Application Meta

jModeltest 2.1

(c) 2011-onwards D. Darriba, G.L. Taboada, R. Doallo and D. Posada,
(1) Department of Biochemistry, Genetics and Immunology
University of Vigo, 36310 Vigo, Spain.
(2) Department of Electronics and Systems
University of A Coruna, 15071 A Coruna, Spain.
e-mail: ddarriba@udc.es, dposada@uvigo.es

${date}
${system}

Citation: Darriba D, Taboada GL, Doallo R and Posada D. 2012. "jModelTest 2: more models, new heuristics and parallel computing". Nature Methods 9, 772.

Notice: This program may contain errors. Please inspect results carefully.

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Settings

Arguments = ${arguments}
Input Alignment: "${alignName}"
NumTaxa = ${numTaxa}
Length = ${seqLength}
Phyml version = ${phymlVersion}
Phyml binary = ${phymlBinary}
Candidate models = ${candidateModels}
number of substitution schemes = ${substSchemes}
<#if includeF == 1> including models with equal/unequal base frequencies (+F)
<#else> including only models with equal base frequencies
<#if includeI == 1> including models with/without a proportion of invariable sites (+I)
<#else> including only models without a proportion of invariable sites
<#if includeG == 1> including models with/without rate variation among sites (+G) (nCat = ${numCat})
<#else> including only models without rate variation among sites
Optimized free parameters (K) = ${freeParameters}
Base tree for likelihood calculations = ${baseTree}
<#if userTreeDef == 1> User tree (${userTreeFilename}) = ${userTree}
Tree topology search operation = ${searchAlgorithm}

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Model Optimization Results

<#list sortedModels as model>
ID Name Partition -lnL p fA fC fG fT ti/tv R(a) R(b) R(c) R(d) R(e) R(f) p-inv shape
${model.index}${model.name} ${model.partition} ${model.lnl} ${model.k} ${model.fA} ${model.fC} ${model.fG} ${model.fT} ${model.titv} ${model.rA} ${model.rB} ${model.rC} ${model.rD} ${model.rE} ${model.rF} ${model.pInv} ${model.shape}
<#if isTopologiesSummary == 1>

There are ${numberOfTopologies} different topologies. The following table shows the models supporting each topology and the rank according to each Information Criterion, as well as Robinson-Foulds and Euclidean distances with the tree of the best-fit model.

<#list sortedTopologies as topology> <#if isAIC == 1> <#else> <#if isBIC == 1> <#else> <#if isAICc == 1> <#else> <#if isDT == 1> <#else> <#if isAIC == 1> <#else> <#if isBIC == 1> <#else> <#if isAICc == 1> <#else> <#if isDT == 1> <#else> <#if isAIC == 1> <#else> <#if isBIC == 1> <#else> <#if isAICc == 1> <#else> <#if isDT == 1> <#else> <#if isAIC == 1> <#else> <#if isBIC == 1> <#else> <#if isAICc == 1> <#else> <#if isDT == 1> <#else> <#if isAIC == 1> <#else> <#if isBIC == 1> <#else> <#if isAICc == 1> <#else> <#if isDT == 1> <#else>
ID Models Topology AIC BIC AICc DT
${topology.index}
${topology.models}
${topology.tree}
RANK${topology.aicRank} - ${topology.bicRank} - ${topology.aiccRank} - ${topology.dtRank} -
Weight${topology.aicWeight} - ${topology.bicWeight} - ${topology.aiccWeight} - ${topology.dtWeight} -
RF${topology.aicRF} - ${topology.bicRF} - ${topology.aiccRF} - ${topology.dtRF} -
AVG Distance${topology.aicAvgDistance} - ${topology.bicAvgDistance} - ${topology.aiccAvgDistance} - ${topology.dtAvgDistance} -
Distance VAR${topology.aicVarDistance} - ${topology.bicVarDistance} - ${topology.aiccVarDistance} - ${topology.dtVarDistance} -

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<#if isAIC == 1>

AIC Selection Results

Model selected

Model ${bestAicModel.name}
partition ${bestAicModel.partition}
-lnL ${bestAicModel.lnl}
K ${bestAicModel.k}
freqA ${bestAicModel.fA} R(a) ${bestAicModel.rA}
freqC ${bestAicModel.fC} R(b) ${bestAicModel.rB}
freqG ${bestAicModel.fG} R(c) ${bestAicModel.rC}
freqT ${bestAicModel.fT} R(d) ${bestAicModel.rD}
ti/tv ${bestAicModel.titv} R(e) ${bestAicModel.rE}
R(f) ${bestAicModel.rF}
p-inv ${bestAicModel.pInv} gamma ${bestAicModel.shape}

Best model tree

${bestAicModel.tree}
Display best model tree in PhyloWidget
<#list aicModels as model>
Model -lnL K AIC delta weight cumWeight
${model.name} ${model.lnl}${model.k}${model.value}${model.delta}${model.weight}${model.cumWeight}
-lnL:negative log likelihod
K: number of estimated parameters
AIC: Akaike Information Criterion
delta: AIC difference
weight: AIC weight
cumWeight:cumulative AIC weight

Confidence interval

There are ${aicConfidenceCount} models in the ${confidenceInterval}% confidence interval:
${aicConfidenceList}


Euclidean distances histogram from each model optimized tree to ${bestAicModel.name} tree.

