plotPointProc-mcmcoutputpermfixhier-method: Plot point processes of the component parameters

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

Description

Calling plotPointProc() plots point processes of the sampled component parameters from MCMC sampling.

Note, this method is so far only implemented for mixture models of Poisson or Binomial distributons.

Usage

## S4 method for signature 'mcmcoutputpermfixhier'
plotPointProc(x, dev = TRUE, ...)

Arguments

x	An mcmcoutputpermfixhier 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

Point process 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

• plotSampRep() for plotting sampling representations of sampled values

• plotPostDens() for plotting posterior densities for sampled values

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(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)
plotPointProc(f_outputperm)

## End(Not run)

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