Struct FDistribution 

pub struct FDistribution {
    pub d1: f64,
    pub d2: f64,
}

F-distribution F(d1, d2).

Examples

use rs_stats::distributions::f_distribution::FDistribution;
use rs_stats::distributions::traits::Distribution;

let f = FDistribution::new(5.0, 10.0).unwrap();
assert!((f.mean() - 10.0 / 8.0).abs() < 1e-10);

Fields

d1: f64

Numerator degrees of freedom d1 > 0

d2: f64

Denominator degrees of freedom d2 > 0

Implementations

impl FDistribution

pub fn new(d1: f64, d2: f64) -> StatsResult<Self>

Creates an F(d1, d2) distribution.

pub fn fit(data: &[f64]) -> StatsResult<Self>

Method-of-moments fitting from data > 0.

Estimates d2 from the mean (requires mean > 1) and d1 from the variance.

Trait Implementations

impl Clone for FDistribution

fn clone(&self) -> FDistribution

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for FDistribution

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

Formats the value using the given formatter.

impl Distribution for FDistribution

fn name(&self) -> &str

Human-readable distribution name, e.g. "Normal".

fn num_params(&self) -> usize

Number of free parameters (used when computing AIC / BIC).

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

Probability density function f(x).

fn logpdf(&self, x: f64) -> StatsResult<f64>

Natural logarithm of the PDF: ln f(x).

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

Cumulative distribution function F(x) = P(X ≤ x).

fn inverse_cdf(&self, p: f64) -> StatsResult<f64>

Quantile (inverse CDF): find x such that F(x) = p.

fn mean(&self) -> f64

Mean (expected value) μ.

fn variance(&self) -> f64

Variance σ².

fn std_dev(&self) -> f64

Standard deviation σ = √(variance).

fn log_likelihood(&self, data: &[f64]) -> StatsResult<f64>

Sum of log-likelihoods: Σ ln f(xᵢ).

fn aic(&self, data: &[f64]) -> StatsResult<f64>

Akaike Information Criterion: AIC = 2k − 2·ln(L̂).

fn bic(&self, data: &[f64]) -> StatsResult<f64>

Bayesian Information Criterion: BIC = k·ln(n) − 2·ln(L̂).

impl Copy for FDistribution