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

All Implemented Interfaces:

Serializable, Statistic, TallyStatistic

Direct Known Subclasses:

EventBasedTally
```
public class Tally
extends Object
implements TallyStatistic
```
The Tally class registers a series of values and provides mean, standard deviation, etc. of the registered values.
Copyright (c) 2002-2023 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 Static 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 registered values between -infinity up to and including a given quantile.
`String`	`getDescription()`	Returns the description of the statistic.
`double`	`getMax()`	Returns the maximum value of any given observation, or NaN when no observations were registered.
`double`	`getMin()`	Returns the minimum value of any given observation, or NaN when no observations were registered.
`long`	`getN()`	Return the current number of observations.
`double`	`getPopulationExcessKurtosis()`	Return the population excess kurtosis of the registered data.
`double`	`getPopulationKurtosis()`	Return the (biased) population kurtosis of the registered data.
`double`	`getPopulationMean()`	Returns the population mean of all observations since the initialization.
`double`	`getPopulationSkewness()`	Return the (biased) population skewness of the registered 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 registered data.
`double`	`getSampleKurtosis()`	Return the sample kurtosis of the registered data.
`double`	`getSampleMean()`	Returns the sample mean of all observations since the initialization.
`double`	`getSampleSkewness()`	Return the (unbiased) sample skewness of the registered 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.
`void`	`initialize()`	Initialize the statistic.
`double`	`register(double value)`	Process one observed value.
`void`	`register(double... values)`	Ingest an array of values.
`static String`	`reportFooter()`	Return a string representing a footer for a textual table with a monospaced font that can contain multiple statistics.
`static String`	`reportHeader()`	Return a string representing a header for a textual table with a monospaced font that can contain multiple statistics.
`String`	`reportLine()`	Return a string representing a line with important statistics values for this statistic, for a textual table with a monospaced font that can contain multiple statistics.
`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.Statistic
formatFixed

- Field Detail
  - semaphore
```
protected Object semaphore
```
    the synchronized lock.
- Constructor Detail
  - Tally
```
public Tally(String description)
```
    Convenience constructor that uses a NoStorageAccumulator to estimate quantiles.
    
    Parameters:
    
    description - String; the description of this tally
  - 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.
- Method Detail
  - initialize
```
public void initialize()
```
    Initialize the statistic.
    
    Specified by:
    
    initialize in interface Statistic
  - register
```
public void register(double... values)
```
    Ingest an array of values.
    
    Parameters:
    
    values - double...; the values to register
  - register
```
public double register(double value)
```
    Process one observed value.
    
    Parameters:
    
    value - double; the value to process
    
    Returns:
    
    double; the value
  - getDescription
```
public String getDescription()
```
    Returns the description of the statistic.
    
    Specified by:
    
    getDescription in interface Statistic
    
    Returns:
    
    String; the description of the statistic
  - getMax
```
public double getMax()
```
    Returns the maximum value of any given observation, or NaN when no observations were registered.
    
    Specified by:
    
    getMax in interface TallyStatistic
    
    Returns:
    
    double; the maximum value of any given observation
  - getMin
```
public double getMin()
```
    Returns the minimum value of any given observation, or NaN when no observations were registered.
    
    Specified by:
    
    getMin in interface TallyStatistic
    
    Returns:
    
    double; the minimum value of any given observation
  - getN
```
public long getN()
```
    Return the current number of observations.
    
    Specified by:
    
    getN in interface Statistic
    
    Returns:
    
    long; the number of observations
  - getSum
```
public double getSum()
```
    Return the sum of the values of the observations.
    
    Returns:
    
    double; the sum of the values of the observations
  - getSampleMean
```
public double getSampleMean()
```
    Returns the sample mean of all observations since the initialization.
    
    Returns:
    
    double; the sample mean
  - getPopulationMean
```
public double getPopulationMean()
```
    Returns the population mean of all observations since the initialization.
    
    Returns:
    
    double; the population mean
  - getSampleStDev
```
public 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.
    
    Returns:
    
    double; the sample standard deviation
  - getPopulationStDev
```
public 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.
    
