Struct statrs::distribution::StudentsT

pub struct StudentsT { /* private fields */ }

Implements the Student’s T distribution

Examples

use statrs::distribution::{StudentsT, Continuous};
use statrs::statistics::Distribution;
use statrs::prec;

let n = StudentsT::new(0.0, 1.0, 2.0).unwrap();
assert_eq!(n.mean().unwrap(), 0.0);
assert!(prec::almost_eq(n.pdf(0.0), 0.353553390593274, 1e-15));

Implementations

impl StudentsT

pub fn new(location: f64, scale: f64, freedom: f64) -> Result<StudentsT>

Constructs a new student’s t-distribution with location location, scale scale, and freedom freedom.

Errors

Returns an error if any of location, scale, or freedom are NaN. Returns an error if scale <= 0.0 or freedom <= 0.0

Examples

use statrs::distribution::StudentsT;

let mut result = StudentsT::new(0.0, 1.0, 2.0);
assert!(result.is_ok());

result = StudentsT::new(0.0, 0.0, 0.0);
assert!(result.is_err());

pub fn location(&self) -> f64

Returns the location of the student’s t-distribution

Examples

use statrs::distribution::StudentsT;

let n = StudentsT::new(0.0, 1.0, 2.0).unwrap();
assert_eq!(n.location(), 0.0);

pub fn scale(&self) -> f64

Returns the scale of the student’s t-distribution

Examples

use statrs::distribution::StudentsT;

let n = StudentsT::new(0.0, 1.0, 2.0).unwrap();
assert_eq!(n.scale(), 1.0);

pub fn freedom(&self) -> f64

Returns the freedom of the student’s t-distribution

Examples

use statrs::distribution::StudentsT;

let n = StudentsT::new(0.0, 1.0, 2.0).unwrap();
assert_eq!(n.freedom(), 2.0);

Trait Implementations

impl Clone for StudentsT

fn clone(&self) -> StudentsT

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Continuous<f64, f64> for StudentsT

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

Calculates the probability density function for the student’s t-distribution at x

Formula

Γ((v + 1) / 2) / (sqrt(vπ) * Γ(v / 2) * σ) * (1 + k^2 / v)^(-1 / 2 * (v
+ 1))

where k = (x - μ) / σ, μ is the location, σ is the scale, v is the freedom, and Γ is the gamma function

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

Calculates the log probability density function for the student’s t-distribution at x

Formula

ln(Γ((v + 1) / 2) / (sqrt(vπ) * Γ(v / 2) * σ) * (1 + k^2 / v)^(-1 / 2 *
(v + 1)))

where k = (x - μ) / σ, μ is the location, σ is the scale, v is the freedom, and Γ is the gamma function

impl ContinuousCDF<f64, f64> for StudentsT

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

Calculates the cumulative distribution function for the student’s t-distribution at x

Formula

if x < μ {
    (1 / 2) * I(t, v / 2, 1 / 2)
} else {
    1 - (1 / 2) * I(t, v / 2, 1 / 2)
}

where t = v / (v + k^2), k = (x - μ) / σ, μ is the location, σ is the scale, v is the freedom, and I is the regularized incomplete beta function

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

Calculates the cumulative distribution function for the student’s t-distribution at x

Formula

if x < μ {
    1 - (1 / 2) * I(t, v / 2, 1 / 2)
} else {
    (1 / 2) * I(t, v / 2, 1 / 2)
}

where t = v / (v + k^2), k = (x - μ) / σ, μ is the location, σ is the scale, v is the freedom, and I is the regularized incomplete beta function

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

Calculates the inverse cumulative distribution function for the Student’s T-distribution at x

impl Debug for StudentsT

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

Formats the value using the given formatter.

impl Distribution<f64> for StudentsT

fn sample<R: Rng + ?Sized>(&self, r: &mut R) -> f64

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 Distribution<f64> for StudentsT

fn mean(&self) -> Option<f64>

Returns the mean of the student’s t-distribution

None

If freedom <= 1.0

Formula

μ

where μ is the location

fn variance(&self) -> Option<f64>

Returns the variance of the student’s t-distribution

None

If freedom <= 2.0

Formula

if v == f64::INFINITY {
    Some(σ^2)
} else if freedom > 2.0 {
    Some(v * σ^2 / (v - 2))
} else {
    None
}

where σ is the scale and v is the freedom

fn entropy(&self) -> Option<f64>

Returns the entropy for the student’s t-distribution

Formula

- ln(σ) + (v + 1) / 2 * (ψ((v + 1) / 2) - ψ(v / 2)) + ln(sqrt(v) * B(v / 2, 1 /
2))

where σ is the scale, v is the freedom, ψ is the digamma function, and B is the beta function

fn skewness(&self) -> Option<f64>

Returns the skewness of the student’s t-distribution

None

If x <= 3.0

Formula

0

fn std_dev(&self) -> Option<T>

Returns the standard deviation, if it exists.

impl Max<f64> for StudentsT

fn max(&self) -> f64

Returns the maximum value in the domain of the student’s t-distribution representable by a double precision float

Formula

f64::INFINITY

impl Median<f64> for StudentsT

fn median(&self) -> f64

Returns the median of the student’s t-distribution

Formula

μ

where μ is the location

impl Min<f64> for StudentsT

fn min(&self) -> f64

Returns the minimum value in the domain of the student’s t-distribution representable by a double precision float

Formula

f64::NEG_INFINITY

impl Mode<Option<f64>> for StudentsT

fn mode(&self) -> Option<f64>

Returns the mode of the student’s t-distribution

Formula

μ

where μ is the location

impl PartialEq for StudentsT

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

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

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

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

impl Copy for StudentsT

impl StructuralPartialEq for StudentsT