Trait statrs::statistics::Statistics[−][src]

pub trait Statistics<T> {
    fn min(self) -> T;

    fn max(self) -> T;

    fn abs_min(self) -> T;

    fn abs_max(self) -> T;

    fn mean(self) -> T;

    fn geometric_mean(self) -> T;

    fn harmonic_mean(self) -> T;

    fn variance(self) -> T;

    fn std_dev(self) -> T;

    fn population_variance(self) -> T;

    fn population_std_dev(self) -> T;

    fn covariance(self, other: Self) -> T;

    fn population_covariance(self, other: Self) -> T;

    fn quadratic_mean(self) -> T;
}

The Statistics trait provides a host of statistical utilities for analzying data sets

Required methods

fn min(self) -> T[src]

Returns the minimum value in the data

Remarks

Returns f64::NAN if data is empty or an entry is f64::NAN

Examples

use std::f64;
use statrs::statistics::Statistics;

let x: [f64; 0] = [];
assert!(x.min().is_nan());

let y = [0.0, f64::NAN, 3.0, -2.0];
assert!(y.min().is_nan());

let z = [0.0, 3.0, -2.0];
assert_eq!(z.min(), -2.0);

fn max(self) -> T[src]

Returns the maximum value in the data

Remarks

Returns f64::NAN if data is empty or an entry is f64::NAN

Examples

use std::f64;
use statrs::statistics::Max;

let x: [f64; 0] = [];
assert!(x.max().is_nan());

let y = [0.0, f64::NAN, 3.0, -2.0];
assert!(y.max().is_nan());

let z = [0.0, 3.0, -2.0];
assert_eq!(z.max(), 3.0);

fn abs_min(self) -> T[src]

Returns the minimum absolute value in the data

Remarks

Returns f64::NAN if data is empty or an entry is f64::NAN

Examples

use std::f64;
use statrs::statistics::Statistics;

let x = [];
assert!(x.abs_min().is_nan());

let y = [0.0, f64::NAN, 3.0, -2.0];
assert!(y.abs_min().is_nan());

let z = [0.0, 3.0, -2.0];
assert_eq!(z.abs_min(), 0.0);

fn abs_max(self) -> T[src]

Returns the maximum absolute value in the data

Remarks

Returns f64::NAN if data is empty or an entry is f64::NAN

Examples

use std::f64;
use statrs::statistics::Statistics;

let x = [];
assert!(x.abs_max().is_nan());

let y = [0.0, f64::NAN, 3.0, -2.0];
assert!(y.abs_max().is_nan());

let z = [0.0, 3.0, -2.0, -8.0];
assert_eq!(z.abs_max(), 8.0);

fn mean(self) -> T[src]

Evaluates the sample mean, an estimate of the population mean.

Remarks

Returns f64::NAN if data is empty or an entry is f64::NAN

Examples

#[macro_use]
extern crate statrs;

use std::f64;
use statrs::statistics::Mean;

let x = [];
assert!(x.mean().is_nan());

let y = [0.0, f64::NAN, 3.0, -2.0];
assert!(y.mean().is_nan());

let z = [0.0, 3.0, -2.0];
assert_almost_eq!(z.mean(), 1.0 / 3.0, 1e-15);

fn geometric_mean(self) -> T[src]

Evaluates the geometric mean of the data

Remarks

Returns f64::NAN if data is empty or an entry is f64::NAN. Returns f64::NAN if an entry is less than 0. Returns 0 if no entry is less than 0 but there are entries equal to 0.

Examples

#[macro_use]
extern crate statrs;

use std::f64;
use statrs::statistics::Statistics;

let x = [];
assert!(x.geometric_mean().is_nan());

let y = [0.0, f64::NAN, 3.0, -2.0];
assert!(y.geometric_mean().is_nan());

let mut z = [0.0, 3.0, -2.0];
assert!(z.geometric_mean().is_nan());

z = [0.0, 3.0, 2.0];
assert_eq!(z.geometric_mean(), 0.0);

z = [1.0, 2.0, 3.0];
// test value from online calculator, could be more accurate
assert_almost_eq!(z.geometric_mean(), 1.81712, 1e-5);

fn harmonic_mean(self) -> T[src]

Evaluates the harmonic mean of the data

Remarks

Returns f64::NAN if data is empty or an entry is f64::NAN, or if any value in data is less than 0. Returns 0 if there are no values less than 0 but there exists values equal to 0.

Examples

#[macro_use]
extern crate statrs;

use std::f64;
use statrs::statistics::Statistics;

let x = [];
assert!(x.harmonic_mean().is_nan());

let y = [0.0, f64::NAN, 3.0, -2.0];
assert!(y.harmonic_mean().is_nan());

let mut z = [0.0, 3.0, -2.0];
assert!(z.harmonic_mean().is_nan());

z = [0.0, 3.0, 2.0];
assert_eq!(z.harmonic_mean(), 0.0);

z = [1.0, 2.0, 3.0];
// test value from online calculator, could be more accurate
assert_almost_eq!(z.harmonic_mean(), 1.63636, 1e-5);

fn variance(self) -> T[src]

Estimates the unbiased population variance from the provided samples

Remarks

On a dataset of size N, N-1 is used as a normalizer (Bessel’s correction).

