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.
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
citypefor 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
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: $$PPV = \frac{True Positives}{True Positives + False Positives}$$
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)
#> [1] 0.8395062
print(detailed_ppv)
#> # 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… 84.0% … 0.840 0.741 0.912 68/81 Binomial… ""