plotHist-mcmcoutputfixhierpost-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.More specifically, all component parameters, K-1 of the weights and the posterior parameters are considered in the histogram plots.

Note that this method calls the equivalent method from the parent class mcmcoutputfixhier.

Usage

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

Arguments

x	An mcmcoutputfixhierpost 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

• 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

## 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()
# 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)
plotHist(f_output)

## End(Not run)

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