Package org.djutils.stats.summarizers

Interface TallyInterface

All Superinterfaces:
BasicTallyInterface, Serializable
All Known Implementing Classes:
EventBasedTally, Tally
public interface TallyInterface
extends BasicTallyInterface
The Tally interface defines the methods to be implemented by a tally object, which 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

  • Method Summary

    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 getPopulationExcessKurtosis()
    Return the population excess kurtosis of the ingested data.
    double getPopulationKurtosis()
    Return the (biased) population kurtosis of the ingested data.
    default double getPopulationMean()
    Returns the population mean of all observations since the initialization.
    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.

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

    getDescription, getMax, getMin, getN, initialize

  • Method Details

    • ingest

      double ingest​(double value)
      Process one observed value.
      Parameters:
      value - double; the value to process
      Returns:
      double; the value

    • getSum

      double getSum()
      Return the sum of the values of the observations.
      Returns:
      double; sum

    • getSampleMean

      double getSampleMean()
      Returns the sample mean of all observations since the initialization.
      Returns:
      double; the sample mean

    • getPopulationMean

      default double getPopulationMean()
      Returns the population mean of all observations since the initialization.
      Returns:
      double; the population mean

    • getSampleStDev

      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

      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

      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

      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

      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.
      Returns:
      double; the sample skewness of the ingested data

    • getPopulationSkewness

      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 ] .
      Returns:
      double; the skewness of the ingested data

    • getSampleKurtosis

      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 ] .
      Returns:
      double; the sample kurtosis of the ingested data

    • getPopulationKurtosis

      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 ] .
      Returns:
      double; the population kurtosis of the ingested data

    • getSampleExcessKurtosis

      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.
      Returns:
      double; the sample excess kurtosis of the ingested data

    • getPopulationExcessKurtosis

      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.
      Returns:
      double; the population excess kurtosis of the ingested data

    • getQuantile

      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

    • getConfidenceInterval

      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

      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 - short; 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