Struct mathru::statistics::test::T

pub struct T<K> { /* fields omitted */ }

T-Test

Fore more information: https://en.wikipedia.org/wiki/Student%27s_t-test

Example

use mathru::{
    self,
    statistics::{
        distrib::{Distribution, Normal},
        test::{Test, T},
    },
};

let rv1 = Normal::new(1.0, 0.5).random_sequence(100);
let rv2 = Normal::new(1.0, 0.5).random_sequence(100);

//Test with sample with identical means
let mut measure: T<f64> = T::test_independence_unequal_variance(&rv1, &rv2);
println!("{}", measure.value());
measure = T::test_independence_equal_variance(&rv1, &rv2);
println!("{}", measure.value());

// Test with different equal mean, but unequal variances
let rv3 = Normal::new(1.0, 1.5).random_sequence(100);
measure = T::test_independence_unequal_variance(&rv1, &rv3);
println!("{}", measure.value());
measure = T::test_independence_equal_variance(&rv1, &rv3);
println!("{}", measure.value());

// When the sample size is not equal anymore
//the equal variance t-statistic is no longer equal to the unequal variance t-statistic:
let rv4 = Normal::new(2.0, 0.5).random_sequence(300);
measure = T::test_independence_unequal_variance(&rv1, &rv4);
println!("{}", measure.value());
measure = T::test_independence_equal_variance(&rv1, &rv4);
println!("{}", measure.value());

//t-Test with different mean, variance and sample size
let rv5 = Normal::new(2.0, 1.0).random_sequence(300);
measure = T::test_independence_unequal_variance(&rv1, &rv5);
println!("{}", measure.value());
measure = T::test_independence_equal_variance(&rv1, &rv5);
println!("{}", measure.value());

Implementations

impl<K> T<K> where
    K: Real + Gamma + Beta + Error + Hypergeometric, 

pub fn one_sample(x: &Vec<K>, mu_0: K) -> T<K>

This is a one-sided test for the null hypothesis that the expected value (mean) of a sample of independent observations a is equal to the given mean.

x: observation

pub fn test_independence_equal_variance(x: &Vec<K>, y: &Vec<K>) -> T<K>

Calculates the T-test for the means of two independent samples of scores

This is a two-sided test for the null hypothesis that two independent samples have identical expected values. It is assumed, that the populations have identical variances.

pub fn test_independence_unequal_variance(x: &Vec<K>, y: &Vec<K>) -> T<K>

Calculates the T-test for the means of two independent samples of scores

This is a two-sided test for the null hypothesis that two independent samples have identical expected values. It is assumed, that the populations have NOT identical variances. It performs the Welch’s t-test

Trait Implementations

impl<K: Clone> Clone for T<K>

pub fn clone(&self) -> T<K>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<K: Copy> Copy for T<K>

impl<K: Debug> Debug for T<K>

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

Formats the value using the given formatter.

impl<'de, K> Deserialize<'de> for T<K> where
    K: Deserialize<'de>, 

pub fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.

impl<K> Serialize for T<K> where
    K: Serialize, 

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

Serialize this value into the given Serde serializer.

impl<K> Test<K> for T<K> where
    K: Real, 

pub fn value(&self) -> K

Test value

pub fn df(&self) -> u32

Degree of freedom

pub fn p_value(&self) -> K