    Returns:
    
    double; the population standard deviation
  - getSampleVariance
```
public 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)
    
    Returns:
    
    double; the current sample variance of this tally
  - getPopulationVariance
```
public 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 ]
    
    Returns:
    
    double; the current population variance of this tally
  - getSampleSkewness
```
public double getSampleSkewness()
```
    Return the (unbiased) sample skewness of the registered 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.
    
    Returns:
    
    double; the sample skewness of the registered data
  - getPopulationSkewness
```
public double getPopulationSkewness()
```
    Return the (biased) population skewness of the registered 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 ] .
    
    Returns:
    
    double; the skewness of the registered data
  - getSampleKurtosis
```
public double getSampleKurtosis()
```
    Return the sample kurtosis of the registered 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 ] .
    
    Returns:
    
    double; the sample kurtosis of the registered data
  - getPopulationKurtosis
```
public double getPopulationKurtosis()
```
    Return the (biased) population kurtosis of the registered 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 ] .
    
    Returns:
    
    double; the population kurtosis of the registered data
  - getSampleExcessKurtosis
```
public double getSampleExcessKurtosis()
```
    Return the sample excess kurtosis of the registered 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.
    
    Returns:
    
    double; the sample excess kurtosis of the registered data
  - getPopulationExcessKurtosis
```
public double getPopulationExcessKurtosis()
```
    Return the population excess kurtosis of the registered 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.
    
    Returns:
    
    double; the population excess kurtosis of the registered data
  - getQuantile
```
public double getQuantile(double probability)
```
    Compute the quantile for the given probability.
    
    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
    
    Throws:
    
    IllegalArgumentException - when the probability is less than 0 or larger than 1
  - getCumulativeProbability
```
public double getCumulativeProbability(double quantile)
```
    Get, or estimate fraction of registered values between -infinity up to and including a given quantile.
    
    Parameters:
    
    quantile - double; the given quantile
    
    Returns:
    
    double; the estimated or observed fraction of registered values between -infinity up to and including the given quantile. When this TallyInterface has registered zero values; this method shall return NaN.
    
    Throws:
    
    IllegalArgumentException - when quantile is NaN
  - getConfidenceInterval
```
public double[] getConfidenceInterval(double alpha)
```
    returns the confidence interval on either side of the mean.
    
    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
    
    Throws:
    
    IllegalArgumentException - when alpha is less than 0 or larger than 1
  - getConfidenceInterval
```
public double[] getConfidenceInterval(double alpha,
                                      ConfidenceInterval side)
```
    returns the confidence interval based of the mean.
    
    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
    
    Throws:
    
    IllegalArgumentException - when alpha is less than 0 or larger than 1
    
    NullPointerException - when side is null
  - toString
```
public String toString()
```
    Overrides:
    
    toString in class Object
  - reportHeader
```
public static String reportHeader()
```
    Return a string representing a header for a textual table with a monospaced font that can contain multiple statistics.
    
    Returns:
    
    String; header for the textual table.
  - reportLine
```
public String reportLine()
```
    Return a string representing a line with important statistics values for this statistic, for a textual table with a monospaced font that can contain multiple statistics.
    
    Specified by:
    
    reportLine in interface Statistic
    
    Returns:
    
    String; line with most important values of the statistic
  - reportFooter
```
public static String reportFooter()
```
    Return a string representing a footer for a textual table with a monospaced font that can contain multiple statistics.
    
    Returns:
    
    String; footer for the textual table

Class Tally

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

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

Field Detail

semaphore

Constructor Detail

Tally

Tally

Method Detail

initialize

register

register

getDescription

getMax

getMin

getN

getSum

getSampleMean

getPopulationMean

getSampleStDev

getPopulationStDev

getSampleVariance

getPopulationVariance

getSampleSkewness

getPopulationSkewness

getSampleKurtosis

getPopulationKurtosis

getSampleExcessKurtosis

getPopulationExcessKurtosis

getQuantile

getCumulativeProbability

getConfidenceInterval

getConfidenceInterval

toString

reportHeader

reportLine

reportFooter