Relative Robinson-Foulds distances histogram from the different topologies to ${bestAicModel.name} topology.
<#if isPAUP == 1>

PAUP block

${aicPaup} <#if doAICAveragedPhylogeny == 1>

Model Averaged Phylogeny

Selection criterionAIC
Confidence interval${confidenceInterval}%
Consensus type${consensusType}
${aicConsensusTree}
Display consensus tree in PhyloWidget

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<#if isAICc == 1>

AICc Selection Results

Model selected

Model ${bestAiccModel.name}
partition ${bestAiccModel.partition}
-lnL ${bestAiccModel.lnl}
K ${bestAiccModel.k}
freqA ${bestAiccModel.fA} R(a) ${bestAiccModel.rA}
freqC ${bestAiccModel.fC} R(b) ${bestAiccModel.rB}
freqG ${bestAiccModel.fG} R(c) ${bestAiccModel.rC}
freqT ${bestAiccModel.fT} R(d) ${bestAiccModel.rD}
ti/tv ${bestAiccModel.titv} R(e) ${bestAiccModel.rE}
R(f) ${bestAiccModel.rF}
p-inv ${bestAiccModel.pInv} gamma ${bestAiccModel.shape}

Best model tree

${bestAiccModel.tree}
Display best model tree in PhyloWidget
<#list aiccModels as model>
Model -lnL K AICc delta weight cumWeight
${model.name} ${model.lnl}${model.k}${model.value}${model.delta}${model.weight}${model.cumWeight}
-lnL:negative log likelihod
K: number of estimated parameters
AICc: Corrected Akaike Information Criterion
delta: AICc difference
weight: AICc weight
cumWeight:cumulative AICc weight

Confidence interval

There are ${aiccConfidenceCount} models in the ${confidenceInterval}% confidence interval:
${aiccConfidenceList}


Euclidean distances histogram from each model optimized tree to ${bestAiccModel.name} tree.

Relative Robinson-Foulds distances histogram from the different topologies to ${bestAiccModel.name} topology.
<#if isPAUP == 1>

PAUP block

${aiccPaup} <#if doAICcAveragedPhylogeny == 1>

Model Averaged Phylogeny

Selection criterionAICc
Confidence interval${confidenceInterval}%
Consensus type${consensusType}
${aiccConsensusTree}
Display consensus tree in PhyloWidget

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<#if isBIC == 1>

BIC Selection Results

Model selected

Model ${bestBicModel.name}
partition ${bestBicModel.partition}
-lnL ${bestBicModel.lnl}
K ${bestBicModel.k}
freqA ${bestBicModel.fA} R(a) ${bestBicModel.rA}
freqC ${bestBicModel.fC} R(b) ${bestBicModel.rB}
freqG ${bestBicModel.fG} R(c) ${bestBicModel.rC}
freqT ${bestBicModel.fT} R(d) ${bestBicModel.rD}
ti/tv ${bestBicModel.titv} R(e) ${bestBicModel.rE}
R(f) ${bestBicModel.rF}
p-inv ${bestBicModel.pInv} gamma ${bestBicModel.shape}

Best model tree

${bestBicModel.tree}
Display best model tree in PhyloWidget
<#list bicModels as model>
Model -lnL K BIC delta weight cumWeight
${model.name} ${model.lnl}${model.k}${model.value}${model.delta}${model.weight}${model.cumWeight}
-lnL:negative log likelihod
K: number of estimated parameters
BIC: Bayesian Information Criterion
delta: BIC difference
weight: BIC weight
cumWeight:cumulative BIC weight

Confidence interval

There are ${bicConfidenceCount} models in the ${confidenceInterval}% confidence interval:
${bicConfidenceList}


Euclidean distances histogram from each model optimized tree to ${bestBicModel.name} tree.

Relative Robinson-Foulds distances histogram from the different topologies to ${bestBicModel.name} topology.
<#if isPAUP == 1>

PAUP block

${bicPaup} <#if doBICAveragedPhylogeny == 1>

Model Averaged Phylogeny

Selection criterionBIC
Confidence interval${confidenceInterval}%
Consensus type${consensusType}
${bicConsensusTree}
Display consensus tree in PhyloWidget

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<#if isDT == 1>

Decision Theory Selection Results

Model selected

Model ${bestDtModel.name}
partition ${bestDtModel.partition}
-lnL ${bestDtModel.lnl}
K ${bestDtModel.k}
freqA ${bestDtModel.fA} R(a) ${bestDtModel.rA}
freqC ${bestDtModel.fC} R(b) ${bestDtModel.rB}
freqG ${bestDtModel.fG} R(c) ${bestDtModel.rC}
freqT ${bestDtModel.fT} R(d) ${bestDtModel.rD}
ti/tv ${bestDtModel.titv} R(e) ${bestDtModel.rE}
R(f) ${bestDtModel.rF}
p-inv ${bestDtModel.pInv} gamma ${bestDtModel.shape}

Best model tree

${bestDtModel.tree}
Display best model tree in PhyloWidget
<#list dtModels as model>
Model -lnL K DT delta weight cumWeight
${model.name} ${model.lnl}${model.k}${model.value}${model.delta}${model.weight}${model.cumWeight}
-lnL:negative log likelihod
K: number of estimated parameters
DT: Akaike Information Criterion
delta: DT difference
weight: DT weight
cumWeight:cumulative DT weight

Confidence interval

There are ${dtConfidenceCount} models in the ${confidenceInterval}% confidence interval:
${dtConfidenceList}


Euclidean distances histogram from each model optimized tree to ${bestDtModel.name} tree.

Relative Robinson-Foulds distances histogram from the different topologies to ${bestDtModel.name} topology.
<#if isPAUP == 1>

PAUP block

${dtPaup} <#if doDTAveragedPhylogeny == 1>

Model Averaged Phylogeny

Selection criterionDT
Confidence interval${confidenceInterval}%
Consensus type${consensusType}
${dtConsensusTree}
Display consensus tree in PhyloWidget

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