Crate stats_ci

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Expand description

This crate aims to provide convenient functions to compute confidence intervals in various situations. The motivation comes from a personal need and was that no crate seem to provide an easy and comprehensive solution to computing such intervals. One exception is the crate criterion which computes confidence intervals for its measurements but does not export such functionality.

This crate exports a type Confidence to express a confidence level and a type Interval to represent a confidence interval. Intervals are generic and can be instantiated for various types, beyond the usual float or integer types.

The crate provides functions to compute various kinds of confidence intervals:

  • intervals over the mean of floating-point data
  • intervals over quantiles (incl. median) of sample data of any ordered types
  • intervals on proportions on Boolean data (arrays or iterators).

Examples

C.I. for the Mean

The crate provides functions to compute confidence intervals for the mean of floating-point (f32 or f64) data. The functions are generic and can be used with any type implementing the num_traits::Float trait from the crate num_traits. When dealing with integer data it is necessary to convert it to floating-point values.

The crate provides three functions to compute confidence intervals for the mean of floating-point data:

The functionality is mainly provided by the trait StatisticsOps (preferred) or the trait MeanCI (legacy) on the above structs.

use stats_ci::*;

let data = [
    82., 94., 68., 6., 39., 80., 10., 97., 34., 66., 62., 7., 39., 68., 93., 64., 10., 74.,
    15., 34., 4., 48., 88., 94., 17., 99., 81., 37., 68., 66., 40., 23., 67., 72., 63.,
    71., 18., 51., 65., 87., 12., 44., 89., 67., 28., 86., 62., 22., 90., 18., 50., 25.,
    98., 24., 61., 62., 86., 100., 96., 27., 36., 82., 90., 55., 26., 38., 97., 73., 16.,
    49., 23., 26., 55., 26., 3., 23., 47., 27., 58., 27., 97., 32., 29., 56., 28., 23.,
    37., 72., 62., 77., 63., 100., 40., 84., 77., 39., 71., 61., 17., 77.,
];
let confidence = Confidence::new_two_sided(0.95);
let ci = mean::Arithmetic::ci(confidence, data)?;
// mean: 53.67
// stddev: 28.097613040716798
// reference values computed in python/numpy
// [48.094823990767836, 59.24517600923217]

use num_traits::Float;
use assert_approx_eq::assert_approx_eq;
assert_approx_eq!(ci.low_f(), 48.094823990767836, 1e-3);
assert_approx_eq!(ci.high_f(), 59.24517600923217, 1e-3);

It is also possible to compute the confidence intervals incrementally by using the mean::Arithmetic::new constructor or the mean::Arithmetic::from_iter.

C.I. for Quantiles

Depending on the type of data and measurements, it is sometimes inappropriate to compute the mean of the data because that value makes little sense. For instance, consider a communication system and suppose that we want to test if at least 95% of messages are delivered within 1 second with 90% confidence. Then, the value of interest is the one-sided confidence interval of the 95th percentile (quantile=.95, condidence level=0.9).

In a different context, if the data is an ordered sequence of strings, it could (in some context) make sense to compute an interval around the median of the data, but the mean cannot be computed.

use stats_ci::*;

let data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15];
let confidence = Confidence::new_two_sided(0.95);
let quantile = 0.5; // median
let interval = quantile::ci(confidence, &data, quantile)?;
assert_eq!(interval, Interval::new(5, 12)?);

let confidence = Confidence::new_two_sided(0.8);
let interval2 = quantile::ci(confidence, &data, quantile)?;
assert_eq!(interval2, Interval::new(6, 11)?);

let data = ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O"];
let confidence = Confidence::new_two_sided(0.95);
let quantile = 0.5; // median
let interval3 = quantile::ci(confidence, &data, quantile)?;
assert_eq!(interval3, Interval::new("E", "L")?);

C.I. for Proportions

Confidence intervals for proportions are often used in the context of A/B testing or when measuring the success/failure rate of a system. It is also useful when running Monte-Carlo simulations to estimate the winning chances of a player in a game.

This crate uses the Wilson score interval to compute the confidence interval for a proportion, which is more stable than the standard normal approximation but results in slightly more conservative intervals.

use stats_ci::*;

let data = [
    true, false, true, true, false, true, true, false, true, true,
    false, false, false, true, false, true, false, false, true, false
];
let confidence = Confidence::new_two_sided(0.95);
let interval = proportion::ci_true(confidence, data)?;
assert_approx_eq!(interval.low().unwrap(), 0.299, 1e-2);
assert_approx_eq!(interval.high().unwrap(), 0.701, 1e-2);

let data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20];
let confidence = Confidence::new_two_sided(0.95);
let interval = proportion::ci_if(confidence, &data, |&x| x <= 10)?;
assert_approx_eq!(interval.low().unwrap(), 0.299, 1e-2);
assert_approx_eq!(interval.high().unwrap(), 0.701, 1e-2);

let population = 500;
let successes = 421;
let confidence = Confidence::new_two_sided(0.95);
let interval = proportion::ci(confidence, population, successes)?;
assert_approx_eq!(interval.low().unwrap(), 0.81, 1e-2);
assert_approx_eq!(interval.high().unwrap(), 0.87, 1e-2);

References

Re-exports

Modules

  • comparison
    Comparison of two samples
  • error
  • mean
    Confidence intervals over the mean (arithmetic, geometric, harmonic) of a given sample.
  • proportion
    Confidence intervals for proportions
  • quantile
    Confidence intervals for quantiles

Enums

  • Confidence
    Confidence level of a confidence interval.
  • Interval
    Interval over a partially ordered type (NB: floating point numbers are partially ordered because of NaN). The interval is defined by its lower and upper bounds. One-sided intervals (with a single concrete bound) are also supported.