phenology_MHmcmc: Run the Metropolis-Hastings algorithm for data
In phenology: Tools to Manage a Parametric Function that Describes Phenology and More

phenology_MHmcmc

R Documentation

Run the Metropolis-Hastings algorithm for data

Description

Run the Metropolis-Hastings algorithm for data.
The number of iterations is n.iter+n.adapt+1 because the initial likelihood is also displayed.
I recommend thin=10.
If initial point is maximum likelihood, n.adapt = 0 is a good solution.
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.
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.
These options are used to prevent the consequences of computer crash or if the run is very very long and computer processes are time limited.

Usage

phenology_MHmcmc(
  result = stop("An output from fit_phenology() must be provided"),
  n.iter = 10000,
  parametersMCMC = stop("A model generated with phenology_MHmcmc_p() must be provided"),
  n.chains = 1,
  n.adapt = 1000,
  thin = 1,
  trace = FALSE,
  traceML = FALSE,
  adaptive = TRUE,
  adaptive.lag = 500,
  adaptive.fun = function(x) {
     ifelse(x > 0.234, 1.3, 0.7)
 },
  intermediate = NULL,
  filename = "intermediate.Rdata",
  previous = NULL
)

Arguments

`result`	An object obtained after a SearchR fit
`n.iter`	Number of iterations for each step
`parametersMCMC`	A set of parameters used as initial point for searching with information on priors
`n.chains`	Number of replicates
`n.adapt`	Number of iterations before to store outputs
`thin`	Number of iterations between each stored output
`trace`	TRUE or FALSE or period, shows progress
`traceML`	TRUE or FALSE to show ML
`adaptive`	Should an adaptive process for SDProp be used
`adaptive.lag`	Lag to analyze the SDProp value in an adaptive content
`adaptive.fun`	Function used to change the SDProp
`intermediate`	Period for saving intermediate result, NULL for no save
`filename`	If intermediate is not NULL, save intermediate result in this file
`previous`	Previous result to be continued. Can be the filename in which intermediate results are saved.

Details

phenology_MHmcmc runs the Metropolis-Hastings algorithm for data (Bayesian MCMC)

Value

A list with resultMCMC being mcmc.list object, resultLnL being likelihoods and parametersMCMC being the parameters used

Author(s)

Marc Girondot

Examples

## Not run: 
library(phenology)
data(Gratiot)
# Generate a formatted list named data_Gratiot 
data_Gratiot <- add_phenology(Gratiot, name="Complete", 
    reference=as.Date("2001-01-01"), format="%d/%m/%Y")
# Generate initial points for the optimisation
parg <- par_init(data_Gratiot, fixed.parameters=NULL)
# Run the optimisation
result_Gratiot <- fit_phenology(data=data_Gratiot, 
		fitted.parameters=parg, fixed.parameters=NULL)
# Generate set of priors for Bayesian analysis
pmcmc <- phenology_MHmcmc_p(result_Gratiot, accept = TRUE)
result_Gratiot_mcmc <- phenology_MHmcmc(result = result_Gratiot, n.iter = 10000, 
parametersMCMC = pmcmc, n.chains = 1, n.adapt = 0, thin = 1, trace = FALSE)
# Get standard error of parameters
summary(result_Gratiot_mcmc)
# Make diagnostics of the mcmc results using coda package
mcmc <- as.mcmc(result_Gratiot_mcmc)
require(coda)
heidel.diag(mcmc)
raftery.diag(mcmc)
autocorr.diag(mcmc)
acf(mcmc[[1]][,"LengthB"], lag.max=200, bty="n", las=1)
acf(mcmc[[1]][,"Max_Gratiot"], lag.max=50, bty="n", las=1)
batchSE(mcmc, batchSize=100)
# The batch standard error procedure is usually thought to 
# be not as accurate as the time series methods used in summary
summary(mcmc)$statistics[,"Time-series SE"]
plot(result_Gratiot_mcmc, parameters=3, las=1, xlim=c(-10, 300))

## End(Not run)

phenology documentation built on Sept. 11, 2024, 6:07 p.m.