Struct FisherF

pub struct FisherF<F>
where
    F: Float,
    StandardNormal: Distribution<F>,
    Exp1: Distribution<F>,
    Open01: Distribution<F>,
{ /* private fields */ }

The Fisher F-distribution F(m, n).

This distribution is equivalent to the ratio of two normalised chi-squared distributions, that is, F(m,n) = (χ²(m)/m) / (χ²(n)/n).

Plot

The plot shows the F-distribution with various values of m and n.

Example

use rand_distr::{FisherF, Distribution};

let f = FisherF::new(2.0, 32.0).unwrap();
let v = f.sample(&mut rand::rng());
println!("{} is from an F(2, 32) distribution", v)

Implementations

impl<F> FisherF<F>

where F: Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

pub fn new(m: F, n: F) -> Result<FisherF<F>, Error>

Create a new FisherF distribution, with the given parameter.

Trait Implementations

impl<F> Clone for FisherF<F>

where F: Float + Clone, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

fn clone(&self) -> FisherF<F>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<F> Debug for FisherF<F>

where F: Float + Debug, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de, F> Deserialize<'de> for FisherF<F>

where F: Float + Deserialize<'de>, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<F> Distribution<F> for FisherF<F>

where F: Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> F

Generate a random value of T, using rng as the source of randomness.

fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>

where R: Rng, Self: Sized,

Create an iterator that generates random values of T, using rng as the source of randomness.

fn map<F, S>(self, func: F) -> DistMap<Self, F, T, S>

where F: Fn(T) -> S, Self: Sized,

Create a distribution of values of ‘S’ by mapping the output of Self through the closure F

impl<F> PartialEq for FisherF<F>

where F: Float + PartialEq, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

fn eq(&self, other: &FisherF<F>) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<F> Serialize for FisherF<F>

where F: Float + Serialize, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl<F> Copy for FisherF<F>

where F: Float + Copy, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

impl<F> StructuralPartialEq for FisherF<F>

where F: Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,