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Merge branch 'hmc-clock' of github.com:beast-dev/beast-mcmc into hmc-…
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@afmagee
afmagee committed Sep 22, 2023
2 parents c8af174 + c890dc5 commit 6f5fea6
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215 changes: 215 additions & 0 deletions ci/TestXML/testActualRepeatedMeasures.xml
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<?xml version="1.0" standalone="yes"?>
<beast>
<taxa id="taxa">
<taxon id="taxon1">
<attr name="X">0.6208852031301316 -1.2991371493463548 -2.6910514790332054 1.109417180690863
1.099166772266214 NA
</attr>
</taxon>
<taxon id="taxon2">
<attr name="X">-0.7057152253938193 NA NA NA NA NA</attr>
</taxon>
<taxon id="taxon3">
<attr name="X">0.8225821150789747 0.2138202366939677 -1.5229118225528515 -0.8078400648319927
-1.1594623872491492 0.17048275938533758
</attr>
</taxon>
<taxon id="taxon4">
<attr name="X">1.2335230762207288 0.5070164014346852 -0.8270985241304889 NA NA NA</attr>
</taxon>
<taxon id="taxon5">
<attr name="X">NA NA NA NA NA NA</attr>
</taxon>
</taxa>

<newick id="tree" usingDates="false" usingHeights="true">
(taxon1:0.034093632223924954,((taxon2:1.5104298872950768,(taxon3:0.39480816393853035,taxon4:2.492954320135449):2.920160090709784):0.7911157035133156,taxon5:3.8691942903231844):0.9572420218437911);
</newick>

<treeModel id="treeModel">
<newick idref="tree"/>
<rootHeight>
<parameter id="treeModel.rootHeight"/>
</rootHeight>
<nodeHeights internalNodes="true">
<parameter id="treeModel.internalNodeHeights"/>
</nodeHeights>
<nodeHeights internalNodes="true" rootNode="true">
<parameter id="treeModel.allInternalNodeHeights"/>
</nodeHeights>
<nodeTraits name="X" rootNode="false" internalNodes="false" leafNodes="true" traitDimension="6">
<parameter id="leafTraits"/>
</nodeTraits>
</treeModel>

<multivariateDiffusionModel id="diffusionModel">
<precisionMatrix>
<matrixParameter id="diffusionPrecision">
<parameter value="2.548821185734364 2.555133831152609 0.32188915087110415"/>
<parameter value="2.555133831152609 3.995880672605147 0.6935229186165055"/>
<parameter value="0.32188915087110415 0.6935229186165055 0.8569431001135701"/>
</matrixParameter>
</precisionMatrix>
</multivariateDiffusionModel>


<continuousTraitDataModel id="traits" traitName="X" numTraits="2" standardize="false">
<treeModel idref="treeModel"/>
<traitParameter>
<parameter idref="leafTraits"/>
</traitParameter>
</continuousTraitDataModel>

<repeatedMeasuresModel id="repeatedMeasures" numTraits="1">
<continuousTraitDataModel idref="traits"/>
<!--<samplingPrecision>-->
<!--<parameter id="gamma" value="2 3"/>-->
<!--</samplingPrecision>-->
<samplingPrecision>
<matrixParameter id="residualPrecision">
<parameter value="3.4852474371652304 0.40649861235323664 -2.439744747816666"/>
<parameter value="0.40649861235323664 1.6902922876733173 0.24083326541940484"/>
<parameter value="-2.439744747816666 0.24083326541940484 2.963752826198275"/>
</matrixParameter>
</samplingPrecision>
<multivariateDiffusionModel idref="diffusionModel"/>
</repeatedMeasuresModel>


<traitDataLikelihood id="traitLikelihood" traitName="X" forceFullPrecision="true" scaleByTime="false"
useTreeLength="false">
<multivariateDiffusionModel idref="diffusionModel"/>
<treeModel idref="treeModel"/>
<repeatedMeasuresModel idref="repeatedMeasures"/>
<conjugateRootPrior>
<meanParameter>
<parameter id="mean" value="0.0 0.0 0"/>
</meanParameter>
<priorSampleSize>
<parameter value="0.001"/>
</priorSampleSize>
</conjugateRootPrior>
</traitDataLikelihood>

<matrixInverse id="diffVariance">
<matrixParameter idref="diffusionPrecision"/>
</matrixInverse>

<varianceProportionStatistic id="varianceLog" matrixRatio="coheritability">
<traitDataLikelihood idref="traitLikelihood"/>
<treeModel idref="treeModel"/>
<repeatedMeasuresModel idref="repeatedMeasures"/>
<multivariateDiffusionModel idref="diffusionModel"/>
</varianceProportionStatistic>


<multivariateWishartPrior id="precisionPrior" df="4">
<scaleMatrix>
<matrixParameter>
<parameter value="1 0 0"/>
<parameter value="0 1 0"/>
<parameter value="0 0 1"/>
</matrixParameter>
</scaleMatrix>
<data>
<matrixParameter idref="diffusionPrecision"/>
</data>
</multivariateWishartPrior>


<multivariateWishartPrior id="samplingPrior" df="4">
<scaleMatrix>
<matrixParameter>
<parameter value="1 0 0"/>
<parameter value="0 1 0"/>
<parameter value="0 0 1"/>
</matrixParameter>
</scaleMatrix>
<data>
<matrixParameter idref="residualPrecision"/>
</data>
</multivariateWishartPrior>

<precisionGibbsOperator id="diffOp" weight="1">
<wishartStatistics id="treeWishartStatistics" traitName="X">
<traitDataLikelihood idref="traitLikelihood"/>
</wishartStatistics>
<multivariateWishartPrior idref="precisionPrior"/>
</precisionGibbsOperator>