Returns f64::NAN if data has less than two entries or if any entry is f64::NAN

Examples

use std::f64;
use statrs::statistics::Variance;

let x = [];
assert!(x.variance().is_nan());

let y = [0.0, f64::NAN, 3.0, -2.0];
assert!(y.variance().is_nan());

let z = [0.0, 3.0, -2.0];
assert_eq!(z.variance(), 19.0 / 3.0);

fn std_dev(self) -> T[src]

Estimates the unbiased population standard deviation from the provided samples

Remarks

On a dataset of size N, N-1 is used as a normalizer (Bessel’s correction).

Returns f64::NAN if data has less than two entries or if any entry is f64::NAN

Examples

use std::f64;
use statrs::statistics::Statistics;

let x = [];
assert!(x.std_dev().is_nan());

let y = [0.0, f64::NAN, 3.0, -2.0];
assert!(y.std_dev().is_nan());

let z = [0.0, 3.0, -2.0];
assert_eq!(z.std_dev(), (19f64 / 3.0).sqrt());

fn population_variance(self) -> T[src]

Evaluates the population variance from a full population.

Remarks

On a dataset of size N, N is used as a normalizer and would thus be biased if applied to a subset

Returns f64::NAN if data is empty or an entry is f64::NAN

Examples

use std::f64;
use statrs::statistics::Statistics;

let x = [];
assert!(x.population_variance().is_nan());

let y = [0.0, f64::NAN, 3.0, -2.0];
assert!(y.population_variance().is_nan());

let z = [0.0, 3.0, -2.0];
assert_eq!(z.population_variance(), 38.0 / 9.0);

fn population_std_dev(self) -> T[src]

Evaluates the population standard deviation from a full population.

Remarks

On a dataset of size N, N is used as a normalizer and would thus be biased if applied to a subset

Returns f64::NAN if data is empty or an entry is f64::NAN

Examples

use std::f64;
use statrs::statistics::Statistics;

let x = [];
assert!(x.population_std_dev().is_nan());

let y = [0.0, f64::NAN, 3.0, -2.0];
assert!(y.population_std_dev().is_nan());

let z = [0.0, 3.0, -2.0];
assert_eq!(z.population_std_dev(), (38f64 / 9.0).sqrt());

fn covariance(self, other: Self) -> T[src]

Estimates the unbiased population covariance between the two provided samples

Remarks

On a dataset of size N, N-1 is used as a normalizer (Bessel’s correction).

Returns f64::NAN if data has less than two entries or if any entry is f64::NAN

Panics

If the two sample containers do not contain the same number of elements

Examples

#[macro_use]
extern crate statrs;

use std::f64;
use statrs::statistics::Statistics;

let x = [];
assert!(x.covariance(&[]).is_nan());

let y1 = [0.0, f64::NAN, 3.0, -2.0];
let y2 = [-5.0, 4.0, 10.0, f64::NAN];
assert!(y1.covariance(&y2).is_nan());

let z1 = [0.0, 3.0, -2.0];
let z2 = [-5.0, 4.0, 10.0];
assert_almost_eq!(z1.covariance(&z2), -5.5, 1e-14);

fn population_covariance(self, other: Self) -> T[src]

Evaluates the population covariance between the two provider populations

Remarks

On a dataset of size N, N is used as a normalizer and would thus be biased if applied to a subset

Returns f64::NAN if data is empty or any entry is f64::NAN

Panics

If the two sample containers do not contain the same number of elements

Examples

#[macro_use]
extern crate statrs;

use std::f64;
use statrs::statistics::Statistics;

let x = [];
assert!(x.population_covariance(&[]).is_nan());

let y1 = [0.0, f64::NAN, 3.0, -2.0];
let y2 = [-5.0, 4.0, 10.0, f64::NAN];
assert!(y1.population_covariance(&y2).is_nan());

let z1 = [0.0, 3.0, -2.0];
let z2 = [-5.0, 4.0, 10.0];
assert_almost_eq!(z1.population_covariance(&z2), -11.0 / 3.0, 1e-14);

fn quadratic_mean(self) -> T[src]

Estimates the quadratic mean (Root Mean Square) of the data

Remarks

Returns f64::NAN if data is empty or any entry is f64::NAN

Examples

#[macro_use]
extern crate statrs;

use std::f64;
use statrs::statistics::Statistics;

let x = [];
assert!(x.quadratic_mean().is_nan());

let y = [0.0, f64::NAN, 3.0, -2.0];
assert!(y.quadratic_mean().is_nan());

let z = [0.0, 3.0, -2.0];
// test value from online calculator, could be more accurate
assert_almost_eq!(z.quadratic_mean(), 2.08167, 1e-5);
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Implementors

impl<T> Statistics<f64> for T where
    T: IntoIterator,
    T::Item: Borrow<f64>, [src]

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