Module platform

Class Regression

java.lang.Object
org.jfree.data.statistics.Regression

public abstract class Regression extends Object
A utility class for fitting regression curves to data.
  • Constructor Summary

    Constructors
    Constructor
    Description
     
  • Method Summary

    Modifier and Type
    Method
    Description
    static double[]
    getOLSRegression(double[][] data)
    Returns the parameters 'a' and 'b' for an equation y = a + bx, fitted to the data using ordinary least squares regression.
    static double[]
    getOLSRegression(XYDataset data, int series)
    Returns the parameters 'a' and 'b' for an equation y = a + bx, fitted to the data using ordinary least squares regression.
    static double[]
    getPolynomialRegression(XYDataset dataset, int series, int order)
    Returns the parameters 'a0', 'a1', 'a2', ..., 'an' for a polynomial function of order n, y = a0 + a1 * x + a2 * x^2 + ... + an * x^n, fitted to the data using a polynomial regression equation.
    static double[]
    getPowerRegression(double[][] data)
    Returns the parameters 'a' and 'b' for an equation y = ax^b, fitted to the data using a power regression equation.
    static double[]
    getPowerRegression(XYDataset data, int series)
    Returns the parameters 'a' and 'b' for an equation y = ax^b, fitted to the data using a power regression equation.

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
  • Constructor Details

    • Regression

      public Regression()
  • Method Details

    • getOLSRegression

      public static double[] getOLSRegression(double[][] data)
      Returns the parameters 'a' and 'b' for an equation y = a + bx, fitted to the data using ordinary least squares regression. The result is returned as a double[], where result[0] --> a, and result[1] --> b.
      Parameters:
      data - the data.
      Returns:
      The parameters.
    • getOLSRegression

      public static double[] getOLSRegression(XYDataset data, int series)
      Returns the parameters 'a' and 'b' for an equation y = a + bx, fitted to the data using ordinary least squares regression. The result is returned as a double[], where result[0] --> a, and result[1] --> b.
      Parameters:
      data - the data.
      series - the series (zero-based index).
      Returns:
      The parameters.
    • getPowerRegression

      public static double[] getPowerRegression(double[][] data)
      Returns the parameters 'a' and 'b' for an equation y = ax^b, fitted to the data using a power regression equation. The result is returned as an array, where double[0] --> a, and double[1] --> b.
      Parameters:
      data - the data.
      Returns:
      The parameters.
    • getPowerRegression

      public static double[] getPowerRegression(XYDataset data, int series)
      Returns the parameters 'a' and 'b' for an equation y = ax^b, fitted to the data using a power regression equation. The result is returned as an array, where double[0] --> a, and double[1] --> b.
      Parameters:
      data - the data.
      series - the series to fit the regression line against.
      Returns:
      The parameters.
    • getPolynomialRegression

      public static double[] getPolynomialRegression(XYDataset dataset, int series, int order)
      Returns the parameters 'a0', 'a1', 'a2', ..., 'an' for a polynomial function of order n, y = a0 + a1 * x + a2 * x^2 + ... + an * x^n, fitted to the data using a polynomial regression equation. The result is returned as an array with a length of n + 2, where double[0] --> a0, double[1] --> a1, .., double[n] --> an. and double[n + 1] is the correlation coefficient R2 Reference: J. D. Faires, R. L. Burden, Numerische Methoden (german edition), pp. 243ff and 327ff.
      Parameters:
      dataset - the dataset (null not permitted).
      series - the series to fit the regression line against (the series must have at least order + 1 non-NaN items).
      order - the order of the function (> 0).
      Returns:
      The parameters.