plotHist-method: Plot histograms of the parameters and weights

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

Description

plotHist() is a class method for mcmcoutput and mcmcoutputperm objects. For the former class it plots histograms of MCMC samples and for the latter of the corresponding permuted samples coming from relabeling.

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

Calling plotHist() plots histograms of the sampled parameters and weights from MCMC sampling. Note, for relabeled MCMC samples this method is so far only implemented for mixtures of Poisson and Binomial distributions.

Hierarchical priors

In case that hierarchical priors had been used in MCMC sampling histograms of the sampled parameters of the hierarchical prior are added to the plot.

Posterior density parameters

In case that posterior density parameters had been stored in MCMC sampling, histograms of these parameters are added to the plot.

Value

Histograms of the MCMC samples.

See Also

• mixturemcmc() for performing MCMC sampling

• mcmcpermute() for permuting MCMC samples

• plotTraces() for plotting the traces 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

• plotPostDens() for plotting the posterior density of component parameters

• mcmcoutput for the class definition of mcmcoutput

• mcmcoutputperm for the class definition of mcmcoutputperm

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)
plotHist(f_outputperm)

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