/*
* LinearRegression.java
*
* Copyright (c) 2002-2015 Alexei Drummond, Andrew Rambaut and Marc Suchard
*
* This file is part of BEAST.
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership and licensing.
*
* BEAST is free software; you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation; either version 2
* of the License, or (at your option) any later version.
*
* BEAST is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with BEAST; if not, write to the
* Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
* Boston, MA 02110-1301 USA
*/
package dr.inference.distribution;
import dr.inference.model.Parameter;
/**
* @author Marc Suchard
*/
@Deprecated // GLM stuff is now in inference.glm - this is here for backwards compatibility temporarily
public class LinearRegression extends GeneralizedLinearModel {
private static final double normalizingConstant = -0.5 * Math.log(2 * Math.PI);
private boolean logTransform = false;
public double[] getTransformedDependentParameter() {
double[] y = dependentParam.getParameterValues();
if (logTransform) {
for(int i=0; i<y.length; i++)
y[i] = Math.log(y[i]);
}
return y;
}
protected double calculateLogLikelihood() {
double logLikelihood = 0;
double[] xBeta = getXBeta();
double[] precision = getScale();
double[] y = getTransformedDependentParameter();
for (int i = 0; i < N; i++) { // assumes that all observations are independent given fixed and random effects
if (logTransform)
logLikelihood -= y[i]; // Jacobian
logLikelihood += 0.5 * Math.log(precision[i]) - 0.5 * (y[i] - xBeta[i]) * (y[i] - xBeta[i]) * precision[i];
}
return N * normalizingConstant + logLikelihood;
}
public LinearRegression(Parameter dependentParam, boolean logTransform) { //, Parameter independentParam, DesignMatrix designMatrix) {
super(dependentParam); //, independentParam, designMatrix);
System.out.println("Constructing a linear regression model");
this.logTransform = logTransform;
}
protected double calculateLogLikelihoodAndGradient(double[] beta, double[] gradient) {
throw new RuntimeException("Optimization not yet implemented.");
}
public boolean requiresScale() {
return true;
}
protected double calculateLogLikelihood(double[] beta) {
throw new RuntimeException("Optimization not yet implemented.");
}
protected boolean confirmIndependentParameters() {
return true;
}
}