Statistical metrics and confidence intervals for classes

The function CI.classNRI calculates the NRI statistics for reclassification of data already in classes with confidence intervals. Uses statistics.classNRI.

Usage

CI.classNRI(
  c1,
  c2,
  y,
  s1 = NULL,
  s2 = NULL,
  conf.level = 0.95,
  n.boot = 1000,
  dp = 3
)

Arguments

c1: Risk classes of the baseline model (ordinal)
c2: Risk classes of new model
y: Binary of outcome of interest. Must be 0 or 1.
s1: The savings or benefit when am event is reclassified to a higher group by the new model (positive numeric)
s2: The benefit when a non-event is reclassified to a lower group (positive numeric)
conf.level: The confidence interval expressed as a fraction of 1 (ie 0.95 is the 95% confidence interval )
n.boot: The number of "bootstraps" to use. Performance slows down with more bootstraps. For trialling result, use a low number (eg 2), for accuracy use a large number (eg 2000)
dp: The number of decimal places to display

Value

A list with the following elements:

meta_data: Some overall meta data - Confidence Interval, number of bootstraps, s1, s2
Metrics: Point estimates of the statistical metrics.
Each_bootstrap_metrics: Point estimates of the statistical metrics for each bootstrapped sample.
Summary_metrics: Point estimates with confidence intervals of the statistical metrics (e.g. Total, Events, Non-events, Prevalence, NRI, IDI, confusion matrices).

A matrix of metrics