posteriorSimulation {CNPBayes} | R Documentation

## Run MCMC simulation.

### Description

nStarts chains are run. b burnin iterations are run and then discarded. Next, s iterations are run in each train. The user can also specify an alternative number of components. The mode of the MCMC simulation is also calculated.

### Usage

    posteriorSimulation(object, k)

    ## S4 method for signature 'MixtureModel'
    posteriorSimulation(object)

    ## S4 method for signature 'TrioBatchModel'
    posteriorSimulation(object)

### Arguments

`object` | see showMethods(posteriorSimulation)
`k` | The number of a priori components. This is optional and if not specified, the stored k model components are used. This parameters is useful for running multiple models of varying components.

### Value

An object of class 'MarginalModel' or 'BatchModel'

### See Also

`ggChains` for diagnosing convergence. See `ggMixture` for plotting the model-based densities.

### Examples

    # Fit model with pre-specified number of components (k=3)
    set.seed(123)
    ## specify small number of iterations so that the example runs quickly
    mp <- McmcParams(iter=2, burnin=0, nStarts=3)
    sb <- SingleBatchModelExample
    mcmcParams(sb) <- mp
    posteriorSimulation(sb)

    # Run additional iterations, but set nStart = 0 so that the last value of the
    # chain is the first value of the next chain
    mcmcParams(sb) <- McmcParams(iter=5, nStarts=0, burnin=0)
    posteriorSimulation(sb)

    # Fit batch models of different sizes (k=1 and 2)
    mb <- MultiBatchModelExample
    mcmcParams(mb) <- mp
    yy <- sample(y(mb), 300)
    batches <- rep(1:3, length.out=length(yy))
    mp <- McmcParams(iter=1000, burnin=500, thin=1, nStarts=4)
    ## Not run:
      mlist <- gibbs(model="MB", k_range=c(1, 2), dat=yy, batches=batches)

    ## End(Not run)

[Package CNPBayes version 1.13.5 Index]