Package ffx.numerics.math

Class SummaryStatistics

java.lang.Object
ffx.numerics.math.SummaryStatistics
public class SummaryStatistics extends Object
The SummaryStatistics class uses online, stable algorithms to calculate summary statistics from double arrays/lists, including mean, variance, standard deviation, max, min, sum, and count.

This is intended for accuracy and numerical stability, not necessarily for performance (e.g. using Kahan summation).

Since:
1.0
Author:
Michael J. Schnieders, Jacob M. Litman

  • Field Summary

    Fields
    Modifier and Type
    Field
    Description
    final long
    count
    Count of observations.
    final long
    dof
    Degrees of freedom.
    final double
    max
    Maximum value.
    final double
    mean
    Mean value.
    final double
    min
    Minimum value.
    final double
    sd
    Sample standard deviation.
    final double
    sdPopulation
    Population standard deviation.
    final double
    sum
    Sum of observations.
    final double
    sumWeights
    Sum of weights.
    final double
    var
    Sample variance.
    final double
    varPopulation
    Population variance.

  • Constructor Summary

    Constructors
    Constructor
    Description
    SummaryStatistics(double[] values)
    Constructs a static summary of a statistic from provided values.
    SummaryStatistics(double[] values, double[] weights, int first, int last, int stride)
    Constructs a static summary of a statistic from provided values.
    SummaryStatistics(double[] values, int first)
    Constructs a static summary of a statistic from provided values.
    SummaryStatistics(double[] values, int first, int last)
    Constructs a static summary of a statistic from provided values.
    SummaryStatistics(double[] values, int first, int last, int stride)
    Constructs a static summary of a statistic from provided values.
    SummaryStatistics(RunningStatistics rs)
    Builds a static view of a running statistic.

  • Method Summary

    Modifier and Type
    Method
    Description
    double
    confidenceInterval()
    Computes a 95% confidence interval based on a Student's T-distribution.
    double
    confidenceInterval(double alpha)
    Computes a confidence interval based on a Student's T-distribution.
    String
    describe()
    Describe the Summary Statistics.
    double
    getMean()
    The mean.
    double
    getSd()
    The standard deviation.
    double
    getVar()
    The variance.
    String
    toString()
    $

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

  • Field Details

    • mean

      public final double mean
      Mean value.

    • var

      public final double var
      Sample variance.

    • varPopulation

      public final double varPopulation
      Population variance.

    • sd

      public final double sd
      Sample standard deviation.

    • sdPopulation

      public final double sdPopulation
      Population standard deviation.

    • sumWeights

      public final double sumWeights
      Sum of weights.

    • min

      public final double min
      Minimum value.

    • max

      public final double max
      Maximum value.

    • count

      public final long count
      Count of observations.

    • sum

      public final double sum
      Sum of observations.

    • dof

      public final long dof
      Degrees of freedom.

  • Constructor Details

    • SummaryStatistics

      public SummaryStatistics(RunningStatistics rs)
      Builds a static view of a running statistic.
      Parameters:
      rs - Running statistic.

    • SummaryStatistics

      public SummaryStatistics(double[] values)
      Constructs a static summary of a statistic from provided values. Assumes weights are all constant (1.0). Assumes all values will be used.
      Parameters:
      values - Values to summarize.

    • SummaryStatistics

      public SummaryStatistics(double[] values, int first)
      Constructs a static summary of a statistic from provided values. Assumes weights are all constant (1.0). Assumes all values from first to end will be used.
      Parameters:
      values - Values to summarize.
      first - First value to use.

    • SummaryStatistics

      public SummaryStatistics(double[] values, int first, int last)
      Constructs a static summary of a statistic from provided values. Assumes weights are all constant (1.0). Assumes a stride of 1.
      Parameters:
      values - Values to summarize.
      first - First value to use.
      last - Last value to use.

    • SummaryStatistics

      public SummaryStatistics(double[] values, int first, int last, int stride)
      Constructs a static summary of a statistic from provided values. Assumes weights are all constant (1.0).
      Parameters:
      values - Values to summarize.
      first - First value to use.
      last - Last value to use.
      stride - Stride between values used.

    • SummaryStatistics

      public SummaryStatistics(@Nullable double[] values, @Nullable double[] weights, int first, int last, int stride)
      Constructs a static summary of a statistic from provided values.
      Parameters:
      values - Values to summarize.
      weights - Weights for each value.
      first - First value to use.
      last - Last value to use.
      stride - Stride between values used.

  • Method Details

    • confidenceInterval

      public double confidenceInterval()
      Computes a 95% confidence interval based on a Student's T-distribution.
      Returns:
      95% confidence interval.

    • confidenceInterval

      public double confidenceInterval(double alpha)
      Computes a confidence interval based on a Student's T-distribution.
      Parameters:
      alpha - Alpha (e.g. 0.05 for a 95% CI).
      Returns:
      Confidence interval.

    • getMean

      public double getMean()
      The mean.
      Returns:
      Return the mean.

    • getSd

      public double getSd()
      The standard deviation.
      Returns:
      Return the standard deviation.

    • getVar

      public double getVar()
      The variance.
      Returns:
      Return the variance.

    • toString

      public String toString()
      $
      Overrides:
      toString in class Object

    • describe

      public String describe()
      Describe the Summary Statistics.
      Returns:
      Return the description.