[−][src]Trait rstats::RStats

pub trait RStats {
    fn amean(self) -> Result<f64>;

    fn ameanstd(self) -> Result<MStats>;

    fn awmean(self) -> Result<f64>;

    fn awmeanstd(self) -> Result<MStats>;

    fn hmean(self) -> Result<f64>;

    fn hwmean(self) -> Result<f64>;

    fn gmean(self) -> Result<f64>;

    fn gmeanstd(self) -> Result<MStats>;

    fn gwmean(self) -> Result<f64>;

    fn gwmeanstd(self) -> Result<MStats>;

    fn median(self) -> Result<Med>;

    fn ranks(self) -> Result<Vec<f64>>;
}

Implementing basic statistical measures. All these methods operate on only one vector (of data), so they take no arguments.

Required methods

fn amean(self) -> Result<f64>

Arithmetic mean

fn ameanstd(self) -> Result<MStats>

Arithmetic mean and standard deviation

fn awmean(self) -> Result<f64>

Weighted arithmetic mean

fn awmeanstd(self) -> Result<MStats>

Weighted arithmetic men and standard deviation

fn hmean(self) -> Result<f64>

Harmonic mean

fn hwmean(self) -> Result<f64>

Weighted harmonic mean

fn gmean(self) -> Result<f64>

Geometric mean

fn gmeanstd(self) -> Result<MStats>

Geometric mean and stndard deviation ratio

fn gwmean(self) -> Result<f64>

Weighed geometric mean

fn gwmeanstd(self) -> Result<MStats>

Weighted geometric mean and standard deviation ratio

fn median(self) -> Result<Med>

Median and quartiles

fn ranks(self) -> Result<Vec<f64>>

Creates vector of ranks for values in self

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Implementations on Foreign Types

impl<'_> RStats for &'_ [i64][src]

fn amean(self) -> Result<f64>[src]

Arithmetic mean of an i64 slice

Example

use rstats::RStats;
let v1:Vec<i64> = vec![1,2,3,4,5,6,7,8,9,10,11,12,13,14];
assert_eq!(v1.as_slice().amean().unwrap(),7.5_f64);

fn ameanstd(self) -> Result<MStats>[src]

Arithmetic mean and standard deviation of an i64 slice

Example

use rstats::RStats;
let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14];
let res = v1.as_slice().ameanstd().unwrap();
assert_eq!(res.mean,7.5_f64);
assert_eq!(res.std,4.031128874149275_f64);

fn awmean(self) -> Result<f64>[src]

Linearly weighted arithmetic mean of an i64 slice.
Linearly descending weights from n down to one.
Time dependent data should be in the stack order - the last being the oldest.

Example

use rstats::RStats;
let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14];
assert_eq!(v1.as_slice().awmean().unwrap(),5.333333333333333_f64);

fn awmeanstd(self) -> Result<MStats>[src]

Liearly weighted arithmetic mean and standard deviation of an i64 slice.
Linearly descending weights from n down to one.
Time dependent data should be in the stack order - the last being the oldest.

Example

use rstats::RStats;
let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14];
let res = v1.as_slice().awmeanstd().unwrap();
assert_eq!(res.mean,5.333333333333333_f64);
assert_eq!(res.std,3.39934634239519_f64);

fn hmean(self) -> Result<f64>[src]

Harmonic mean of an i64 slice.

Example

use rstats::RStats;
let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14];
assert_eq!(v1.as_slice().hmean().unwrap(),4.305622526633627_f64);

fn hwmean(self) -> Result<f64>[src]

Linearly weighted harmonic mean of an i64 slice.
Linearly descending weights from n down to one.
Time dependent data should be in the stack order - the last being the oldest.

Example

use rstats::RStats;
let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14];
assert_eq!(v1.as_slice().hwmean().unwrap(),3.019546395306663_f64);

fn gmean(self) -> Result<f64>[src]

Geometric mean of an i64 slice.
The geometric mean is just an exponential of an arithmetic mean of log data (natural logarithms of the data items).
The geometric mean is less sensitive to outliers near maximal value.
Zero valued data is not allowed.

Example

use rstats::RStats;
let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14];
assert_eq!(v1.as_slice().gmean().unwrap(),6.045855171418503_f64);

fn gwmean(self) -> Result<f64>[src]

Linearly weighted geometric mean of an i64 slice.
Descending weights from n down to one.
Time dependent data should be in the stack order - the last being the oldest.
The geometric mean is just an exponential of an arithmetic mean of log data (natural logarithms of the data items).
The geometric mean is less sensitive to outliers near maximal value.
Zero data is not allowed - would at best only produce zero result.

Example

use rstats::RStats;
let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14];
assert_eq!(v1.as_slice().gwmean().unwrap(),4.144953510241978_f64);

fn gmeanstd(self) -> Result<MStats>[src]

Geometric mean and std ratio of an i64 slice.
Zero valued data is not allowed.
Std of ln data becomes a ratio after conversion back.

