \frac{Number of Current Cases (Positives)}{Total Number of Cases}

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_prevalence <- dx_prevalence(cm, detail = "simple")
detailed_prevalence <- dx_prevalence(cm)
print(simple_prevalence)
print(detailed_prevalence)

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