Obtain posterior model probabilities after running Bayesian model selection

1 | ```
postProb(object, nmax, method='norm')
``` |

`object` |
Object of class msfit, e.g. as returned by |

`nmax` |
Maximum number of models to report (defaults to no max) |

`method` |
For 'norm' probabilities are obtained by renormalizing the stored integrated likelihoods, for 'exact' they are given by the proportion of MCMC visits to each model. 'norm' has less variability but can be biased if the chain has not converged. |

A `data.frame`

with posterior model probabilities in column pp.
Column modelid indicates the indexes of the selected covariates (empty
for the null model with no covariates).

David Rossell

`modelSelection`

to perform model selection

1 | ```
#See help(modelSelection)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.