R/mcmcMetropolis.R

Defines functions Metropolis sampleMetropolis

Documented in Metropolis sampleMetropolis

#' Creates a Metropolis-type MCMC with options for covariance adaptatin, delayed rejection, Metropolis-within-Gibbs, and tempering
#' @author Florian Hartig
#' @param bayesianSetup either an object of class bayesianSetup created by \code{\link{createBayesianSetup}} (recommended), or a log target function 
#' @param settings a list of settings - possible options follow below
#' @param startValue startValue for the MCMC and optimization (if optimize = T). If not provided, the sampler will attempt to obtain the startValue from the bayesianSetup
#' @param optimize logical, determines whether an optimization for start values and proposal function should be run before starting the sampling
#' @param proposalGenerator optional proposalgenerator object (see \code{\link{createProposalGenerator}})
#' @param proposalScaling additional scaling parameter for the proposals that controls the different scales of the proposals after delayed rejection (typical, after a rejection, one would want to try a smaller scale). Needs to be as long as DRlevels. Defaults to 0.5^(- 0:(mcmcSampler$settings$DRlevels -1)
#' @param burnin number of iterations treated as burn-in. These iterations are not recorded in the chain.
#' @param thin thinning parameter. Determines the interval in which values are recorded.
#' @param consoleUpdates integer, determines the frequency with which sampler progress is printed to the console
#' @param adapt logical, determines wheter an adaptive algorithm should be implemented. Default is TRUE.
#' @param adaptationInterval integer, determines the interval  of the adaption if adapt = TRUE.
#' @param adaptationNotBefore integer, determines the start value for the adaption if adapt = TRUE.
#' @param DRlevels integer, determines the number of levels for a delayed rejection sampler. Default is 1, which means no delayed rejection is used.
#' @param temperingFunction function to implement simulated tempering in the algorithm. The function describes how the acceptance rate will be influenced in the course of the iterations.
#' @param gibbsProbabilities vector that defines the relative probabilities of the number of parameters to be changes simultaniously.
#' @param message logical determines whether the sampler's progress should be printed
#' @details The 'Metropolis' function is the main function for all Metropolis based samplers in this package. To call the derivatives from the basic Metropolis-Hastings MCMC, you can either use the corresponding function (e.g. \code{\link{AM}} for an adaptive Metropolis sampler) or use the parameters to adapt the basic Metropolis-Hastings. The advantage of the latter case is that you can easily combine different properties (e.g. adapive sampling and delayed rejection sampling) without changing the function.
#' @import coda
#' @example /inst/examples/MetropolisHelp.R
#' @export
#' @references Haario, H., E. Saksman, and J. Tamminen (2001). An adaptive metropolis algorithm. Bernoulli , 223-242.
#' @references Haario, Heikki, et al. "DRAM: efficient adaptive MCMC." Statistics and Computing 16.4 (2006): 339-354.
#' @references Hastings, W. K. (1970). Monte carlo sampling methods using markov chains and their applications. Biometrika 57 (1), 97-109.
#' @references Green, Peter J., and Antonietta Mira. "Delayed rejection in reversible jump Metropolis-Hastings." Biometrika (2001): 1035-1053.
#' @references Metropolis, N., A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller (1953). Equation of state calculations by fast computing machines. The journal of chemical physics 21 (6), 1087 - 1092.
Metropolis <- function(bayesianSetup, 
                       settings = list(startValue = NULL, 
                                       optimize = T, 
                                       proposalGenerator = NULL, 
                                       consoleUpdates=100, 
                                       burnin = 0,
                                       thin = 1, 
                                       parallel = NULL,
                                       adapt = T,
                                       adaptationInterval= 500,
                                       adaptationNotBefore = 3000,
                                       DRlevels = 1 ,
                                       proposalScaling = NULL,
                                       adaptationDepth = NULL,
                                       temperingFunction = NULL,
                                       gibbsProbabilities = NULL,
                                       message = TRUE
                                       )){
  
