NB: As probably obvious from the 0.0.x version number, this crate is not currently in a finished state and any commit can possibly introduce breaking changes. At this point, I am making no particular efforts to preserve backward compatibility. Therefore, please use at your own risks at least until version 0.1 or above.
Stats-ci provides some basic functions to compute confidence intervals of sample data. This includes the following: * confidence intervals around the mean for numerical data, * confidence intervals around a quantile (e.g., median) for arbitrary ordered data, * confidence intervals for proportions.
Not included yet but planned are: * confidence intervals for difference of sample data. * confidence intervals for regression parameters.
The motivation behind creating this crate came both from the recurring need of confidence intervals in personal projects and also out of frustration from having to look up the formulas each time. I reckoned that I might not be alone in this situation and that such a crate could prove useful to some.
I am far from being a statistician and I will gladly welcome any advice or corrections. I only made a feeble attempt at numerical statibility (e.g., kahan sum, log-sum-exp). In any case, please be circumspect about the results obtained from this crate.
Add the most recent release to your Cargo.toml
Rust
[dependencies]
stats-ci = "0.0.1"
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 that implements the Float trait from the crate num-traits.
The crate provides three functions to compute confidence intervals for the mean of floating-point data:
* mean::Arithmetic::ci computes the confidence interval for the arithmetic mean.
* mean::Geometric::ci computes the confidence interval for the geometric mean
* mean::Harmonic::ci computes the confidence interval for the harmonic mean
```rust use statsci::*; 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::newtwo_sided(0.95); let ci = mean::Arithmetic::ci(confidence, data)?; // mean: 53.67 // stddev: 28.097613040716798 println!("ci: {}", ci); // ci: [48.09482399055084, 59.24517600944916] println!("low: {:?}", ci.low()); // low: Some(48.09482399055084) println!("high: {:?}", ci.high()); // high: Some(59.24517600944916)
let ci = Harmonic::ci(confidence, data)?; // harmonic mean: 30.031313156339586 println!("ci: {}", ci); // ci: [23.614092539460778, 41.23786064976718]
let ci = Geometric::ci(confidence, data)?; // geometric mean: 43.7268032829256 println!("ci: {}", ci); // ci: [37.731050052007845, 50.67532768656474] ```
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 make sense to compute an interval around the median of the data, but the mean cannot be computed.
```rust use stats_ci::*;
let quantile = 0.5; // median
let data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15];
let confidence = Confidence::newtwosided(0.95); let ci = quantile::ci(confidence, &data, quantile)?; assert_eq!(ci, Interval::new(4, 12));
let confidence = Confidence::newtwosided(0.8); let ci = quantile::ci(confidence, &data, quantile)?; assert_eq!(ci, Interval::new(6, 10));
let data = ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O"]; let confidence = Confidence::newtwosided(0.95); let ci = quantile::ci(confidence, &data, quantile)?; assert_eq!(interval3, Interval::new("D", "L")); ```
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.
```rust use statsci::*; let confidence = Confidence::newtwo_sided(0.95);
let data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]; let ci = proportion::ci_if(confidence, &data, |&x| x <= 10)?; println!("ci: {}", ci); // ci: [0.2992980081982124, 0.7007019918017876] assert!(ci.contains(0.5));
let population = 500; let successes = 421; let ci = proportion::ci(confidence, population, successes)?; println!("ci: {}", ci); // ci: [0.8074376489887337, 0.8713473021355645] assert!(ci.contains(0.842)); ```
I will gladly and carefully consider any constructive comments that you have to offer. In particular, I will be considering constructive feedback both on the interface and the calculations with the following priorities correctness, code readability, genericity, efficiency.
Currently, the following are on my TODO list:
unwrap() and reduce panicking codeLicensed under either of
at your option.
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.