Struct average::WeightedMeanWithError

pub struct WeightedMeanWithError { /* private fields */ }

Estimate the weighted and unweighted arithmetic mean and the unweighted variance of a sequence of numbers (“population”).

This can be used to estimate the standard error of the weighted mean.

Example

use average::WeightedMeanWithError;

let a: WeightedMeanWithError = (1..6).zip(1..6)
    .map(|(x, w)| (f64::from(x), f64::from(w))).collect();
println!("The weighted mean is {} ± {}.", a.weighted_mean(), a.error());

Implementations

impl WeightedMeanWithError

pub fn new() -> WeightedMeanWithError

Create a new weighted and unweighted mean estimator.

pub fn add(&mut self, sample: f64, weight: f64)

Add an observation sampled from the population.

pub fn is_empty(&self) -> bool

Determine whether the sample is empty.

pub fn sum_weights(&self) -> f64

Return the sum of the weights.

Returns 0 for an empty sample.

pub fn sum_weights_sq(&self) -> f64

Return the sum of the squared weights.

Returns 0 for an empty sample.

pub fn weighted_mean(&self) -> f64

Estimate the weighted mean of the population.

Returns NaN for an empty sample, or if the sum of weights is zero.

pub fn unweighted_mean(&self) -> f64

Estimate the unweighted mean of the population.

Returns NaN for an empty sample.

pub fn len(&self) -> u64

Return the sample size.

pub fn effective_len(&self) -> f64

Calculate the effective sample size.

pub fn population_variance(&self) -> f64

Calculate the unweighted population variance of the sample.

This is a biased estimator of the variance of the population.

Returns NaN for an empty sample.

pub fn sample_variance(&self) -> f64

Calculate the unweighted sample variance.

This is an unbiased estimator of the variance of the population.

Returns NaN for samples of size 1 or less.

pub fn variance_of_weighted_mean(&self) -> f64

Estimate the standard error of the weighted mean of the population.

Returns NaN if the sample is empty, or if the sum of weights is zero.

This unbiased estimator assumes that the samples were independently drawn from the same population with constant variance.

pub fn error(&self) -> f64

Available on crate features std or libm only.

Estimate the standard error of the weighted mean of the population.

Returns NaN if the sample is empty, or if the sum of weights is zero.

This unbiased estimator assumes that the samples were independently drawn from the same population with constant variance.

Trait Implementations

impl Clone for WeightedMeanWithError

fn clone(&self) -> WeightedMeanWithError

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for WeightedMeanWithError

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

Formats the value using the given formatter.

impl Default for WeightedMeanWithError

fn default() -> WeightedMeanWithError

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for WeightedMeanWithError

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<'a> Extend<&'a (f64, f64)> for WeightedMeanWithError

fn extend<T: IntoIterator<Item = &'a (f64, f64)>>(&mut self, iter: T)

Extends a collection with the contents of an iterator.

fn extend_one(&mut self, item: A)

🔬This is a nightly-only experimental API. (extend_one)

Extends a collection with exactly one element.

fn extend_reserve(&mut self, additional: usize)

🔬This is a nightly-only experimental API. (extend_one)

Reserves capacity in a collection for the given number of additional elements.

impl Extend<(f64, f64)> for WeightedMeanWithError

fn extend<T: IntoIterator<Item = (f64, f64)>>(&mut self, iter: T)

Extends a collection with the contents of an iterator.

fn extend_one(&mut self, item: A)

🔬This is a nightly-only experimental API. (extend_one)

Extends a collection with exactly one element.

fn extend_reserve(&mut self, additional: usize)

🔬This is a nightly-only experimental API. (extend_one)

Reserves capacity in a collection for the given number of additional elements.

impl<'a> FromIterator<&'a (f64, f64)> for WeightedMeanWithError

fn from_iter<T>(iter: T) -> WeightedMeanWithError

where T: IntoIterator<Item = &'a (f64, f64)>,

Creates a value from an iterator.

impl FromIterator<(f64, f64)> for WeightedMeanWithError

fn from_iter<T>(iter: T) -> WeightedMeanWithError

where T: IntoIterator<Item = (f64, f64)>,

Creates a value from an iterator.

impl Merge for WeightedMeanWithError

fn merge(&mut self, other: &WeightedMeanWithError)

Merge another sample into this one.

Example

use average::{WeightedMeanWithError, Merge};

let weighted_sequence: &[(f64, f64)] = &[
    (1., 0.1), (2., 0.2), (3., 0.3), (4., 0.4), (5., 0.5),
    (6., 0.6), (7., 0.7), (8., 0.8), (9., 0.9)];
let (left, right) = weighted_sequence.split_at(3);
let avg_total: WeightedMeanWithError = weighted_sequence.iter().collect();
let mut avg_left: WeightedMeanWithError = left.iter().collect();
let avg_right: WeightedMeanWithError = right.iter().collect();
avg_left.merge(&avg_right);
assert!((avg_total.weighted_mean() - avg_left.weighted_mean()).abs() < 1e-15);
assert!((avg_total.error() - avg_left.error()).abs() < 1e-15);

impl Serialize for WeightedMeanWithError

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

where __S: Serializer,

Serialize this value into the given Serde serializer.