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. | 
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> <#if includeI == 1> including models with/without a proportion of invariable sites (+I)
<#else> including only models without a proportion of invariable sites
#if> <#if includeG == 1> including models with/without rate variation among sites (+G) (nCat = ${numCat})
<#else> including only models without rate variation among sites
#if> Optimized free parameters (K) = ${freeParameters}
Base tree for likelihood calculations = ${baseTree}
<#if userTreeDef == 1> User tree (${userTreeFilename}) = ${userTree}
#if> Tree topology search operation = ${searchAlgorithm}
Model Optimization Results
| 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} | 
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.
| ID | Models | Topology | AIC | BIC | AICc | DT | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| ${topology.index} |  
      ${topology.models}
     | RANK<#if isAIC == 1> | ${topology.aicRank}<#else> | -#if> <#if isBIC == 1> | ${topology.bicRank}<#else> | -#if> <#if isAICc == 1> | ${topology.aiccRank}<#else> | -#if> <#if isDT == 1> | ${topology.dtRank}<#else> | -#if> | |
| Weight<#if isAIC == 1> | ${topology.aicWeight}<#else> | -#if> <#if isBIC == 1> | ${topology.bicWeight}<#else> | -#if> <#if isAICc == 1> | ${topology.aiccWeight}<#else> | -#if> <#if isDT == 1> | ${topology.dtWeight}<#else> | -#if> | |||
| RF<#if isAIC == 1> | ${topology.aicRF}<#else> | -#if> <#if isBIC == 1> | ${topology.bicRF}<#else> | -#if> <#if isAICc == 1> | ${topology.aiccRF}<#else> | -#if> <#if isDT == 1> | ${topology.dtRF}<#else> | -#if> | |||
| AVG Distance<#if isAIC == 1> | ${topology.aicAvgDistance}<#else> | -#if> <#if isBIC == 1> | ${topology.bicAvgDistance}<#else> | -#if> <#if isAICc == 1> | ${topology.aiccAvgDistance}<#else> | -#if> <#if isDT == 1> | ${topology.dtAvgDistance}<#else> | -#if> | |||
| Distance VAR<#if isAIC == 1> | ${topology.aicVarDistance}<#else> | -#if> <#if isBIC == 1> | ${topology.bicVarDistance}<#else> | -#if> <#if isAICc == 1> | ${topology.aiccVarDistance}<#else> | -#if> <#if isDT == 1> | ${topology.dtVarDistance}<#else> | -#if> | 
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} | 
| 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}
Relative Robinson-Foulds distances histogram from the different topologies to ${bestAicModel.name} topology.
PAUP block
${aicPaup} #if> <#if doAICAveragedPhylogeny == 1>Model Averaged Phylogeny
| Selection criterion | AIC | 
|---|---|
| Confidence interval | ${confidenceInterval}% | 
| Consensus type | ${consensusType} | 
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} | 
| 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}
Relative Robinson-Foulds distances histogram from the different topologies to ${bestAiccModel.name} topology.
PAUP block
${aiccPaup} #if> <#if doAICcAveragedPhylogeny == 1>Model Averaged Phylogeny
| Selection criterion | AICc | 
|---|---|
| Confidence interval | ${confidenceInterval}% | 
| Consensus type | ${consensusType} | 
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} | 
| 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}
Relative Robinson-Foulds distances histogram from the different topologies to ${bestBicModel.name} topology.
PAUP block
${bicPaup} #if> <#if doBICAveragedPhylogeny == 1>Model Averaged Phylogeny
| Selection criterion | BIC | 
|---|---|
| Confidence interval | ${confidenceInterval}% | 
| Consensus type | ${consensusType} | 
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} | 
| 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}
Relative Robinson-Foulds distances histogram from the different topologies to ${bestDtModel.name} topology.
PAUP block
${dtPaup} #if> <#if doDTAveragedPhylogeny == 1>Model Averaged Phylogeny
| Selection criterion | DT | 
|---|---|
| Confidence interval | ${confidenceInterval}% | 
| Consensus type | ${consensusType} | 
 
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