plotPostDens-mcmcoutputpermbase-method: Plot posterior densities of the component parameters

In simonsays1980/finmix: An R package for Bayesian estimation of finite mixture distributions

Description

Calling plotPostDens() plots posterior densities of the sampled component parameters from MCMC sampling, if the number of components is two.

Note, this method is so far only implemented for Poisson or Binomial mixture distributions.

Usage

## S4 method for signature 'mcmcoutputpermbase'
plotPostDens(x, dev = TRUE, ...)

Arguments

x	An mcmcoutputpermbase object containing all sampled values.
dev	A logical indicating, if the plots should be shown by a graphical device. If plots should be stored to a file set dev to FALSE.
...	Further arguments to be passed to the plotting function.

Value

Posterior densities of the MCMC samples.

See Also

• mixturemcmc() for performing MCMC sampling

• mcmcpermute() for permuting MCMC samples

• plotTraces() for plotting the traces of sampled values

• plotHist() for plotting histograms of sampled values

• plotDens() for plotting densities of sampled values

• plotSampRep() for plotting sampling representations of sampled values

• plotPointProc() for plotting point processes for sampled values

Examples

## Not run: 
# Define a Poisson mixture model with two components.
f_model <- model("poisson", par = list(lambda = c(0.3, 1.2)), K = 2)
# Simulate data from the mixture model.
f_data <- simulate(f_model)
# Define the hyper-parameters for MCMC sampling.
f_mcmc <- mcmc(storepost = FALSE)
# Define the prior distribution by relying on the data.
f_prior <- priordefine(f_data, f_model)
# Do not use a hierarchical prior.
setHier(f_prior) <- FALSE
# Start MCMC sampling.
f_output <- mixturemcmc(f_data, f_model, f_prior, f_mcmc)
f_outputperm <- mcmcpermute(f_output)
plotPostDens(f_outputperm)

## End(Not run)

simonsays1980/finmix documentation built on Dec. 23, 2021, 2:25 a.m.