java.lang.Object
- org.djutils.stats.summarizers.Tally

All Implemented Interfaces:

Serializable, BasicTallyInterface, TallyInterface
```
public class Tally
extends Object
implements TallyInterface
```
The Tally class ingests a series of values and provides mean, standard deviation, etc. of the ingested values.
Copyright (c) 2002-2021 Delft University of Technology, Jaffalaan 5, 2628 BX Delft, the Netherlands. All rights reserved. See for project information https://simulation.tudelft.nl. The DSOL project is distributed under a three-clause BSD-style license, which can be found at https://simulation.tudelft.nl/dsol/3.0/license.html.

Author:

Alexander Verbraeck, Peter Jacobs , Peter Knoppers

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type Field Description

protected Object semaphore
the synchronized lock.

Constructor Summary

Constructors
Constructor	Description
`Tally(String description)`	Convenience constructor that uses a NoStorageAccumulator to estimate quantiles.
`Tally(String description, QuantileAccumulator quantileAccumulator)`	Constructs a new Tally.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`double[]`	`getConfidenceInterval(double alpha)`	returns the confidence interval on either side of the mean.
`double[]`	`getConfidenceInterval(double alpha, ConfidenceInterval side)`	returns the confidence interval based of the mean.
`double`	`getCumulativeProbability(double quantile)`	Get, or estimate fraction of ingested values between -infinity up to and including a given quantile.
`String`	`getDescription()`	returns the description of this tally.
`double`	`getMax()`	Returns the max.
`double`	`getMin()`	Returns the min.
`long`	`getN()`	Returns the number of observations.
`double`	`getPopulationExcessKurtosis()`	Return the population excess kurtosis of the ingested data.
`double`	`getPopulationKurtosis()`	Return the (biased) population kurtosis of the ingested data.
`double`	`getPopulationSkewness()`	Return the (biased) population skewness of the ingested data.
`double`	`getPopulationStDev()`	Returns the current (biased) population standard deviation of all observations since the initialization.
`double`	`getPopulationVariance()`	Returns the current (biased) population variance of all observations since the initialization.
`double`	`getQuantile(double probability)`	Compute the quantile for the given probability.
`double`	`getSampleExcessKurtosis()`	Return the sample excess kurtosis of the ingested data.
`double`	`getSampleKurtosis()`	Return the sample kurtosis of the ingested data.
`double`	`getSampleMean()`	Returns the sample mean of all observations since the initialization.
`double`	`getSampleSkewness()`	Return the (unbiased) sample skewness of the ingested data.
`double`	`getSampleStDev()`	Returns the current (unbiased) sample standard deviation of all observations since the initialization.
`double`	`getSampleVariance()`	Returns the current (unbiased) sample variance of all observations since the initialization.
`double`	`getSum()`	Return the sum of the values of the observations.
`double`	`ingest(double value)`	Process one observed value.
`void`	`ingest(double... values)`	Ingest an array of values.
`void`	`initialize()`	initializes the Tally.
`String`	`toString()`

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.djutils.stats.summarizers.TallyInterface
getPopulationMean

- Field Detail
  - semaphore
```
protected Object semaphore
```
    the synchronized lock.
- Constructor Detail
  - Tally
```
public Tally(String description,
             QuantileAccumulator quantileAccumulator)
```
    Constructs a new Tally.
    
    Parameters:
    
    description - String; the description of this tally
    
    quantileAccumulator - QuantileAccumulator; the input series accumulator that can approximate or compute quantiles.
  - Tally
```
public Tally(String description)
```
    Convenience constructor that uses a NoStorageAccumulator to estimate quantiles.
    
    Parameters:
    
    description - String; the description of this tally
- Method Detail
  - getSampleMean
```
public final double getSampleMean()
```
    Returns the sample mean of all observations since the initialization.
    
    Specified by:
    
    getSampleMean in interface TallyInterface
    
    Returns:
    
    double; the sample mean
  - getQuantile
```
public final double getQuantile(double probability)
```
    Compute the quantile for the given probability.
    
    Specified by:
    
    getQuantile in interface TallyInterface
    
    Parameters:
    
    probability - double; the probability for which the quantile is to be computed. The value should be between 0 and 1, inclusive.
    
    Returns:
    
    double; the quantile for the probability
  - getCumulativeProbability
```
public double getCumulativeProbability(double quantile)
```
    Get, or estimate fraction of ingested values between -infinity up to and including a given quantile.
    
