plotHist-mcmcoutputpermbase-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.

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

Usage

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

Arguments

x	An mcmcoutputpermbase 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

## Not run: 
# 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)
# 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)
plotHist(f_outputperm)

## End(Not run)

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