The only interpretation of the Bayes Factor calculated using the output of BayesTraits is this:
2 Positive evidence
5-10 Strong evidence
>10 Very strong evidence
Is there a reason why Bayes Factor won't get a negative value?
Also, is a Bayes Factor value that provides 'negative' evidence, because they way I see, weak evidence is still evidence in favour of the dependent model, but not strong enough. Or is this not the case?
Many thanks in advance!
In simple terms, Bayes Factor is the ratio of two probabilities, and probabilities can only take on a value between 0 and 1 (they cannot be negative). By extension, the ratio of two probabilities cannot be negative.
Bayes factors < 0 are evidence against the alternative. If you use the log Bayes factor, you see the symmetry (logBF of -5 is as much evidence against than a logBF of +5 is evidence in favour).
The Bayes factor is a likelihood ratio of the marginal likelihood of two competing hypotheses, usually a null and an alternative. represents the probability that some data are produced under the assumption of the model M; evaluating it correctly is the key to Bayesian model comparison. https://en.m.wikipedia.org/wiki/Bayes_factor
Check https://easystats.github.io/bayestestR/articles/bayes_factors.html
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