    Specified by:
    
    getCumulativeProbability in interface TallyInterface
    
    Parameters:
    
    quantile - double; the given quantile
    
    Returns:
    
    double; the estimated or observed fraction of ingested values between -infinity up to and including the given quantile. When this TallyInterface has ingested zero values; this method shall return NaN.
  - getConfidenceInterval
```
public final double[] getConfidenceInterval(double alpha)
```
    returns the confidence interval on either side of the mean.
    
    Specified by:
    
    getConfidenceInterval in interface TallyInterface
    
    Parameters:
    
    alpha - double; Alpha is the significance level used to compute the confidence level. The confidence level equals 100*(1 - alpha)%, or in other words, an alpha of 0.05 indicates a 95 percent confidence level.
    
    Returns:
    
    double[]; the confidence interval of this tally
  - getConfidenceInterval
```
public final double[] getConfidenceInterval(double alpha,
                                            ConfidenceInterval side)
```
    returns the confidence interval based of the mean.
    
    Specified by:
    
    getConfidenceInterval in interface TallyInterface
    
    Parameters:
    
    alpha - double; Alpha is the significance level used to compute the confidence level. The confidence level equals 100*(1 - alpha)%, or in other words, an alpha of 0.05 indicates a 95 percent confidence level.
    
    side - ConfidenceInterval; the side of the confidence interval with respect to the mean
    
    Returns:
    
    double[]; the confidence interval of this tally
  - getDescription
```
public final String getDescription()
```
    returns the description of this tally.
    
    Specified by:
    
    getDescription in interface BasicTallyInterface
    
    Returns:
    
    Sting description
  - getMax
```
public final double getMax()
```
    Returns the max.
    
    Specified by:
    
    getMax in interface BasicTallyInterface
    
    Returns:
    
    double
  - getMin
```
public final double getMin()
```
    Returns the min.
    
    Specified by:
    
    getMin in interface BasicTallyInterface
    
    Returns:
    
    double
  - getN
```
public final long getN()
```
    Returns the number of observations.
    
    Specified by:
    
    getN in interface BasicTallyInterface
    
    Returns:
    
    long n
  - getSampleStDev
```
public final double getSampleStDev()
```
    Returns the current (unbiased) sample standard deviation of all observations since the initialization. The sample standard deviation is defined as the square root of the sample variance.
    
    Specified by:
    
    getSampleStDev in interface TallyInterface
    
    Returns:
    
    double; the sample standard deviation
  - getPopulationStDev
```
public final double getPopulationStDev()
```
    Returns the current (biased) population standard deviation of all observations since the initialization. The population standard deviation is defined as the square root of the population variance.
    
    Specified by:
    
    getPopulationStDev in interface TallyInterface
    
    Returns:
    
    double; the population standard deviation
  - getSum
```
public final double getSum()
```
    Return the sum of the values of the observations.
    
    Specified by:
    
    getSum in interface TallyInterface
    
    Returns:
    
    double; sum
  - getSampleVariance
```
public final double getSampleVariance()
```
    Returns the current (unbiased) sample variance of all observations since the initialization. The calculation of the sample variance in relation to the population variance is undisputed. The formula is:
    S² = (1 / (n - 1)) * [ Σx² - (Σx)² / n ]
    which can be calculated on the basis of the calculated population variance σ² as follows:
    S² = σ² * n / (n - 1)
    
    Specified by:
    
    getSampleVariance in interface TallyInterface
    
    Returns:
    
    double; the current sample variance of this tally
  - getPopulationVariance
```
public final double getPopulationVariance()
```
    Returns the current (biased) population variance of all observations since the initialization. The population variance is defined as:
    σ² = (1 / n) * [ Σx² - (Σx)² / n ]
    
    Specified by:
    
    getPopulationVariance in interface TallyInterface
    
    Returns:
    
    double; the current population variance of this tally
  - getSampleSkewness
```
public final double getSampleSkewness()
```
    Return the (unbiased) sample skewness of the ingested data. There are different formulas to calculate the unbiased (sample) skewness from the biased (population) skewness. Minitab, for instance calculates unbiased skewness as:
    Skew_unbiased = Skew_biased [ ( n - 1) / n ]^3/2
    whereas SAS, SPSS and Excel calculate it as:
    Skew_unbiased = Skew_biased √[ n ( n - 1)] / (n - 2)
    Here we follow the last mentioned formula. All formulas converge to the same value with larger n.
    
