plotPostDens-method: Plot the posterior density of component parameters

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

Description

Calling plotPostDens() on an object of class mcmcoutput or mcmcoutputperm plots the posterior density of the sampled component parameters from MCMC sampling, either the original parameters or the relabeled ones (mcmcoutputperm).

Arguments

x	An mcmcoutput or mcmcoutputperm 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.

Details

Next to sampling representations and the point process of MCMC samples the posterior density of component parameters can also be plotted directly for finite mixture distributions with K=2 components and a single parameter. The posterior density will always be bimodal due to to label-switching in the MCMC sampling. This could change when considering a relabeld MCMC sample (mcmcoutputperm object).

Note that this method for mcmcoutputperm objects is only implemented for mixtures of Poisson and Binomial distributions.

Value

The posterior density 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

• plotPointProc() for plotting the point process of sampled values

• plotSampRep() for plotting the sampling representation for sampled values

Examples

# 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()
# Define the prior distribution by relying on the data.
f_prior <- priordefine(f_data, f_model)
# Start MCMC sampling.
f_output <- mixturemcmc(f_data, f_model, f_prior, f_mcmc)
plotPostDens(f_output)

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