plotSampRep-mcmcoutputpermhierpost-method: Plot sampling representations for the component parameters

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

Description

Calling plotSampRep() plots sampling representations of the sampled component parameters from MCMC sampling.

Note, this method is only implemented for mixtures of Poisson and Binomial distributions.

Usage

## S4 method for signature 'mcmcoutputpermhierpost'
plotSampRep(x, dev = TRUE, ...)

Arguments

x	An mcmcoutputpermhierpost 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

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

• plotPointProc() for plotting point processes of sampled values

• plotPostDens() for plotting posterior densities 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)
f_outputperm <- mcmcpermute(f_output)
plotSampRep(f_outputperm)

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