Examples with TreePriorParameterizationType dr.app.beauti.types.TreePriorParameterizationType used on opensource projects

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Example 1 with TreePriorParameterizationType

use of dr.app.beauti.types.TreePriorParameterizationType in project beast-mcmc by beast-dev.

the class TreePriorGenerator method writeTreePriorModel.

//    void writeTreePrior(PartitionTreePrior prior, PartitionTreeModel model, XMLWriter writer) {    // for species, partitionName.treeModel
//        setModelPrefix(prior.getPrefix()); // only has prefix, if (options.getPartitionTreePriors().size() > 1)
//
//        writePriorLikelihood(prior, model, writer);
//    }
/**
     * Write a tree prior (coalescent or speciational) model
     *
     * @param prior  the partition tree prior
     * @param writer the writer
     */
void writeTreePriorModel(PartitionTreePrior prior, XMLWriter writer) {
    // only has prefix, if (options.getPartitionTreePriors().size() > 1)
    setModelPrefix(prior.getPrefix());
    String initialPopSize = null;
    TreePriorType nodeHeightPrior = prior.getNodeHeightPrior();
    Units.Type units = options.units;
    TreePriorParameterizationType parameterization = prior.getParameterization();
    switch(nodeHeightPrior) {
        case CONSTANT:
            writer.writeComment("A prior assumption that the population size has remained constant", "throughout the time spanned by the genealogy.");
            writer.writeOpenTag(ConstantPopulationModelParser.CONSTANT_POPULATION_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + "constant"), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(options.units)) });
            writer.writeOpenTag(ConstantPopulationModelParser.POPULATION_SIZE);
            writeParameter("constant.popSize", prior, writer);
            writer.writeCloseTag(ConstantPopulationModelParser.POPULATION_SIZE);
            writer.writeCloseTag(ConstantPopulationModelParser.CONSTANT_POPULATION_MODEL);
            break;
        case EXPONENTIAL:
            // generate an exponential prior tree
            writer.writeComment("A prior assumption that the population size has grown exponentially", "throughout the time spanned by the genealogy.");
            writer.writeOpenTag(ExponentialGrowthModelParser.EXPONENTIAL_GROWTH_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + "exponential"), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(options.units)) });
            // write pop size socket
            writer.writeOpenTag(ExponentialGrowthModelParser.POPULATION_SIZE);
            writeParameter("exponential.popSize", prior, writer);
            writer.writeCloseTag(ExponentialGrowthModelParser.POPULATION_SIZE);
            if (parameterization == TreePriorParameterizationType.GROWTH_RATE) {
                // write growth rate socket
                writer.writeOpenTag(ExponentialGrowthModelParser.GROWTH_RATE);
                writeParameter("exponential.growthRate", prior, writer);
                writer.writeCloseTag(ExponentialGrowthModelParser.GROWTH_RATE);
            } else {
                // write doubling time socket
                writer.writeOpenTag(ExponentialGrowthModelParser.DOUBLING_TIME);
                writeParameter("exponential.doublingTime", prior, writer);
                writer.writeCloseTag(ExponentialGrowthModelParser.DOUBLING_TIME);
            }
            writer.writeCloseTag(ExponentialGrowthModelParser.EXPONENTIAL_GROWTH_MODEL);
            break;
        case LOGISTIC:
            // generate an exponential prior tree
            writer.writeComment("A prior assumption that the population size has grown logistically", "throughout the time spanned by the genealogy.");
            writer.writeOpenTag(LogisticGrowthModelParser.LOGISTIC_GROWTH_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + "logistic"), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(options.units)) });
            // write pop size socket
            writer.writeOpenTag(LogisticGrowthModelParser.POPULATION_SIZE);
            writeParameter("logistic.popSize", prior, writer);
            writer.writeCloseTag(LogisticGrowthModelParser.POPULATION_SIZE);
            if (parameterization == TreePriorParameterizationType.GROWTH_RATE) {
                // write growth rate socket
                writer.writeOpenTag(LogisticGrowthModelParser.GROWTH_RATE);
                writeParameter("logistic.growthRate", prior, writer);
                writer.writeCloseTag(LogisticGrowthModelParser.GROWTH_RATE);
            } else {
                // write doubling time socket
                writer.writeOpenTag(LogisticGrowthModelParser.DOUBLING_TIME);
                writeParameter("logistic.doublingTime", prior, writer);
                writer.writeCloseTag(LogisticGrowthModelParser.DOUBLING_TIME);
            }
