/* * GLMSubstitutionModel.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.oldevomodel.substmodel; import dr.evolution.datatype.DataType; import dr.inference.distribution.LogLinearModel; import dr.inference.loggers.LogColumn; import dr.inference.model.BayesianStochasticSearchVariableSelection; import dr.inference.model.Model; import dr.util.Citation; import dr.util.CommonCitations; import java.util.Collections; import java.util.List; /** * @author Marc A. Suchard */ public class GLMSubstitutionModel extends ComplexSubstitutionModel { public GLMSubstitutionModel(String name, DataType dataType, FrequencyModel rootFreqModel, LogLinearModel glm) { super(name, dataType, rootFreqModel, null); this.glm = glm; addModel(glm); testProbabilities = new double[stateCount*stateCount]; } public double[] getRates() { return glm.getXBeta(); } protected void handleModelChangedEvent(Model model, Object object, int index) { if (model == glm) { updateMatrix = true; fireModelChanged(); } else super.handleModelChangedEvent(model,object,index); } public LogColumn[] getColumns() { return glm.getColumns(); } public double getLogLikelihood() { double logL = super.getLogLikelihood(); if (logL == 0 && BayesianStochasticSearchVariableSelection.Utils.connectedAndWellConditioned(testProbabilities,this)) { // Also check that graph is connected return 0; } return Double.NEGATIVE_INFINITY; } @Override public String getDescription() { return "Generalized linear (model, GLM) substitution model"; // TODO Horrible; fix } @Override public List<Citation> getCitations() { return Collections.singletonList(CommonCitations.LEMEY_2014_UNIFYING); } private LogLinearModel glm; private double[] testProbabilities; }