R/HatchingSuccess.MHmcmc.R

In embryogrowth: Tools to Analyze the Thermal Reaction Norm of Embryo Growth

#' HatchingSuccess.MHmcmc runs the Metropolis-Hastings algorithm for hatching success (Bayesian MCMC)
#' @title Metropolis-Hastings algorithm for hatching success
#' @author Marc Girondot
#' @return A list with resultMCMC being mcmc.list object, resultLnL being likelihoods and parametersMCMC being the parameters used
#' @param n.iter Number of iterations for each step
#' @param parametersMCMC A set of parameters used as initial point for searching with information on priors
#' @param result An object obtained after a SearchR fit
#' @param n.chains Number of replicates
#' @param n.adapt Number of iterations before to store outputs
#' @param thin Number of iterations between each stored output
#' @param trace TRUE or FALSE or period, shows progress
#' @param traceML TRUE or FALSE to show ML
#' @param batchSize Number of observations to include in each batch fo SE estimation
#' @param adaptive Should an adaptive process for SDProp be used
#' @param adaptive.lag  Lag to analyze the SDProp value in an adaptive content
#' @param adaptive.fun Function used to change the SDProp
#' @param filename If intermediate is not NULL, save intermediate result in this file
#' @param intermediate Period for saving intermediate result, NULL for no save
#' @param previous Previous result to be continued. Can be the filename in which intermediate results are saved.
#' @description Run the Metropolis-Hastings algorithm for hatching success.\cr
#' The number of iterations is n.iter+n.adapt+1 because the initial likelihood is also displayed.\cr
#' I recommend that thin=1 because the method to estimate SE uses resampling.\cr
#' If initial point is maximum likelihood, n.adapt = 0 is a good solution.\cr
#' To get the SE from result_mcmc <- HatchingSuccess.MHmcmc(result=try), use:\cr
#' result_mcmc$BatchSE or result_mcmc$TimeSeriesSE\cr
#' The batch standard error procedure is usually thought to be not as accurate as the time series methods.\cr
#' Based on Jones, Haran, Caffo and Neath (2005), the batch size should be equal to sqrt(n.iter).\cr
#' Jones, G.L., Haran, M., Caffo, B.S. and Neath, R. (2006) Fixed Width Output Analysis for Markov chain Monte Carlo , Journal of the American Statistical Association, 101:1537-1547.\cr
#' coda package is necessary for this function.\cr
#' The parameters intermediate and filename are used to save intermediate results every 'intermediate' iterations (for example 1000). Results are saved in a file of name filename.\cr
#' The parameter previous is used to indicate the list that has been save using the parameters intermediate and filename. It permits to continue a mcmc search.\cr
#' These options are used to prevent the consequences of computer crash or if the run is very very long and processes at time limited.\cr
#' @family Hatching success
#' @examples
#' \dontrun{
#' library(embryogrowth)
#' totalIncubation_Cc <- subset(DatabaseTSD, 
#'                              Species=="Caretta caretta" & 
#'                                Note != "Sinusoidal pattern" & 
#'                                !is.na(Total) & Total != 0)
#' 
#' par <- c(S.low=0.5, S.high=0.3, 
#'          P.low=25, deltaP=10, MaxHS=0.8)
#'          
#' g <- HatchingSuccess.fit(par=par, data=totalIncubation_Cc)
#' pMCMC <- HatchingSuccess.MHmcmc_p(g, accept=TRUE)
#' mcmc <- HatchingSuccess.MHmcmc(result=g, parameters = pMCMC, 
#'                   adaptive=TRUE, n.iter=100000, trace=1000)
#' }
#' @export

HatchingSuccess.MHmcmc <- function(result=stop("Give a result of HatchingSuccess.fit()"), 
                                    n.iter=10000, 
                                    parametersMCMC=NULL, n.chains = 1, n.adapt = 0, 
                                    thin=1, trace=FALSE, traceML=FALSE, batchSize=sqrt(n.iter), 
                                    adaptive=FALSE, adaptive.lag=500, 
                                    adaptive.fun=function(x) {ifelse(x>0.234, 1.3, 0.7)},
                                    intermediate=NULL, filename="intermediate.Rdata", previous=NULL) {
  
  # result=eo_logistic; parametersMCMC=NULL; 
  # n.iter=10000; n.chains = 1;  n.adapt = 0; thin=1; trace=TRUE; batchSize=sqrt(n.iter);intermediate=NULL; filename="intermediate.Rdata"; previous=NULL; adaptive=FALSE; adaptive.lag=500; adaptive.fun=function(x) {ifelse(x>0.234, 1.3, 0.7)}
  
  if (is.character(previous)) {
    itr <- NULL
    load(previous)
    previous <- itr
    rm(itr)
    print("Continue previous mcmc run")
  } else {
    print(parametersMCMC)
  }
  
  # 29/1/2014; Ajout de result$weight
  # 30/1/2015 Ajout de fixedparameters
  out <- MHalgoGen(n.iter=n.iter, parameters=parametersMCMC, 
                   n.chains = n.chains, n.adapt = n.adapt, thin=thin, 
                   trace=trace, traceML=traceML, 
                   data=result$data, fixed.parameters=result$fixed.parameters, 
                   column.Incubation.temperature=result$column.Incubation.temperature,
                   column.Hatched=result$column.Hatched,
                   column.NotHatched=result$column.NotHatched,
                   likelihood=getFromNamespace("HatchingSuccess.lnL", ns="embryogrowth"), 
                   parameters_name = "par", 
                   adaptive=adaptive, adaptive.lag=adaptive.lag, adaptive.fun=adaptive.fun,
                   intermediate=intermediate, filename=filename, previous=previous)
  
  fin <- try(summary(out), silent=TRUE)
  
  if (batchSize>=n.iter/2) {
    print("batchSize cannot be larger than half the number of iterations.")
    rese <- rep(NA, dim(parametersMCMC)[1])
    names(rese) <- rownames(parametersMCMC)
    out <- c(out, SE=list(rese))
  } else {
    out <- c(out, BatchSE=list(coda::batchSE(out$resultMCMC, batchSize=batchSize)))
  }
  
  # class(out) <- "mcmcComposite"
  
  if (inherits(fin, "try-error")) { 
    lp <- rep(NA, nrow(out$parametersMCMC$parameters))
    names(lp) <- rownames(out$parametersMCMC$parameters)
    out <- c(out, TimeSeriesSE=list(lp))
    out <- c(out, SD=list(lp))
  } else {
    out <- c(out, TimeSeriesSE=list(fin$statistics[,4]))
    out <- c(out, SD=list(fin$statistics[,"SD"]))
  }
  
  out <- addS3Class(out, "mcmcComposite")
  
  return(out)
}