[−][src]Trait rstats::RStats
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
Implementations on Foreign Types
impl<'_> RStats for &'_ [i64]
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fn amean(self) -> Result<f64>
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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>
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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>
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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>
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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>
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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>
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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>
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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>
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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>
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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>
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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>
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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>>
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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
impl<'_> RStats for &'_ [f64]
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fn amean(self) -> Result<f64>
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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>
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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>
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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>
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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>
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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>
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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>
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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>
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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>
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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>
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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>
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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>>
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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