dcvts: Posterior Density of Constant Variance, Stochastic Volatility...
In tsbridge: Calculate normalising constants for Bayesian time series models.

Description Usage Arguments Author(s) See Also

View source: R/dcvts.R

This function is intended for internal use only. It calculates the posterior density of a constant variance, stochastic volatility or random variance shift time series model given a set of sample of parameter values.

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dcvts(bug, sims, ymean, hmean = NULL, iter = NULL)
dsvts(bug, sims, ymean, hmean = NULL, iter = NULL)
drvts(bug, sims, ymean, hmean = NULL, iter = NULL)

`bug`	A BUGS model created in the tsbugs package.
`sims`	A `data.frame` of simulated parameter values with column names labelled according to output from the R2OpenBUGS package.
`ymean`	A `data.frame` of mean values for response y, the fitted mean process. Columns represent time and rows represent simulations.
`hmean`	A `data.frame` of mean values for h, the fitted volatility process. Columns represent time and rows represent simulations. This argument is not used for `dcvts`.
`iter`	Prints the contributions of each iteration (simulation) number to the density calculation. By default is set to `NULL`, hence no values are printed.