org.jfree.data.statistics

Class Regression

java.lang.Object

org.jfree.data.statistics.Regression

public abstract class Regression
extends java.lang.Object

A utility class for fitting regression curves to data.

Constructor Summary

Constructors

Constructor and Description
Regression()

Method Summary

Modifier and Type	Method and Description
private static double[][]	calculateSubMatrix(double[][] matrix) Returns a matrix with the following features: (1) the number of rows and columns is 1 less than that of the original matrix; (2)the matrix is triangular, i.e.
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

calculateSubMatrix

private static double[][] calculateSubMatrix(double[][] matrix)

Returns a matrix with the following features: (1) the number of rows and columns is 1 less than that of the original matrix; (2)the matrix is triangular, i.e. all elements a (row, column) with column > row are zero. This method is used for calculating a polynomial regression.

Parameters:

matrix - the start matrix.

Returns:

The new matrix.