plotHist-mcmcoutputpermhier-method: Plot histograms of the parameters and weights

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

Description

Calling plotHist() plots histograms of the sampled parameters and weights from MCMC sampling. In addition the parameters of the hierarchical prior are plotted.

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

Usage

## S4 method for signature 'mcmcoutputpermhier'
plotHist(x, dev = TRUE, ...)

Arguments

x	An mcmcoutputpermhier 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

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

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(storepost = FALSE)
# 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.