            // write logistic t50 socket
            writer.writeOpenTag(LogisticGrowthModelParser.TIME_50);
            //	            if (options.clockModelOptions.getRateOptionClockModel() == FixRateType.FIX_MEAN
            //	        			|| options.clockModelOptions.getRateOptionClockModel() == FixRateType.RELATIVE_TO) {
            //            		writer.writeComment("No calibration");
            //                writer.writeComment("logistic.t50 initial always has to < treeRootHeight initial");
            //                dr.app.beauti.options.Parameter priorPara = prior.getParameter("logistic.t50");
            //
            //                double initRootHeight;
            //                if (options.isShareSameTreePrior()) {
            //                    initRootHeight = priorPara.initial;
            //                    for (PartitionTreeModel tree : options.getPartitionTreeModels()) {
            //                        double tmpRootHeight = tree.getParameter("treeModel.rootHeight").initial;
            //                        if (initRootHeight > tmpRootHeight) { // take min
            //                            initRootHeight = tmpRootHeight;
            //                        }
            //                    }
            //                } else {
            //                    initRootHeight = prior.getTreeModel().getParameter("treeModel.rootHeight").initial;
            //                }
            //                // logistic.t50 initial always has to < treeRootHeight initial
            //                if (priorPara.initial >= initRootHeight) {
            //                    priorPara.initial = initRootHeight / 2; // tree prior.initial has to < treeRootHeight.initial
            //                }
            //	            } else {
            //	            	writer.writeComment("Has calibration");
            //
            //	            	throw new IllegalArgumentException("This function is not available in this release !");
            //	            }
            writeParameter("logistic.t50", prior, writer);
            writer.writeCloseTag(LogisticGrowthModelParser.TIME_50);
            writer.writeCloseTag(LogisticGrowthModelParser.LOGISTIC_GROWTH_MODEL);
            initialPopSize = "logistic.popSize";
            break;
        case EXPANSION:
            // generate an exponential prior tree
            writer.writeComment("A prior assumption that the population size has grown exponentially", "from some ancestral population size in the past.");
            writer.writeOpenTag(ExpansionModelParser.EXPANSION_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + "expansion"), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(options.units)) });
            // write pop size socket
            writeParameter(ExpansionModelParser.POPULATION_SIZE, "expansion.popSize", prior, writer);
            if (parameterization == TreePriorParameterizationType.GROWTH_RATE) {
                // write growth rate socket
                writeParameter(ExpansionModelParser.GROWTH_RATE, "expansion.growthRate", prior, writer);
            } else {
                // write doubling time socket
                writeParameter(ExpansionModelParser.DOUBLING_TIME, "expansion.doublingTime", prior, writer);
            }
            // write ancestral proportion socket
            writeParameter(ExpansionModelParser.ANCESTRAL_POPULATION_PROPORTION, "expansion.ancestralProportion", prior, writer);
            writer.writeCloseTag(ExpansionModelParser.EXPANSION_MODEL);
            initialPopSize = "expansion.popSize";
            break;
        case YULE:
        case YULE_CALIBRATION:
            if (nodeHeightPrior == TreePriorType.YULE_CALIBRATION) {
                writer.writeComment("Calibrated Yule: Heled J, Drummond AJ (2011), Syst Biol, doi: " + "10.1093/sysbio/syr087");
            } else {
                writer.writeComment("A prior on the distribution node heights defined given", "a Yule speciation process (a pure birth process).");
            }
            writer.writeOpenTag(YuleModelParser.YULE_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + YuleModelParser.YULE), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(units)) });
            writeParameter(YuleModelParser.BIRTH_RATE, "yule.birthRate", prior, writer);
            writer.writeCloseTag(YuleModelParser.YULE_MODEL);
            break;
        case BIRTH_DEATH:
        case BIRTH_DEATH_INCOMPLETE_SAMPLING:
            writer.writeComment("A prior on the distribution node heights defined given");
            writer.writeComment(nodeHeightPrior == TreePriorType.BIRTH_DEATH_INCOMPLETE_SAMPLING ? BirthDeathModelParser.getCitationRHO() : BirthDeathModelParser.getCitation());
