Description Usage Arguments Details Value Author(s) Examples

Calculate Bayes factors and posterior model probabilities

1 2 | ```
bayes.factors(..., prior = NULL, boot = TRUE, n = 1000,
prob = 0.95)
``` |

`...` |
list of marginal likelihood objects, see details |

`prior` |
numeric, the prior model probabilities |

`boot` |
logical, whether to perform parametric boostrap of probabilities |

`n` |
numeric, number of bootstrap samples |

`prob` |
numeric, the probability used to calculate the boostrap CI |

Input is a list of marginal likelihood objects, with each object generated by
either `stepping.stones()`

or `gauss.quad()`

. If ```
boot =
TRUE
```

, parametric bootstrap is performed by assuming the log-marginal
likelihood estimates are normally distributed with standard deviation equal
to the standard error. The re-sampled `n`

marginal log-likelihoods are
used to estimate re-sampled posterior probabilities, and to calculate an
equal-tail bootstrap confidence interval for these.

Note that the length of `prior`

should be the same as the number of
models being compared. The `prior`

is rescaled so that
`sum(prior) == 1`

.

A list with elements `bf`

, the Bayes factors; `pr`

, the posterior
model probabilities; `prior`

the prior model probabilities and, if
`boot = TRUE`

, `pr.ci`

the equal-tail bootstrap confidence interval.

Mario dos Reis

1 2 3 4 5 6 7 8 9 10 | ```
# See Table 5 in dos Reis et al. (2018, Syst. Biol., 67: 594-615)
# Bayesian selection of relaxed clock models for the 1st and 2nd sites
# of mitochondrial protein-coding genes of primates
# Models: strick clock, independent-rates, and autocorrelated-rates
sc <- list(); sc$logml <- -16519.03; sc$se <- .01
ir <- list(); ir$logml <- -16480.58; ir$se <- .063
ar <- list(); ar$logml <- -16477.82; ar$se <- .035
bayes.factors(sc, ir, ar)
bayes.factors(sc, ir, ar, prior=c(.25,.5,.25))
bayes.factors(sc, ir, ar, prior=c(0,1,0))
``` |

dosreislab/mcmc3r documentation built on Oct. 20, 2018, 2:41 a.m.

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