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Java example source code file (GLSMultipleLinearRegression.java)

This example Java source code file (GLSMultipleLinearRegression.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

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Java - Java tags/keywords

abstractmultiplelinearregression, array2drowrealmatrix, glsmultiplelinearregression, omegainverse, override, realmatrix, realvector, xtoix

The GLSMultipleLinearRegression.java Java example source code

 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *      http://www.apache.org/licenses/LICENSE-2.0
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * See the License for the specific language governing permissions and
 * limitations under the License.
package org.apache.commons.math3.stat.regression;

import org.apache.commons.math3.linear.LUDecomposition;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.RealVector;

 * The GLS implementation of multiple linear regression.
 * GLS assumes a general covariance matrix Omega of the error
 * <pre>
 * u ~ N(0, Omega)
 * </pre>
 * Estimated by GLS,
 * <pre>
 * b=(X' Omega^-1 X)^-1X'Omega^-1 y
 * </pre>
 * whose variance is
 * <pre>
 * Var(b)=(X' Omega^-1 X)^-1
 * </pre>
 * @since 2.0
public class GLSMultipleLinearRegression extends AbstractMultipleLinearRegression {

    /** Covariance matrix. */
    private RealMatrix Omega;

    /** Inverse of covariance matrix. */
    private RealMatrix OmegaInverse;

    /** Replace sample data, overriding any previous sample.
     * @param y y values of the sample
     * @param x x values of the sample
     * @param covariance array representing the covariance matrix
    public void newSampleData(double[] y, double[][] x, double[][] covariance) {
        validateSampleData(x, y);
        validateCovarianceData(x, covariance);

     * Add the covariance data.
     * @param omega the [n,n] array representing the covariance
    protected void newCovarianceData(double[][] omega){
        this.Omega = new Array2DRowRealMatrix(omega);
        this.OmegaInverse = null;

     * Get the inverse of the covariance.
     * <p>The inverse of the covariance matrix is lazily evaluated and cached.

* @return inverse of the covariance */ protected RealMatrix getOmegaInverse() { if (OmegaInverse == null) { OmegaInverse = new LUDecomposition(Omega).getSolver().getInverse(); } return OmegaInverse; } /** * Calculates beta by GLS. * <pre> * b=(X' Omega^-1 X)^-1X'Omega^-1 y * </pre> * @return beta */ @Override protected RealVector calculateBeta() { RealMatrix OI = getOmegaInverse(); RealMatrix XT = getX().transpose(); RealMatrix XTOIX = XT.multiply(OI).multiply(getX()); RealMatrix inverse = new LUDecomposition(XTOIX).getSolver().getInverse(); return inverse.multiply(XT).multiply(OI).operate(getY()); } /** * Calculates the variance on the beta. * <pre> * Var(b)=(X' Omega^-1 X)^-1 * </pre> * @return The beta variance matrix */ @Override protected RealMatrix calculateBetaVariance() { RealMatrix OI = getOmegaInverse(); RealMatrix XTOIX = getX().transpose().multiply(OI).multiply(getX()); return new LUDecomposition(XTOIX).getSolver().getInverse(); } /** * Calculates the estimated variance of the error term using the formula * <pre> * Var(u) = Tr(u' Omega^-1 u)/(n-k) * </pre> * where n and k are the row and column dimensions of the design * matrix X. * * @return error variance * @since 2.2 */ @Override protected double calculateErrorVariance() { RealVector residuals = calculateResiduals(); double t = residuals.dotProduct(getOmegaInverse().operate(residuals)); return t / (getX().getRowDimension() - getX().getColumnDimension()); } }

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