Trait rstats::Stats[−][src]

pub trait Stats {
    fn amean(self) -> Result<f64>
    where
        Self: Sized,
    { ... }

    fn ameanstd(self) -> Result<MStats>
    where
        Self: Sized,
    { ... }

    fn awmean(self) -> Result<f64>
    where
        Self: Sized,
    { ... }

    fn awmeanstd(self) -> Result<MStats>
    where
        Self: Sized,
    { ... }

    fn hmean(self) -> Result<f64>
    where
        Self: Sized,
    { ... }

    fn hwmean(self) -> Result<f64>
    where
        Self: Sized,
    { ... }

    fn gmean(self) -> Result<f64>
    where
        Self: Sized,
    { ... }

    fn gmeanstd(self) -> Result<MStats>
    where
        Self: Sized,
    { ... }

    fn gwmean(self) -> Result<f64>
    where
        Self: Sized,
    { ... }

    fn gwmeanstd(self) -> Result<MStats>
    where
        Self: Sized,
    { ... }

    fn median(self) -> Result<Med>
    where
        Self: Sized,
    { ... }
}
Expand description

Basic one dimensional (1-d) statistical measures and ranking. These methods operate on just one vector (of data) and take no arguments.

Provided methods

Arithmetic mean

Arithmetic mean and standard deviation

Weighted arithmetic mean

Weighted arithmetic men and standard deviation

Harmonic mean

Weighted harmonic mean

Geometric mean

Geometric mean and standard deviation ratio

Weighed geometric mean

Weighted geometric mean and standard deviation ratio

Median and quartiles

Implementations on Foreign Types

Arithmetic mean of an f64 slice

Example

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

Arithmetic mean and (population) standard deviation of an f64 slice

Example

use rstats::Stats;
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);

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::Stats;
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);

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::Stats;
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);

Harmonic mean of an f64 slice.

Example

use rstats::Stats;
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);

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::Stats;
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);

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::Stats;
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);

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::Stats;
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);

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::Stats;
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);

Linearly weighted version of gmeanstd.

Example

use rstats::Stats;
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);

Median of an f64 slice

Example

use rstats::Stats;
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,4.25_f64);
assert_eq!(res.uquartile,10.75_f64);

Arithmetic mean of an f64 slice

Example

use rstats::Stats;
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);

Arithmetic mean and (population) standard deviation of an f64 slice

Example

use rstats::Stats;
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);

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::Stats;
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);

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::Stats;
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);

Harmonic mean of an f64 slice.

Example

use rstats::Stats;
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);

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::Stats;
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);

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::Stats;
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);

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::Stats;
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);

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::Stats;
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);

Linearly weighted version of gmeanstd.

Example

use rstats::Stats;
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);

Median of an f64 slice

Example

use rstats::Stats;
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,4.25_f64);
assert_eq!(res.uquartile,10.75_f64);

Implementors