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In addition to the subsidization of solar adopters by non-participants, we highlight the channels through which cross-subsidization of rate classes can arise in practice.
The Γ distribution was approximated with four discrete rate classes of equal size.
Models with 5 rate classes showed significantly improved model fit for 96 to 100 of the 100 permutations.
Independent general discrete distributions were initiated with random starting parameters and 4 rate classes each for synonymous and non-synonymous rates.
In each case the nucleotide substitution matrix was optimized for each data set with Beta-Gamma rate variation and three rate classes as initial running parameters.
We hint at how this can be achieved with a Genetic Algorithm approach in which rate classes are assigned on the basis of sequence information content.
For models with two rate classes, significantly improved model fit was evident in at least 43 and up to 80 of the 100 random models (15 and 66 with Bonferroni correction).
Here, we show that the single rate model of non-synonymous substitution is easily outperformed by a model with multiple non-synonymous rate classes, yet in which amino acid substitution pairs are assigned randomly to these classes.
When the data were analyzed using a partitioned model that groups sites according to rate classes inferred by the mixture model, ML recovered the true tree, whereas BI continued to be biased in favor of the MA tree.
Huelsenbeck [ 8] added additional rate classes to this model.
Starting from the default number of four rate classes for the discrete gamma approximation (GTR+G4), we have tested increasing numbers of rate classes.
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