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DISCRETE tests for correlated evolution between two binary traits by comparing the fit of two continuous-time Markov models.
BayesDiscrete tests for correlated evolution between two binary traits by comparing the fit (log-likelihood) of two continuous-time Markov models.
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Phenotypic correlations were measured between the 20 continuous and the two binary traits.
The dependent model can adopt four states, one for each combination of the two binary traits.
The method compares the statistical likelihood of a model in which two binary traits are allowed to evolve independently on the tree, with a model in which the two traits are allowed to evolve in a correlated fashion.
Life history classification was decomposed into two binary traits for quantitative genetic analyses: "LHSmolt" and "LHMature".
We simulated additive values, residuals and fixed effects for one continuous trait and liabilities of four binary traits, and QTL effects for one of the liabilities.
Finally, we examined the evolution of three binary traits (geographic distribution, pollination syndromes, and floral orientation) by reconstructing their ancestral states in the R package corHMM [ 31].
The evolution of all traits was significantly different from random evolution, and the evolution of three binary traits (female combat, parental care, and male parental care) did not differ significantly from the Brownian model (p > 0.05).
(Cf. blue stragglers) A likely scenario is a collision with a binary star system, or between two binary systems containing white dwarfs.
The MCC represents a Pearson correlation between two binary vectors.
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