Struct rv::dist::KsTwoAsymptotic

pub struct KsTwoAsymptotic {}

Kolmogorov-Smirnov distribution where the number of samples, $N$, is assumed to be large This is the distribution of $\sqrt{N} D_n$ where $D_n = \sup_x |F_n(x) - F(x)|$ where $F$ is the true CDF and $F_n$ the emperical CDF.

Example

Calculate the Survival Function for a particular KS stat.

use rv::traits::*;
use rv::dist::KsTwoAsymptotic;

let ks = KsTwoAsymptotic::default();
let sf = ks.sf(&1.0);
const EXPECTED: f64 = 0.26999967167735456;
assert!((sf - EXPECTED).abs() < 1E-15);

Implementations

impl KsTwoAsymptotic

pub fn new() -> Self

Create a new KsTwoAsymptotic distribution

Trait Implementations

impl Cdf<f32> for KsTwoAsymptotic

fn cdf(&self, x: &f32) -> f64

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Cdf<f64> for KsTwoAsymptotic

fn cdf(&self, x: &f64) -> f64

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Clone for KsTwoAsymptotic

fn clone(&self) -> KsTwoAsymptotic

Returns a copy of the value.

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

Performs copy-assignment from source.

impl ContinuousDistr<f32> for KsTwoAsymptotic

fn pdf(&self, x: &X) -> f64

The value of the Probability Density Function (PDF) at x

fn ln_pdf(&self, x: &X) -> f64

The value of the log Probability Density Function (PDF) at x

impl ContinuousDistr<f64> for KsTwoAsymptotic

fn pdf(&self, x: &X) -> f64

The value of the Probability Density Function (PDF) at x

fn ln_pdf(&self, x: &X) -> f64

The value of the log Probability Density Function (PDF) at x

impl Copy for KsTwoAsymptotic

impl Debug for KsTwoAsymptotic

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

Formats the value using the given formatter.

impl Default for KsTwoAsymptotic

fn default() -> KsTwoAsymptotic

Returns the “default value” for a type.

impl Display for KsTwoAsymptotic

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

Formats the value using the given formatter.

impl InverseCdf<f32> for KsTwoAsymptotic

fn invcdf(&self, p: f64) -> f32

The value of the x at the given probability in the CDF

fn quantile(&self, p: f64) -> X

Alias for invcdf

fn interval(&self, p: f64) -> (X, X)

Interval containing p proportion for the probability

impl InverseCdf<f64> for KsTwoAsymptotic

fn invcdf(&self, p: f64) -> f64

The value of the x at the given probability in the CDF

fn quantile(&self, p: f64) -> X

Alias for invcdf

fn interval(&self, p: f64) -> (X, X)

Interval containing p proportion for the probability

impl PartialEq<KsTwoAsymptotic> for KsTwoAsymptotic

fn eq(&self, other: &KsTwoAsymptotic) -> bool

This method tests for self and other values to be equal, and is used by ==.

#[must_use]

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

This method tests for !=.

impl Rv<f32> for KsTwoAsymptotic

fn ln_f(&self, x: &f32) -> f64

Probability function

fn draw<R: Rng>(&self, rng: &mut R) -> f32

Single draw from the Rv

fn f(&self, x: &X) -> f64

Probability function

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>

Multiple draws of the Rv

fn sample_stream<'r, R: Rng>(
    &'r self,
    rng: &'r mut R
) -> Box<dyn Iterator<Item = X> + 'r>

Create a never-ending iterator of samples

impl Rv<f64> for KsTwoAsymptotic

fn ln_f(&self, x: &f64) -> f64

Probability function

fn draw<R: Rng>(&self, rng: &mut R) -> f64

Single draw from the Rv

fn f(&self, x: &X) -> f64

Probability function

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>

Multiple draws of the Rv

fn sample_stream<'r, R: Rng>(
    &'r self,
    rng: &'r mut R
) -> Box<dyn Iterator<Item = X> + 'r>

Create a never-ending iterator of samples

impl StructuralPartialEq for KsTwoAsymptotic

impl Support<f32> for KsTwoAsymptotic

fn supports(&self, x: &f32) -> bool

Returns true if x is in the support of the Rv

impl Support<f64> for KsTwoAsymptotic

fn supports(&self, x: &f64) -> bool

Returns true if x is in the support of the Rv