Package org.djutils.stats.summarizers

Class Tally

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) 2002-2020 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 Details

  • Constructor Details

    • 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 Details

    • 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

    • 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 - short; 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:
        Skewunbiased = Skewbiased [ ( n - 1) / n ] 3/2
      whereas SAS, SPSS and Excel calculate it as:
        Skewunbiased = Skewbiased √[ 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:
        Skewbiased = [ Σ ( x - μ ) 3 ] / [ n . S3 ]
      where S2 is the sample variance. So the denominator is equal to [ n . sample_var3/2 ] .
      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:
        Kurtunbiased = [ 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:
        Kurtbiased = [ 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:
        ExcessKurtunbiased = ( n - 1 ) / [( n - 2 ) * ( n - 3 )] [ ( n + 1 ) * ExcessKurtbiased + 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 - 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