bayesTFR (version 1.1-0)

tfr.pardensity.plot: Plotting MCMC Parameter Density

Description

Functions for plotting density of the posterior distribution of the MCMC parameters.

Usage

tfr.pardensity.plot(mcmc.list = NULL, 
    sim.dir = file.path(getwd(), "bayesTFR.output"), 
    chain.ids = NULL, par.names = tfr.parameter.names(trans = TRUE), 
    burnin = NULL, dev.ncol=5, low.memory = TRUE, ...)
    
tfr.pardensity.cs.plot(country, mcmc.list=NULL, 
    sim.dir=file.path(getwd(), 'bayesTFR.output'), 
    chain.ids=NULL, par.names=tfr.parameter.names.cs(trans=TRUE), 
    burnin=NULL, dev.ncol=3, low.memory=TRUE, ...)

Rdversion

1.1

Details

The functions plot the density of the posterior distribution either for country-independent parameters (tfr.pardensity.plot) or for country-specific parameters (tfr.pardensity.cs.plot), one graph per parameter. One can restrict it to specific chains by setting the chain.ids argument and to specific parameters by setting the par.names argument.

If mcmc.list is an object of class bayesTFR.prediction and if this object contains thinned traces, they are used instead of the full chains. In such a case, burnin and chain.ids cannot be modified - their value is set to the one used when the thinned traces were created, namely when running tfr.predict. In a situation with long MCMC chains, this approach can significantly speed-up creation of the density plots.

See Also

tfr.partraces.plot

Examples

sim.dir <- file.path(.find.package("bayesTFR"), "ex-data", 'bayesTFR.output')
tfr.pardensity.plot(sim.dir=sim.dir)
tfr.pardensity.cs.plot(country='Ireland', sim.dir=sim.dir, bw=0.2)