org.jfree.data.statistics
Class Regression

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

public abstract class Regression
extends java.lang.Object

A utility class for fitting regression curves to data.


Constructor Summary
Regression()
           
 
Method Summary
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 + ...
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 Detail

Regression

public Regression()
Method Detail

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.
Since:
1.0.14