plotTraces-mcmcoutputpermfixpost-method: Plot traces of MCMC sampling

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

Description

Calling plotTraces() plots the MCMC traces of the mixture log-likelihood , the mixture log-likelihood of the prior distribution, the log-likelihood of the complete data posterior, or the weights and parameters if lik is set to 1.s

If lik is set to 0 the parameters of the components and the posterior parameters are plotted together with K-1 weights.

Usage

## S4 method for signature 'mcmcoutputpermfixpost'
plotTraces(x, dev = TRUE, lik = 1, col = FALSE, ...)

Arguments

x	An mcmcoutputpermfixpost 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.
lik	An integer indicating, if the log-likelihood traces should be plotted (default). If set to 0 the traces for the parameters and weights are plotted instead.
col	A logical indicating, if the plot should be colored.
...	Further arguments to be passed to the plotting function.

Value

A plot of the traces of the MCMC samples.

See Also

• mixturemcmc() for performing MCMC sampling

• mcmcpermute() for permuting MCMC samples

• plotHist() for plotting histograms 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)
# Do not use an 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)
plotTraces(f_outputperm, lik = 0)

## End(Not run)

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