R/as.mcmc.mcmcComposite.R

In HelpersMG: Tools for Environmental Analyses, Ecotoxicology and Various R Functions

#' as.mcmc Extract mcmc object from the result of phenology_MHmcmc to be used with coda package
#' @title Extract mcmc object from a mcmcComposite object
#' @author Marc Girondot \email{marc.girondot@@gmail.com}
#' @return A mcmc.list object
#' @param x A mcmcComposite obtained as a result of \code{MHalgoGen()} function
#' @param ... Not used
#' @description Take a mcmcComposite object and create a mcmc.list object to be used with coda package.
#' @family mcmcComposite functions
#' @examples
#' \dontrun{
#' library(HelpersMG)
#' require(coda)
#' x <- rnorm(30, 10, 2)
#' dnormx <- function(data, x) {
#'  data <- unlist(data)
#'  return(-sum(dnorm(data, mean=x['mean'], sd=x['sd'], log=TRUE)))
#' }
#' parameters_mcmc <- data.frame(Density=c('dnorm', 'dlnorm'), 
#' Prior1=c(10, 0.5), Prior2=c(2, 0.5), SDProp=c(1, 1), 
#' Min=c(-3, 0), Max=c(100, 10), Init=c(10, 2), stringsAsFactors = FALSE, 
#' row.names=c('mean', 'sd'))
#' mcmc_run <- MHalgoGen(n.iter=1000, parameters=parameters_mcmc, data=x, 
#' likelihood=dnormx, n.chains=1, n.adapt=100, thin=1, trace=1)
#' plot(mcmc_run, xlim=c(0, 20))
#' plot(mcmc_run, xlim=c(0, 10), parameters="sd")
#' mcmcforcoda <- as.mcmc(mcmc_run)
#' #' heidel.diag(mcmcforcoda)
#' raftery.diag(mcmcforcoda)
#' autocorr.diag(mcmcforcoda)
#' acf(mcmcforcoda[[1]][,"mean"], lag.max=20, bty="n", las=1)
#' acf(mcmcforcoda[[1]][,"sd"], lag.max=20, bty="n", las=1)
#' batchSE(mcmcforcoda, batchSize=100)
#' # The batch standard error procedure is usually thought to 
#' # be not as accurate as the time series methods used in summary
#' summary(mcmcforcoda)$statistics[,"Time-series SE"]
#' summary(mcmc_run)
#' as.parameters(mcmc_run)
#' lastp <- as.parameters(mcmc_run, index="last")
#' parameters_mcmc[,"Init"] <- lastp
#' # The n.adapt set to 1 is used to not record the first set of parameters
#' # then it is not duplicated (as it is also the last one for 
#' # the object mcmc_run)
#' mcmc_run2 <- MHalgoGen(n.iter=1000, parameters=parameters_mcmc, data=x, 
#' likelihood=dnormx, n.chains=1, n.adapt=1, thin=1, trace=1)
#' mcmc_run3 <- merge(mcmc_run, mcmc_run2)
#' ####### no adaptation, n.adapt must be 0
#' parameters_mcmc[,"Init"] <- c(mean(x), sd(x))
#' mcmc_run3 <- MHalgoGen(n.iter=1000, parameters=parameters_mcmc, data=x, 
#' likelihood=dnormx, n.chains=1, n.adapt=0, thin=1, trace=1)
#' }
#' @method as.mcmc mcmcComposite
#' @import coda
#' @export

as.mcmc.mcmcComposite <- function(x, ...) {

		return(x$resultMCMC)
}