Description Usage Arguments Value See Also Examples

The recommended function for fitting mixture models and evaluating convergence is through the ‘gibbs' function. This function will return a list of models ordered by the marginal likelihood. The marginal likelihood is computed using the Chib’s estimator (JASA, Volume 90 (435), 1995).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
marginalLikelihood(model, params = mlParams())
## S4 method for signature 'SingleBatchModel'
marginalLikelihood(model, params = mlParams())
## S4 method for signature 'SingleBatchPooled'
marginalLikelihood(model, params = mlParams())
## S4 method for signature 'MultiBatchModel'
marginalLikelihood(model, params = mlParams())
## S4 method for signature 'MultiBatchPooled'
marginalLikelihood(model, params = mlParams())
## S4 method for signature 'list'
marginalLikelihood(model, params = mlParams(warnings =
FALSE))
``` |

`model` |
An object of class |

`params` |
A list containing parameters for marginalLikelihood computation. See |

A vector of the marginal likelihood of the model(s)

See `mlParams`

for parameters related to computing the log marginal likelihood via Chib's estimator. See `gibbs`

for fitting multiple mixture models and returning a list sorted by the marginal likelihood. See `marginal_lik`

for the accessor.

Note: currently thinning of the reduced MCMC chains is not allowed.

1 2 3 4 5 6 | ```
## In practice, run a much longer burnin and increase the number of
## iterations to save after burnin
mm <- SingleBatchModelExample
mcmcParams(mm) <- McmcParams(iter=50, burnin=0, nStarts=0)
mm <- posteriorSimulation(mm)
marginalLikelihood(mm)
``` |

CNPBayes documentation built on May 2, 2018, 3:57 a.m.

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