Trait statrs::statistics::OrderStatistics

pub trait OrderStatistics<T> {
    // Required methods
    fn order_statistic(&mut self, order: usize) -> T;
    fn median(&mut self) -> T;
    fn quantile(&mut self, tau: f64) -> T;
    fn percentile(&mut self, p: usize) -> T;
    fn lower_quartile(&mut self) -> T;
    fn upper_quartile(&mut self) -> T;
    fn interquartile_range(&mut self) -> T;
    fn ranks(&mut self, tie_breaker: RankTieBreaker) -> Vec<T>;
}

The OrderStatistics trait provides statistical utilities having to do with ordering. All the algorithms are in-place thus requiring a mutable borrow.

Required Methods

fn order_statistic(&mut self, order: usize) -> T

Returns the order statistic (order 1..N) from the data

Remarks

No sorting is assumed. Order must be one-based (between 1 and N inclusive) Returns f64::NAN if order is outside the viable range or data is empty.

Examples

use statrs::statistics::OrderStatistics;
use statrs::statistics::Data;

let x = [];
let mut x = Data::new(x);
assert!(x.order_statistic(1).is_nan());

let y = [0.0, 3.0, -2.0];
let mut y = Data::new(y);
assert!(y.order_statistic(0).is_nan());
assert!(y.order_statistic(4).is_nan());
assert_eq!(y.order_statistic(2), 0.0);
assert!(y != Data::new([0.0, 3.0, -2.0]));

fn median(&mut self) -> T

Returns the median value from the data

Remarks

Returns f64::NAN if data is empty

Examples

use statrs::statistics::OrderStatistics;
use statrs::statistics::Data;

let x = [];
let mut x = Data::new(x);
assert!(x.median().is_nan());

let y = [0.0, 3.0, -2.0];
let mut y = Data::new(y);
assert_eq!(y.median(), 0.0);
assert!(y != Data::new([0.0, 3.0, -2.0]));

fn quantile(&mut self, tau: f64) -> T

Estimates the tau-th quantile from the data. The tau-th quantile is the data value where the cumulative distribution function crosses tau.

Remarks

No sorting is assumed. Tau must be between 0 and 1 inclusive. Returns f64::NAN if data is empty or tau is outside the inclusive range.

Examples

use statrs::statistics::OrderStatistics;
use statrs::statistics::Data;

let x = [];
let mut x = Data::new(x);
assert!(x.quantile(0.5).is_nan());

let y = [0.0, 3.0, -2.0];
let mut y = Data::new(y);
assert!(y.quantile(-1.0).is_nan());
assert!(y.quantile(2.0).is_nan());
assert_eq!(y.quantile(0.5), 0.0);
assert!(y != Data::new([0.0, 3.0, -2.0]));

fn percentile(&mut self, p: usize) -> T

Estimates the p-Percentile value from the data.

Remarks

Use quantile for non-integer percentiles. p must be between 0 and 100 inclusive. Returns f64::NAN if data is empty or p is outside the inclusive range.

Examples

use statrs::statistics::OrderStatistics;
use statrs::statistics::Data;

let x = [];
let mut x = Data::new(x);
assert!(x.percentile(0).is_nan());

let y = [1.0, 5.0, 3.0, 4.0, 10.0, 9.0, 6.0, 7.0, 8.0, 2.0];
let mut y = Data::new(y);
assert_eq!(y.percentile(0), 1.0);
assert_eq!(y.percentile(50), 5.5);
assert_eq!(y.percentile(100), 10.0);
assert!(y.percentile(105).is_nan());
assert!(y != Data::new([1.0, 5.0, 3.0, 4.0, 10.0, 9.0, 6.0, 7.0, 8.0, 2.0]));

fn lower_quartile(&mut self) -> T

Estimates the first quartile value from the data.

Remarks

Returns f64::NAN if data is empty

Examples

#[macro_use]
extern crate statrs;

use statrs::statistics::OrderStatistics;
use statrs::statistics::Data;

let x = [];
let mut x = Data::new(x);
assert!(x.lower_quartile().is_nan());

let y = [2.0, 1.0, 3.0, 4.0];
let mut y = Data::new(y);
assert_almost_eq!(y.lower_quartile(), 1.416666666666666, 1e-15);
assert!(y != Data::new([2.0, 1.0, 3.0, 4.0]));

fn upper_quartile(&mut self) -> T

Estimates the third quartile value from the data.

Remarks

Returns f64::NAN if data is empty

Examples

#[macro_use]
extern crate statrs;

use statrs::statistics::OrderStatistics;
use statrs::statistics::Data;

let x = [];
let mut x = Data::new(x);
assert!(x.upper_quartile().is_nan());

let y = [2.0, 1.0, 3.0, 4.0];
let mut y = Data::new(y);
assert_almost_eq!(y.upper_quartile(), 3.5833333333333333, 1e-15);
assert!(y != Data::new([2.0, 1.0, 3.0, 4.0]));

fn interquartile_range(&mut self) -> T

Estimates the inter-quartile range from the data.

Remarks

Returns f64::NAN if data is empty

Examples

#[macro_use]
extern crate statrs;

use statrs::statistics::Data;
use statrs::statistics::OrderStatistics;

let x = [];
let mut x = Data::new(x);
assert!(x.interquartile_range().is_nan());

let y = [2.0, 1.0, 3.0, 4.0];
let mut y = Data::new(y);
assert_almost_eq!(y.interquartile_range(), 2.166666666666667, 1e-15);
assert!(y != Data::new([2.0, 1.0, 3.0, 4.0]));

fn ranks(&mut self, tie_breaker: RankTieBreaker) -> Vec<T>

Evaluates the rank of each entry of the data.

Examples

use statrs::statistics::{OrderStatistics, RankTieBreaker};
use statrs::statistics::Data;

let x = [];
let mut x = Data::new(x);
assert_eq!(x.ranks(RankTieBreaker::Average).len(), 0);

let y = [1.0, 3.0, 2.0, 2.0];
let mut y = Data::new([1.0, 3.0, 2.0, 2.0]);
assert_eq!(y.clone().ranks(RankTieBreaker::Average), [1.0, 4.0,
2.5, 2.5]);
assert_eq!(y.clone().ranks(RankTieBreaker::Min), [1.0, 4.0, 2.0,
2.0]);

Implementors

impl<D: AsMut<[f64]> + AsRef<[f64]>> OrderStatistics<f64> for Data<D>