<precisionGibbsOperator id="resOp" weight="1">
<repeatedMeasuresWishartStatistics forceResample="false">
<repeatedMeasuresModel idref="repeatedMeasures"/>
<traitDataLikelihood idref="traitLikelihood"/>
</repeatedMeasuresWishartStatistics>
<multivariateWishartPrior idref="samplingPrior"/>
</precisionGibbsOperator>

<operators id="operators" optimizationSchedule="log">

<compoundPrecisionOperator weight="1">
<diffusionOperator>
<precisionGibbsOperator idref="diffOp"/>
</diffusionOperator>
<residualOperator>
<precisionGibbsOperator idref="resOp"/>
</residualOperator>
</compoundPrecisionOperator>
</operators>

<!-- <report>-->
<!-- <likelihood>-->
<!-- <traitDataLikelihood idref="traitLikelihood"/>-->
<!-- </likelihood>-->
<!-- </report>-->


<assertEqual tolerance="1e-10" verbose="true">
<message>
Check log likelihood of observed data
</message>
<actual regex="likelihood:\s+(.*)\n">
<likelihood>
<traitDataLikelihood idref="traitLikelihood"/>
</likelihood>
</actual>
<expected>
-42.01874315757184
</expected>
</assertEqual>

<mcmc id="mcmc" chainLength="1" autoOptimize="true">
<posterior id="posterior">
<prior id="prior">
<multivariateWishartPrior idref="samplingPrior"/>
<multivariateWishartPrior idref="precisionPrior"/>
</prior>
<likelihood>
<traitDataLikelihood idref="traitLikelihood"/>
</likelihood>
</posterior>
<operators idref="operators"/>
<log id="screenLog" logEvery="1">
<column label="Posterior" dp="4" width="12">
<posterior idref="posterior"/>
</column>
<column label="Prior" dp="4" width="12">
<posterior idref="prior"/>
</column>
<column label="Likelihood" dp="4" width="12">
<posterior idref="traitLikelihood"/>
</column>
</log>
<log id="fileLog" logEvery="100" fileName="precision_log.log">
<posterior idref="posterior"/>
<prior idref="prior"/>
<likelihood idref="traitLikelihood"/>
<matrixParameter idref="residualPrecision"/>
<matrixParameter idref="diffusionPrecision"/>
<variancePorportionStatistic idref="varianceLog"/>
<matrixInverse idref="diffVariance"/>
</log>
</mcmc>

</beast>

105 changes: 105 additions & 0 deletions ci/TestXML/testBlombergKStatistic.xml
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<?xml version="1.0" standalone="yes"?>
<beast>
<taxa id="taxa">
<taxon id="A">
<attr name="X">1.60411326 -0.09262507</attr>
</taxon>
<taxon id="B">
<attr name="X">0.02369208 1.06519200</attr>
</taxon>
<taxon id="C">
<attr name="X">-1.4149805 -0.6334667</attr>
</taxon>
<taxon id="D">
<attr name="X">-1.1346685 -0.2332645</attr>
</taxon>
<taxon id="E">
<attr name="X">-1.3531916 0.8481068</attr>
</taxon>
</taxa>

<newick id="tree" usingDates="false" usingHeights="true">
(C:0.3213803857,((B:0.1327716981,E:0.9665925966):0.7209950339,(A:0.5044368715,D:0.7394543779):0.9151846841):0.7891438203);
</newick>

<treeModel id="treeModel">
<newick idref="tree"/>
<rootHeight>
<parameter id="treeModel.rootHeight"/>
</rootHeight>
<nodeHeights internalNodes="true">
<parameter id="treeModel.internalNodeHeights"/>
</nodeHeights>
<nodeHeights internalNodes="true" rootNode="true">
<parameter id="treeModel.allInternalNodeHeights"/>
</nodeHeights>
<nodeTraits name="X" rootNode="false" internalNodes="false" leafNodes="true" traitDimension="2">
<parameter id="leafTraits"/>
</nodeTraits>
</treeModel>

<multivariateDiffusionModel id="diffusionModel">
<precisionMatrix>
<matrixParameter id="precisionMatrix">
<parameter id="prec.col1" value="1.0 0.0"/>
<parameter id="prec.col2" value="0.0 1.0"/>
</matrixParameter>
</precisionMatrix>
</multivariateDiffusionModel>


<traitDataLikelihood id="traitLikelihood" traitName="X" forceFullPrecision="true" scaleByTime="true"
useTreeLength="false">
<multivariateDiffusionModel idref="diffusionModel"/>
<treeModel idref="treeModel"/>
<traitParameter>
<parameter idref="leafTraits"/>
</traitParameter>
<conjugateRootPrior>
<meanParameter>
<parameter id="mean" value="0.0 0.0"/>
</meanParameter>
<priorSampleSize>
<parameter value="0.001"/>
</priorSampleSize>
</conjugateRootPrior>
</traitDataLikelihood>


<blombergsK id="kstat" traitName="X">
<traitDataLikelihood idref="traitLikelihood"/>
</blombergsK>

<cachedReport id="report">
<report>
<blombergsK idref="kstat"/>
</report>
</cachedReport>


<assertEqual tolerance="1e-5" toleranceType="absolute" verbose="true">
<message>
Check Blomberg's K statistic (1)
</message>
<actual regex="kstat1:\s+(.*)\n">
<cachedReport idref="report"/>
</actual>
<expected>
0.478642
</expected>
</assertEqual>

<assertEqual tolerance="1e-5" toleranceType="absolute" verbose="true">
<message>
Check Blomberg's K statistic (2)
</message>
<actual regex="kstat2:\s+(.*)\n">
<cachedReport idref="report"/>
</actual>
<expected>
1.216268
</expected>
</assertEqual>

</beast>

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