            writer.writeOpenTag(BirthDeathModelParser.BIRTH_DEATH_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + BirthDeathModelParser.BIRTH_DEATH), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(units)) });
            writeParameter(BirthDeathModelParser.BIRTHDIFF_RATE, BirthDeathModelParser.MEAN_GROWTH_RATE_PARAM_NAME, prior, writer);
            writeParameter(BirthDeathModelParser.RELATIVE_DEATH_RATE, BirthDeathModelParser.RELATIVE_DEATH_RATE_PARAM_NAME, prior, writer);
            if (nodeHeightPrior == TreePriorType.BIRTH_DEATH_INCOMPLETE_SAMPLING) {
                writeParameter(BirthDeathModelParser.SAMPLE_PROB, BirthDeathModelParser.BIRTH_DEATH + "." + BirthDeathModelParser.SAMPLE_PROB, prior, writer);
            }
            writer.writeCloseTag(BirthDeathModelParser.BIRTH_DEATH_MODEL);
            break;
        case BIRTH_DEATH_SERIAL_SAMPLING:
            writer.writeComment(BirthDeathSerialSamplingModelParser.getCitationPsiOrg());
            writer.writeOpenTag(BirthDeathSerialSamplingModelParser.BIRTH_DEATH_SERIAL_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + BirthDeathSerialSamplingModelParser.BDSS), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(units)), new Attribute.Default<Boolean>(BirthDeathSerialSamplingModelParser.HAS_FINAL_SAMPLE, false) });
            writeParameter(BirthDeathSerialSamplingModelParser.LAMBDA, BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.LAMBDA, prior, writer);
            writeParameter(BirthDeathSerialSamplingModelParser.RELATIVE_MU, BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.RELATIVE_MU, prior, writer);
            //                writeParameter(BirthDeathSerialSamplingModelParser.SAMPLE_PROBABILITY,
            //                        BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.SAMPLE_PROBABILITY, prior, writer);
            writeParameter(BirthDeathSerialSamplingModelParser.PSI, BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.PSI, prior, writer);
            writeParameter(BirthDeathSerialSamplingModelParser.ORIGIN, BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.ORIGIN, prior, writer);
            writer.writeCloseTag(BirthDeathSerialSamplingModelParser.BIRTH_DEATH_SERIAL_MODEL);
            break;
        case BIRTH_DEATH_BASIC_REPRODUCTIVE_NUMBER:
            writer.writeComment(BirthDeathSerialSamplingModelParser.getCitationRT());
            writer.writeOpenTag(BirthDeathEpidemiologyModelParser.BIRTH_DEATH_EPIDEMIOLOGY, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + BirthDeathEpidemiologyModelParser.BIRTH_DEATH_EPIDEMIOLOGY), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(units)) });
            writeParameter(BirthDeathEpidemiologyModelParser.R0, BirthDeathEpidemiologyModelParser.R0, prior, writer);
            writeParameter(BirthDeathEpidemiologyModelParser.RECOVERY_RATE, BirthDeathEpidemiologyModelParser.RECOVERY_RATE, prior, writer);
            writeParameter(BirthDeathEpidemiologyModelParser.SAMPLING_PROBABILITY, BirthDeathEpidemiologyModelParser.SAMPLING_PROBABILITY, prior, writer);
            writeParameter(BirthDeathEpidemiologyModelParser.ORIGIN, BirthDeathEpidemiologyModelParser.ORIGIN, prior, writer);
            writer.writeCloseTag(BirthDeathEpidemiologyModelParser.BIRTH_DEATH_EPIDEMIOLOGY);
            break;
        case SPECIES_BIRTH_DEATH:
        case SPECIES_YULE:
        case SPECIES_YULE_CALIBRATION:
            writer.writeComment("A prior assumption that the population size has remained constant");
            writer.writeComment("throughout the time spanned by the genealogy.");
            if (nodeHeightPrior == TreePriorType.SPECIES_YULE_CALIBRATION)
                writer.writeComment("Calibrated Yule: Heled J, Drummond AJ (2011), Syst Biol, doi: " + "10.1093/sysbio/syr087");
            writer.writeOpenTag(ConstantPopulationModelParser.CONSTANT_POPULATION_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + "constant"), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(options.units)) });
            // initial value for pop mean is the same as what used to be the value for the population size
            Parameter para = options.starBEASTOptions.getParameter(TraitData.TRAIT_SPECIES + "." + options.starBEASTOptions.POP_MEAN);
            prior.getParameter("constant.popSize").setInitial(para.getInitial());
            writer.writeOpenTag(ConstantPopulationModelParser.POPULATION_SIZE);
            writeParameter("constant.popSize", prior, writer);
            writer.writeCloseTag(ConstantPopulationModelParser.POPULATION_SIZE);
            writer.writeCloseTag(ConstantPopulationModelParser.CONSTANT_POPULATION_MODEL);
            break;
    }
    if ((!options.useStarBEAST) && nodeHeightPrior != TreePriorType.CONSTANT && nodeHeightPrior != TreePriorType.EXPONENTIAL) {
        // If the node height prior is not one of these two then we need to simulate a
        // random starting tree under a constant size coalescent.
