plotPostDens-mcmcoutputpermfix-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.

Usage

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

Arguments

x	An mcmcoutputpermfix 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, 
                 indicfix = TRUE)
# 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.