Constructor Detail

• PairedStatsAccumulator

  public PairedStatsAccumulator()

Method Detail

• add

  public void add(double x,
         double y)

  Adds the given pair of values to the dataset.

• addAll

  public void addAll(PairedStats values)

  Adds the given statistics to the dataset, as if the individual values used to compute the statistics had been added directly.

• snapshot

  public PairedStats snapshot()

  Returns an immutable snapshot of the current statistics.

• count

  public long count()

  Returns the number of pairs in the dataset.

• xStats

  public Stats xStats()

  Returns an immutable snapshot of the statistics on the x values alone.

• yStats

  public Stats yStats()

  Returns an immutable snapshot of the statistics on the y values alone.

• populationCovariance

  public double populationCovariance()

  Returns the population covariance of the values. The count must be non-zero.

  This is guaranteed to return zero if the dataset contains a single pair of finite values. It is not guaranteed to return zero when the dataset consists of the same pair of values multiple times, due to numerical errors.

  Non-finite values

  If the dataset contains any non-finite values (Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, or Double.NaN) then the result is Double.NaN.

  Throws:

  IllegalStateException - if the dataset is empty

• sampleCovariance

  public final double sampleCovariance()

  Returns the sample covariance of the values. The count must be greater than one.

  This is not guaranteed to return zero when the dataset consists of the same pair of values multiple times, due to numerical errors.

  Non-finite values

  If the dataset contains any non-finite values (Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, or Double.NaN) then the result is Double.NaN.

  Throws:

  IllegalStateException - if the dataset is empty or contains a single pair of values

• pearsonsCorrelationCoefficient

  public final double pearsonsCorrelationCoefficient()

  Returns the Pearson's or product-moment correlation coefficient of the values. The count must greater than one, and the x and y values must both have non-zero population variance (i.e. xStats().populationVariance() > 0.0 && yStats().populationVariance() > 0.0). The result is not guaranteed to be exactly +/-1 even when the data are perfectly (anti-)correlated, due to numerical errors. However, it is guaranteed to be in the inclusive range [-1, +1].

  Non-finite values

  If the dataset contains any non-finite values (Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, or Double.NaN) then the result is Double.NaN.

  Throws:

  IllegalStateException - if the dataset is empty or contains a single pair of values, or either the x and y dataset has zero population variance

• leastSquaresFit

  public final LinearTransformation leastSquaresFit()

  Returns a linear transformation giving the best fit to the data according to Ordinary Least Squares linear regression of y as a function of x. The count must be greater than one, and either the x or y data must have a non-zero population variance (i.e. xStats().populationVariance() > 0.0 || yStats().populationVariance() > 0.0). The result is guaranteed to be horizontal if there is variance in the x data but not the y data, and vertical if there is variance in the y data but not the x data.

  This fit minimizes the root-mean-square error in y as a function of x. This error is defined as the square root of the mean of the squares of the differences between the actual y values of the data and the values predicted by the fit for the x values (i.e. it is the square root of the mean of the squares of the vertical distances between the data points and the best fit line). For this fit, this error is a fraction sqrt(1 - R*R) of the population standard deviation of y, where R is the Pearson's correlation coefficient (as given by pearsonsCorrelationCoefficient()).

  The corresponding root-mean-square error in x as a function of y is a fraction sqrt(1/(R*R) - 1) of the population standard deviation of x. This fit does not normally minimize that error: to do that, you should swap the roles of x and y.

  Non-finite values

  If the dataset contains any non-finite values (Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, or Double.NaN) then the result is LinearTransformation.forNaN().

  Throws:

  IllegalStateException - if the dataset is empty or contains a single pair of values, or both the x and y dataset have zero population variance