        writer.writeComment("This is a simple constant population size coalescent model", "that is used to generate an initial tree for the chain.");
        writer.writeOpenTag(ConstantPopulationModelParser.CONSTANT_POPULATION_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + "initialDemo"), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(units)) });
        writer.writeOpenTag(ConstantPopulationModelParser.POPULATION_SIZE);
        if (initialPopSize != null) {
            writer.writeIDref(ParameterParser.PARAMETER, modelPrefix + initialPopSize);
        } else {
            writeParameter(modelPrefix + "initialDemo.popSize", 1, 100.0, Double.NaN, Double.NaN, writer);
        }
        writer.writeCloseTag(ConstantPopulationModelParser.POPULATION_SIZE);
        writer.writeCloseTag(ConstantPopulationModelParser.CONSTANT_POPULATION_MODEL);
    }
//        if (nodeHeightPrior == TreePriorType.BIRTH_DEATH_BASIC_REPRODUCTIVE_NUMBER) {
//            writer.writeComment("R0 = b/(b*d+s*r)");
//            writer.writeOpenTag(RPNcalculatorStatisticParser.RPN_STATISTIC,
//                    new Attribute[]{
//                            new Attribute.Default<String>(XMLParser.ID, modelPrefix + "R0")
//                    });
//
//            writer.writeOpenTag(RPNcalculatorStatisticParser.VARIABLE,
//                    new Attribute[]{
//                            new Attribute.Default<String>(Statistic.NAME, modelPrefix + "b")
//                    });
//            writeParameterRef(modelPrefix + BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.LAMBDA, writer);
//            writer.writeCloseTag(RPNcalculatorStatisticParser.VARIABLE);
//
//            writer.writeOpenTag(RPNcalculatorStatisticParser.VARIABLE,
//                    new Attribute[]{
//                            new Attribute.Default<String>(Statistic.NAME, modelPrefix + "d")
//                    });
//            writeParameterRef(modelPrefix + BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.RELATIVE_MU, writer);
//            writer.writeCloseTag(RPNcalculatorStatisticParser.VARIABLE);
//
//            writer.writeOpenTag(RPNcalculatorStatisticParser.VARIABLE,
//                    new Attribute[]{
//                            new Attribute.Default<String>(Statistic.NAME, modelPrefix + "s")
//                    });
//            writeParameterRef(modelPrefix + BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.PSI, writer);
//            writer.writeCloseTag(RPNcalculatorStatisticParser.VARIABLE);
//
//            writer.writeOpenTag(RPNcalculatorStatisticParser.VARIABLE,
//                    new Attribute[]{
//                            new Attribute.Default<String>(Statistic.NAME, modelPrefix + "r")
//                    });
//            writeParameterRef(modelPrefix + BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.R, writer);
//            writer.writeCloseTag(RPNcalculatorStatisticParser.VARIABLE);
//
//            writer.writeOpenTag(RPNcalculatorStatisticParser.EXPRESSION,
//                    new Attribute[]{
//                            new Attribute.Default<String>(Statistic.NAME, modelPrefix + "R0")
//                    });
//            writer.writeText(modelPrefix + "b " + modelPrefix + "b " + modelPrefix + "d " + "* " + modelPrefix + "s " + modelPrefix + "r " + "* + /");
//            writer.writeCloseTag(RPNcalculatorStatisticParser.EXPRESSION);
//
//            writer.writeCloseTag(RPNcalculatorStatisticParser.RPN_STATISTIC);
//        }
}
Also used : TreePriorParameterizationType(dr.app.beauti.types.TreePriorParameterizationType) Attribute(dr.util.Attribute) TreePriorType(dr.app.beauti.types.TreePriorType) Units(dr.evolution.util.Units)

Aggregations

TreePriorParameterizationType (dr.app.beauti.types.TreePriorParameterizationType)1 TreePriorType (dr.app.beauti.types.TreePriorType)1 Units (dr.evolution.util.Units)1 Attribute (dr.util.Attribute)1
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