 de.lmu.ifi.dbs.elki.math.statistics

Class PolynomialRegression

• public class PolynomialRegression
extends MultipleLinearRegression
A polynomial fit is a specific type of multiple regression. The simple regression model (a first-order polynomial) can be trivially extended to higher orders.

The regression model y = b0 + b1*x + b2*x^2 + ... + bp*x^p + e is a system of polynomial equations of order p with polynomial coefficients { b0 ... bp}. The model can be expressed using data matrix x, target double[] y and parameter double[] ?. The ith row of X and Y will contain the x and y value for the ith data sample.

The variables will be transformed in the following way: x => x1, ..., x^p => xp Then the model can be written as a multiple linear equation model: y = b0 + b1*x1 + b2*x2 + ... + bp*xp + e

Since:
0.1
Author:
Elke Achtert
• Field Detail

• p

public final int p
The order of the polynom.
• Constructor Detail

• PolynomialRegression

public PolynomialRegression(double[] y,
double[] x,
int p)
Constructor.
Parameters:
y - the (n x 1) - double[] holding the response values (y1, ..., yn)^T.
x - the (n x 1)-double[] holding the x-values (x1, ..., xn)^T.
p - the order of the polynom.
• Method Detail

• xMatrix

private static double[][] xMatrix(double[] x,
int p)

Returns the adapted coefficient of determination
Returns:
the adapted coefficient of determination
• estimateY

public double estimateY(double x)
Performs an estimation of y on the specified x value.
Parameters:
x - the x-value for which y is estimated
Returns:
the estimation of y