  ## General setup - this template should be similar for all MCMC algorithms
  
  setup <- checkBayesianSetup(bayesianSetup, parallel = settings$parallel) # calling parallel will check if requested parallelization in settings is provided by the BayesianSetup
  if(is.null(settings$parallel)) settings$parallel = bayesianSetup$parallel # checking back - if no parallelization is provided, we use the parallelization in the BayesianSetup. We could also set parallel = F, but I feel it makes more sense to use the Bayesiansetup as default
  
  settings = applySettingsDefault(settings, sampler = "Metropolis")
  
  if(is.null(settings$startValue)){
    settings$startValue = bayesianSetup$prior$sampler()
  }
  if(is.function(settings$startValue)){
    settings$startValue = settings$startValue()
  }
  
  ## Parameter consistency checks 

  if(is.null(settings$adaptationDepth)){
    settings$adaptationDepth = settings$adaptationNotBefore
  } 
  
  # Decreasing scaling for DRAM by default
  if (is.null(settings$proposalScaling)) settings$proposalScaling = 0.5^(- 0:(settings$DRlevels -1))
  
  tmp <- setupStartProposal(proposalGenerator = settings$proposalGenerator, bayesianSetup = bayesianSetup, settings = settings)
  settings = tmp$settings
  proposalGenerator = tmp$proposalGenerator

  ####### CREATE CHAIN
  
  chain = array(dim = c(1,bayesianSetup$numPars+3))
  chain[1,1:bayesianSetup$numPars] = settings$startValue
  colnames(chain) = c(1:bayesianSetup$numPars, "LP", "LL", "LPr")
  chain[1, (bayesianSetup$numPars+1):(bayesianSetup$numPars+3)] = setup$posterior$density(settings$startValue, returnAll = T)
  
  current = settings$startValue
  currentLP = as.numeric(chain[1, (bayesianSetup$numPars+1)])
  
  ##### Sampling
  
  classFields = list(
    setup = setup,
    settings = settings,
    current = current,
    currentLP = currentLP,
    chain = chain, 
    proposalGenerator = proposalGenerator,
    funEvals = 0,
    acceptanceRate = 0
  )
  
  class(classFields) <- c("mcmcSampler", "bayesianOutput")
  return(classFields)
}


#' gets samples while adopting the MCMC proposal generator
#' @author Florian Hartig
#' @param mcmcSampler an mcmcSampler 
#' @param iterations iterations
#' @description Function to sample with cobinations of the basic Metropolis-Hastings MCMC algorithm (Metropolis et al., 1953), a variation of the adaptive Metropolis MCMC (Haario et al., 2001), the delayed rejection algorithm (Tierney & Mira, 1999), and the delayed rejection adaptive Metropolis algorithm (DRAM, Haario et al), and the Metropolis within Gibbs 
#' @export
#' @keywords internal
sampleMetropolis <- function(mcmcSampler, iterations){
  
  burnin <- mcmcSampler$settings$burnin
  thin <- mcmcSampler$settings$thin
  
  CounterFunEvals = mcmcSampler$funEvals
  CounterAccept = nrow(mcmcSampler$chain)*mcmcSampler$acceptanceRate
  
  if (mcmcSampler$settings$DRlevels > 2) stop("DRlevels > 2 currently not implemented")
  
  # Increase chain
  lastvalue = nrow(mcmcSampler$chain)
  mcmcSampler$chain = rbind(mcmcSampler$chain, array(dim = c(floor((iterations-burnin)/thin),mcmcSampler$setup$numPars+3)))
  
  alpha = rep(NA, mcmcSampler$settings$DRlevels)
  proposalEval = matrix( nrow = mcmcSampler$settings$DRlevels, ncol = 3)
  proposal = matrix( nrow = mcmcSampler$settings$DRlevels, ncol = mcmcSampler$setup$numPars)
  
