org.apache.commons.math.stat.regression
Class AbstractMultipleLinearRegression

java.lang.Object
  org.apache.commons.math.stat.regression.AbstractMultipleLinearRegression

All Implemented Interfaces:

MultipleLinearRegression

Direct Known Subclasses:

GLSMultipleLinearRegression, OLSMultipleLinearRegression

public abstract class AbstractMultipleLinearRegression
extends Object
implements MultipleLinearRegression

Abstract base class for implementations of MultipleLinearRegression.

Since:

2.0

Version:

$Revision: 811685 $ $Date: 2009-09-05 13:36:48 -0400 (Sat, 05 Sep 2009) $

Field Summary

protected RealMatrix	X X sample data.
protected RealVector	Y Y sample data.

Constructor Summary

AbstractMultipleLinearRegression()

Method Summary

protected abstract RealVector	calculateBeta() Calculates the beta of multiple linear regression in matrix notation.
protected abstract RealMatrix	calculateBetaVariance() Calculates the beta variance of multiple linear regression in matrix notation.
protected RealVector	calculateResiduals() Calculates the residuals of multiple linear regression in matrix notation.
protected abstract double	calculateYVariance() Calculates the Y variance of multiple linear regression.
double	estimateRegressandVariance() Returns the variance of the regressand, ie Var(y).
double[]	estimateRegressionParameters() Estimates the regression parameters b.
double[]	estimateRegressionParametersStandardErrors() Returns the standard errors of the regression parameters.
double[][]	estimateRegressionParametersVariance() Estimates the variance of the regression parameters, ie Var(b).
double[]	estimateResiduals() Estimates the residuals, ie u = y - X*b.
void	newSampleData(double[] data, int nobs, int nvars) Loads model x and y sample data from a flat array of data, overriding any previous sample.
protected void	newXSampleData(double[][] x) Loads new x sample data, overriding any previous sample
protected void	newYSampleData(double[] y) Loads new y sample data, overriding any previous sample
protected void	validateCovarianceData(double[][] x, double[][] covariance) Validates sample data.
protected void	validateSampleData(double[][] x, double[] y) Validates sample data.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

X

protected RealMatrix X

X sample data.

Y

protected RealVector Y

Y sample data.

Constructor Detail

AbstractMultipleLinearRegression

public AbstractMultipleLinearRegression()

Method Detail

newSampleData

public void newSampleData(double[] data,
                          int nobs,
                          int nvars)

Loads model x and y sample data from a flat array of data, overriding any previous sample. Assumes that rows are concatenated with y values first in each row.

Parameters:

data - input data array

nobs - number of observations (rows)

nvars - number of independent variables (columns, not counting y)

newYSampleData

protected void newYSampleData(double[] y)

Loads new y sample data, overriding any previous sample

Parameters:

y - the [n,1] array representing the y sample

newXSampleData

protected void newXSampleData(double[][] x)

Loads new x sample data, overriding any previous sample

Parameters:

x - the [n,k] array representing the x sample

validateSampleData

protected void validateSampleData(double[][] x,
                                  double[] y)

Validates sample data.

Parameters:

x - the [n,k] array representing the x sample

y - the [n,1] array representing the y sample

Throws:

IllegalArgumentException - if the x and y array data are not compatible for the regression

validateCovarianceData

protected void validateCovarianceData(double[][] x,
                                      double[][] covariance)

Validates sample data.

Parameters:

x - the [n,k] array representing the x sample

covariance - the [n,n] array representing the covariance matrix

Throws:

IllegalArgumentException - if the x sample data or covariance matrix are not compatible for the regression

estimateRegressionParameters

public double[] estimateRegressionParameters()

Estimates the regression parameters b.

Specified by:

estimateRegressionParameters in interface MultipleLinearRegression

Returns:

The [k,1] array representing b

estimateResiduals

public double[] estimateResiduals()

Estimates the residuals, ie u = y - X*b.

Specified by:

estimateResiduals in interface MultipleLinearRegression

Returns:

The [n,1] array representing the residuals

estimateRegressionParametersVariance

public double[][] estimateRegressionParametersVariance()

Estimates the variance of the regression parameters, ie Var(b).

Specified by:

estimateRegressionParametersVariance in interface MultipleLinearRegression

Returns:

The [k,k] array representing the variance of b

estimateRegressionParametersStandardErrors

public double[] estimateRegressionParametersStandardErrors()

Returns the standard errors of the regression parameters.

Specified by:

estimateRegressionParametersStandardErrors in interface MultipleLinearRegression

Returns:

standard errors of estimated regression parameters

estimateRegressandVariance

public double estimateRegressandVariance()

Returns the variance of the regressand, ie Var(y).

Specified by:

estimateRegressandVariance in interface MultipleLinearRegression

Returns:

The double representing the variance of y

calculateBeta

protected abstract RealVector calculateBeta()

Calculates the beta of multiple linear regression in matrix notation.

Returns:

beta

calculateBetaVariance

protected abstract RealMatrix calculateBetaVariance()

Calculates the beta variance of multiple linear regression in matrix notation.

Returns:

beta variance

calculateYVariance

protected abstract double calculateYVariance()

Calculates the Y variance of multiple linear regression.

Returns:

Y variance

calculateResiduals

protected RealVector calculateResiduals()

Calculates the residuals of multiple linear regression in matrix notation.

 u = y - X * b
 

Returns:

The residuals [n,1] matrix