Struct average::WeightedMeanWithError

pub struct WeightedMeanWithError { /* fields omitted */ }

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());

Methods

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 0 for an empty sample.

pub fn unweighted_mean(&self) -> f64

Estimate the unweighted mean of the population.

Returns 0 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.

pub fn sample_variance(&self) -> f64

Calculate the unweighted sample variance.

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

pub fn error(&self) -> f64

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

Returns 0 if the sum of weights is 0.

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

Trait Implementations

impl Debug for WeightedMeanWithError

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

Formats the value using the given formatter.

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 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 Default for WeightedMeanWithError

fn default() -> WeightedMeanWithError

Returns the "default value" for a type.

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<'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.