  # Initialize counter for chain update
  counter <- lastvalue
  
  for (i in lastvalue:(lastvalue+iterations-1)){
  
    accepted = F
    
    if(is.null(mcmcSampler$settings$temperingFunction)) tempering = 1 else tempering = mcmcSampler$settings$temperingFunction(i)
    
    if(tempering < 1) warning("Tempering option < 1. This usually doesn't make sense!")
    
    for (j in 1:mcmcSampler$settings$DRlevels){
      
      proposal[j,] = mcmcSampler$proposalGenerator$returnProposal(x = mcmcSampler$current, scale = mcmcSampler$settings$proposalScaling[j])
      proposalEval[j,] <- mcmcSampler$setup$posterior$density(proposal[j,], returnAll = T)
      CounterFunEvals <- CounterFunEvals+1
      
      # case j = 1 (normal MH-MCMC)
      if (j == 1){
        alpha[j] = metropolisRatio(proposalEval[j,1], mcmcSampler$currentLP, tempering)
        jumpProbab = alpha[1]
        # case j = 2 (first delayed rejection)
      } else if (j == 2 & alpha[j-1] > 0 ){
        alpha[j] = metropolisRatio(proposalEval[j,1], proposalEval[j-1,1], tempering)
        
        temp <- metropolisRatio(mcmcSampler$proposalGenerator$returnDensity(proposal[1,], proposal[2,]), mcmcSampler$proposalGenerator$returnDensity(mcmcSampler$current, proposal[1,]))
        
        jumpProbab = metropolisRatio(proposalEval[j,1], mcmcSampler$currentLP, tempering) * temp * (1.0-alpha[j]) / (1.0-alpha[j-1]) 
      }
      
      if (runif(1) < jumpProbab){
        accepted = T
        mcmcSampler$current = proposal[j,]
        mcmcSampler$currentLP = proposalEval[j,1]
        if((i > (lastvalue+burnin)) && (i %% thin == 0) ){
          counter <- counter+1
        mcmcSampler$chain[counter,] = c(proposal[j,], proposalEval[j,])
        }
        break
      }
    }
    if((accepted == F) && (i > (lastvalue+burnin)) && (i %% thin == 0)){
      counter <- counter +1
      mcmcSampler$chain[counter,] = mcmcSampler$chain[counter-1,]
    } 
    if(accepted == T) CounterAccept <- CounterAccept+1
    
    # Proposal update
    
    if(mcmcSampler$settings$adapt  == T & i > mcmcSampler$settings$adaptationNotBefore & i %% mcmcSampler$settings$adaptationInterval == 0 ){
      start = max(1, counter - mcmcSampler$settings$adaptationDepth)
      mcmcSampler$proposalGenerator = updateProposalGenerator(proposal = mcmcSampler$proposalGenerator, chain = mcmcSampler$chain[start:counter,1:mcmcSampler$setup$numPars], message = F)
    }
    
    # Console update
    
    if(mcmcSampler$settings$message){
    if( i %% mcmcSampler$settings$consoleUpdates == 0 ) cat("\r","Running Metropolis-MCMC, chain ", 
                                                            mcmcSampler$settings$currentChain, "iteration" ,i,"of",iterations,
                                                            ". Current logp: ", mcmcSampler$chain[counter,mcmcSampler$setup$numPars+1]," Please wait!","\r")
    flush.console()
  }
  }
  
  # Make sure chain has right size
  mcmcSampler$chain <- mcmcSampler$chain[1:counter,]
  
  mcmcSampler$codaChain = coda::mcmc(mcmcSampler$chain)
  mcmcSampler$funEvals <- CounterFunEvals
  mcmcSampler$acceptanceRate <- CounterAccept/CounterFunEvals
  return(mcmcSampler)
}

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BayesianTools documentation built on Dec. 10, 2019, 1:08 a.m.