Example

use rstats::RStats;
let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14];
let res = v1.as_slice().gmeanstd().unwrap();
assert_eq!(res.mean,6.045855171418503_f64);
assert_eq!(res.std,2.1084348239406303_f64);

fn gwmeanstd(self) -> Result<MStats>[src]

Linearly weighted version of gmeanstd.

Example

use rstats::RStats;
let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14];
let res = v1.as_slice().gwmeanstd().unwrap();
assert_eq!(res.mean,4.144953510241978_f64);
assert_eq!(res.std,2.1572089236412597_f64);

fn median(self) -> Result<Med>[src]

Fast median (avoids sorting).
The data values must be within a moderate range not exceeding u16size (65535).

Example

use rstats::RStats;
let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14];
let res = v1.as_slice().median().unwrap();
assert_eq!(res.median,7.5_f64);
assert_eq!(res.lquartile,4_f64);
assert_eq!(res.uquartile,11_f64);

fn ranks(self) -> Result<Vec<f64>>[src]

Returns vector of ranks 1..n, ranked from the biggest number in self (rank 1) to the smallest (rank n). Equalities lead to fractional ranks, hence Vec output and the range of rank values is reduced.

impl<'_> RStats for &'_ [f64][src]

fn amean(self) -> Result<f64>[src]

Arithmetic mean of an f64 slice

Example

use rstats::RStats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
assert_eq!(v1.as_slice().amean().unwrap(),7.5_f64);

fn ameanstd(self) -> Result<MStats>[src]

Arithmetic mean and standard deviation of an f64 slice

Example

use rstats::RStats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
let res = v1.as_slice().ameanstd().unwrap();
assert_eq!(res.mean,7.5_f64);
assert_eq!(res.std,4.031128874149275_f64);

fn awmean(self) -> Result<f64>[src]

Linearly weighted arithmetic mean of an f64 slice.
Linearly descending weights from n down to one.
Time dependent data should be in the stack order - the last being the oldest.

Example

use rstats::RStats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
assert_eq!(v1.as_slice().awmean().unwrap(),5.333333333333333_f64);

fn awmeanstd(self) -> Result<MStats>[src]

Liearly weighted arithmetic mean and standard deviation of an f64 slice.
Linearly descending weights from n down to one.
Time dependent data should be in the stack order - the last being the oldest.

Example

use rstats::RStats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
let res = v1.as_slice().awmeanstd().unwrap();
assert_eq!(res.mean,5.333333333333333_f64);
assert_eq!(res.std,3.39934634239519_f64);

fn hmean(self) -> Result<f64>[src]

Harmonic mean of an f64 slice.

Example

use rstats::RStats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
assert_eq!(v1.as_slice().hmean().unwrap(),4.305622526633627_f64);

fn hwmean(self) -> Result<f64>[src]

Linearly weighted harmonic mean of an f64 slice.
Linearly descending weights from n down to one.
Time dependent data should be in the stack order - the last being the oldest.

Example

use rstats::RStats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
assert_eq!(v1.as_slice().hwmean().unwrap(),3.019546395306663_f64);

fn gmean(self) -> Result<f64>[src]

Geometric mean of an i64 slice.
The geometric mean is just an exponential of an arithmetic mean of log data (natural logarithms of the data items).
The geometric mean is less sensitive to outliers near maximal value.
Zero valued data is not allowed.

Example

use rstats::RStats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
assert_eq!(v1.as_slice().gmean().unwrap(),6.045855171418503_f64);

fn gwmean(self) -> Result<f64>[src]

Linearly weighted geometric mean of an i64 slice.
Descending weights from n down to one.
Time dependent data should be in the stack order - the last being the oldest.
The geometric mean is just an exponential of an arithmetic mean of log data (natural logarithms of the data items).
The geometric mean is less sensitive to outliers near maximal value.
Zero data is not allowed - would at best only produce zero result.

Example

use rstats::RStats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
assert_eq!(v1.as_slice().gwmean().unwrap(),4.144953510241978_f64);

fn gmeanstd(self) -> Result<MStats>[src]

Geometric mean and std ratio of an f64 slice.
Zero valued data is not allowed.
Std of ln data becomes a ratio after conversion back.

Example

use rstats::RStats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
let res = v1.as_slice().gmeanstd().unwrap();
assert_eq!(res.mean,6.045855171418503_f64);
assert_eq!(res.std,2.1084348239406303_f64);

fn gwmeanstd(self) -> Result<MStats>[src]

Linearly weighted version of gmeanstd.

Example

use rstats::RStats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
let res = v1.as_slice().gwmeanstd().unwrap();
assert_eq!(res.mean,4.144953510241978_f64);
assert_eq!(res.std,2.1572089236412597_f64);

fn median(self) -> Result<Med>[src]

Median of an f64 slice

Example

use rstats::RStats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
let res = v1.as_slice().median().unwrap();
assert_eq!(res.median,7.5_f64);
assert_eq!(res.lquartile,4_f64);
assert_eq!(res.uquartile,11_f64);

fn ranks(self) -> Result<Vec<f64>>[src]

Returns vector of ranks 1..n, ranked from the biggest number in self (rank 1) to the smallest (rank n). Equalities lead to fractional ranks, hence Vec output and the range of rank values is reduced.

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Implementors

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