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For the Bayesian analysis including missing data, the data were partitioned for the four different genes and assigned the appropriate evolutionary model (given above), then unlinked so that the parameters were estimated separately and allowed to have a different evolutionary rate.
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The data were partitioned by gene for 18S, 28S, 12S, and by codon position for COI (three partition; 3p).
The data were partitioned by gene and, for protein coding genes, by codon position.
For the combined analysis, data were partitioned and parameters were estimated separately for each gene.
These data were partitioned randomly into two sets, for training and testing.
For this analysis, data were partitioned into training and holdout sets (80% of observations used for training, 20% used as holdout) and relative sizes of positive samples were kept similar in both the sets (Supplementary Table 1).
Data were partitioned if different models were identified for individual data sets before combination.
The data were partitioned by gene (cob and nad4) for Bayesian analyses (all partitions unlinked) implemented in MrBayes 3.0 [ 85].
For all calculations data were partitioned as described earlier and the BI topology was used as starting tree.
For each cross validation iteration, the data were partitioned into training and test sets.
The data were partitioned as noted above.
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