    Specified by:
    
    getSampleSkewness in interface TallyInterface
    
    Returns:
    
    double; the sample skewness of the ingested data
  - getPopulationSkewness
```
public final double getPopulationSkewness()
```
    Return the (biased) population skewness of the ingested data. The population skewness is defined as:
    Skew_biased = [ Σ ( x - μ ) ³ ] / [ n . S³ ]
    where S² is the sample variance. So the denominator is equal to [ n . sample_var^3/2 ] .
    
    Specified by:
    
    getPopulationSkewness in interface TallyInterface
    
    Returns:
    
    double; the skewness of the ingested data
  - getSampleKurtosis
```
public final double getSampleKurtosis()
```
    Return the sample kurtosis of the ingested data. The sample kurtosis can be defined in multiple ways. Here, we choose the following formula:
    Kurt_unbiased = [ Σ ( x - μ ) ⁴ ] / [ ( n - 1 ) . S⁴ ]
    where S² is the sample variance. So the denominator is equal to [ ( n - 1 ) . sample_var² ] .
    
    Specified by:
    
    getSampleKurtosis in interface TallyInterface
    
    Returns:
    
    double; the sample kurtosis of the ingested data
  - getPopulationKurtosis
```
public final double getPopulationKurtosis()
```
    Return the (biased) population kurtosis of the ingested data. The population kurtosis is defined as:
    Kurt_biased = [ Σ ( x - μ ) ⁴ ] / [ n . σ⁴ ]
    where σ² is the population variance. So the denominator is equal to [ n . pop_var² ] .
    
    Specified by:
    
    getPopulationKurtosis in interface TallyInterface
    
    Returns:
    
    double; the population kurtosis of the ingested data
  - getSampleExcessKurtosis
```
public final double getSampleExcessKurtosis()
```
    Return the sample excess kurtosis of the ingested data. The sample excess kurtosis is the sample-corrected value of the excess kurtosis. Several formulas exist to calculate the sample excess kurtosis from the population kurtosis. Here we use:
    ExcessKurt_unbiased = ( n - 1 ) / [( n - 2 ) * ( n - 3 )] [ ( n + 1 ) * ExcessKurt_biased + 6]
    This is the excess kurtosis that is calculated by, for instance, SAS, SPSS and Excel.
    
    Specified by:
    
    getSampleExcessKurtosis in interface TallyInterface
    
    Returns:
    
    double; the sample excess kurtosis of the ingested data
  - getPopulationExcessKurtosis
```
public final double getPopulationExcessKurtosis()
```
    Return the population excess kurtosis of the ingested data. The kurtosis value of the normal distribution is 3. The excess kurtosis is the kurtosis value shifted by -3 to be 0 for the normal distribution.
    
    Specified by:
    
    getPopulationExcessKurtosis in interface TallyInterface
    
    Returns:
    
    double; the population excess kurtosis of the ingested data
  - initialize
```
public void initialize()
```
    initializes the Tally. This methods sets the max, min, n, sum and variance values to their initial values.
    
    Specified by:
    
    initialize in interface BasicTallyInterface
  - ingest
```
public void ingest(double... values)
```
    Ingest an array of values.
    
    Parameters:
    
    values - double...; the values to ingest
  - ingest
```
public double ingest(double value)
```
    Process one observed value.
    
    Specified by:
    
    ingest in interface TallyInterface
    
    Parameters:
    
    value - double; the value to process
    
    Returns:
    
    double; the value
  - toString
```
public String toString()
```
    Overrides:
    
    toString in class Object

Class Tally

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Methods inherited from interface org.djutils.stats.summarizers.TallyInterface

Field Detail

semaphore

Constructor Detail

Tally

Tally

Method Detail

getSampleMean

getQuantile

getCumulativeProbability

getConfidenceInterval

getConfidenceInterval

getDescription

getMax

getMin

getN

getSampleStDev

getPopulationStDev

getSum

getSampleVariance

getPopulationVariance

getSampleSkewness

getPopulationSkewness

getSampleKurtosis

getPopulationKurtosis

getSampleExcessKurtosis

getPopulationExcessKurtosis

initialize

ingest

ingest

toString