/*
* 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,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.math.stat.regression;
import org.apache.commons.math.linear.Array2DRowRealMatrix;
import org.apache.commons.math.linear.LUDecompositionImpl;
import org.apache.commons.math.linear.QRDecomposition;
import org.apache.commons.math.linear.QRDecompositionImpl;
import org.apache.commons.math.linear.RealMatrix;
import org.apache.commons.math.linear.RealVector;
/**
* <p>Implements ordinary least squares (OLS) to estimate the parameters of a
* multiple linear regression model.</p>
*
* <p>OLS assumes the covariance matrix of the error to be diagonal and with
* equal variance.</p>
* <p>
* u ~ N(0, σ<sup>2</sup>I)
* </p>
*
* <p>The regression coefficients, b, satisfy the normal equations:
* <p>
* X<sup>T</sup> X b = X<sup>T</sup> y
* </p>
*
* <p>To solve the normal equations, this implementation uses QR decomposition
* of the X matrix. (See {@link QRDecompositionImpl} for details on the
* decomposition algorithm.)
* </p>
* <p>X<sup>T</sup>X b = X<sup>T</sup> y <br/>
* (QR)<sup>T</sup> (QR) b = (QR)<sup>T</sup>y <br/>
* R<sup>T</sup> (Q<sup>T</sup>Q) R b = R<sup>T</sup> Q<sup>T</sup> y <br/>
* R<sup>T</sup> R b = R<sup>T</sup> Q<sup>T</sup> y <br/>
* (R<sup>T</sup>)<sup>-1</sup> R<sup>T</sup> R b = (R<sup>T</sup>)<sup>-1</sup> R<sup>T</sup> Q<sup>T</sup> y <br/>
* R b = Q<sup>T</sup> y
* </p>
* Given Q and R, the last equation is solved by back-subsitution.</p>
*
* @version $Revision: 825925 $ $Date: 2009-10-16 11:11:47 -0400 (Fri, 16 Oct 2009) $
* @since 2.0
*/
public class OLSMultipleLinearRegression extends AbstractMultipleLinearRegression {
/** Cached QR decomposition of X matrix */
private QRDecomposition qr = null;
/**
* Loads model x and y sample data, overriding any previous sample.
*
* Computes and caches QR decomposition of the X matrix.
* @param y the [n,1] array representing the y sample
* @param x the [n,k] array representing the x sample
* @throws IllegalArgumentException if the x and y array data are not
* compatible for the regression
*/
public void newSampleData(double[] y, double[][] x) {
validateSampleData(x, y);
newYSampleData(y);
newXSampleData(x);
}
/**
* {@inheritDoc}
*
* Computes and caches QR decomposition of the X matrix
*/
@Override
public void newSampleData(double[] data, int nobs, int nvars) {
super.newSampleData(data, nobs, nvars);
qr = new QRDecompositionImpl(X);
}
/**
* <p>Compute the "hat" matrix.
* </p>
* <p>The hat matrix is defined in terms of the design matrix X
* by X(X<sup>T</sup>X)<sup>-1</sup>X<sup>T</sup>
* </p>
* <p>The implementation here uses the QR decomposition to compute the
* hat matrix as Q I<sub>p</sub>Q<sup>T</sup> where I<sub>p</sub> is the
* p-dimensional identity matrix augmented by 0's. This computational
* formula is from "The Hat Matrix in Regression and ANOVA",
* David C. Hoaglin and Roy E. Welsch,
* <i>The American Statistician</i>, Vol. 32, No. 1 (Feb., 1978), pp. 17-22.
*
* @return the hat matrix
*/
public RealMatrix calculateHat() {
// Create augmented identity matrix
RealMatrix Q = qr.getQ();
final int p = qr.getR().getColumnDimension();
final int n = Q.getColumnDimension();
Array2DRowRealMatrix augI = new Array2DRowRealMatrix(n, n);
double[][] augIData = augI.getDataRef();
for (int i = 0; i < n; i++) {
for (int j =0; j < n; j++) {
if (i == j && i < p) {
augIData[i][j] = 1d;
} else {
augIData[i][j] = 0d;
}
}
}
// Compute and return Hat matrix
return Q.multiply(augI).multiply(Q.transpose());
}
/**
* Loads new x sample data, overriding any previous sample
*
* @param x the [n,k] array representing the x sample
*/
@Override
protected void newXSampleData(double[][] x) {
this.X = new Array2DRowRealMatrix(x);
qr = new QRDecompositionImpl(X);
}
/**
* Calculates regression coefficients using OLS.
*
* @return beta
*/
@Override
protected RealVector calculateBeta() {
return qr.getSolver().solve(Y);
}
/**
* <p>Calculates the variance on the beta by OLS.
* </p>
* <p>Var(b) = (X<sup>T</sup>X)<sup>-1</sup>
* </p>
* <p>Uses QR decomposition to reduce (X<sup>T</sup>X)<sup>-1</sup>
* to (R<sup>T</sup>R)<sup>-1</sup>, with only the top p rows of
* R included, where p = the length of the beta vector.</p>
*
* @return The beta variance
*/
@Override
protected RealMatrix calculateBetaVariance() {
int p = X.getColumnDimension();
RealMatrix Raug = qr.getR().getSubMatrix(0, p - 1 , 0, p - 1);
RealMatrix Rinv = new LUDecompositionImpl(Raug).getSolver().getInverse();
return Rinv.multiply(Rinv.transpose());
}
/**
* <p>Calculates the variance on the Y by OLS.
* </p>
* <p> Var(y) = Tr(u<sup>T</sup>u)/(n - k)
* </p>
* @return The Y variance
*/
@Override
protected double calculateYVariance() {
RealVector residuals = calculateResiduals();
return residuals.dotProduct(residuals) /
(X.getRowDimension() - X.getColumnDimension());
}
}