phenology (version 3.40)

phenology_MHmcmc: Run the Metropolis-Hastings algorithm for data

Description

Run the Metropolis-Hastings algorithm for data. Deeply modified from a MCMC script by Olivier Martin (INRA, Paris-Grignon). The number of iterations is n.iter+n.adapt+1 because the initial likelihood is also displayed. I recommend that thin=1 because the method to estimate SE uses resampling. As initial point is maximum likelihood, n.adapt = 0 seems a good solution.

Usage

phenology_MHmcmc(result = stop("An output from searchR must be provided"),
    n.iter = 10000,
    parametersMCMC = stop("A model generated with phenology_MHmcmc_p() must be provided"),
    n.chains = 4, n.adapt = 0, thin = 1, trace = FALSE)

Arguments

n.iter

Number of iterations for each step

parametersMCMC

A set of parameters used as initial point for searching with information on priors

result

An object obtained after a SearchR fit

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, shows progress

Value

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

Details

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

Examples

library(phenology)
# Read a file with data
# Gratiot<-read.delim("http://max2.ese.u-psud.fr/epc/conservation/BI/Complete.txt", , header=FALSE)
data(Gratiot)
# Generate a formatted list named data_Gratiot
data_Gratiot<-add_format(origin=NULL, add=Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y")
# Generate initial points for the optimisation
parg<-par_init(data_Gratiot, parametersfixed=NULL)
# Run the optimisation
## not run
# result_Gratiot<-fit_phenology(data=data_Gratiot, parametersfit=parg, parametersfixed=NULL, trace=1)
## end not run
data(result_Gratiot)
# Generate set of priors for Bayesian analysis
## not run
## pmcmc <- phenology_MHmcmc_p(result_Gratiot)
## end not run
pmcmc <- structure(c("dunif", "dunif", "dunif", "dunif", "dunif", "dunif",
"dunif", "dunif", "0", "0", "0", "0", "0", "0", "0", "0", "200",
"365", "200", "50", "200", "5", "5", "10", "2", "2", "2", "2",
"2", "2", "2", "2", "0", "0", "0", "0", "0", "0", "0", "0", "200",
"365", "200", "50", "200", "5", "5", "10", "95.826796339888",
"175.36499338462", "62.4313052780003", "6.77668901451618e-05",
"33.1138407661406", "0.21779065736816", "0.424368825094697",
"3.58302217559733"), .Dim = c(8L, 7L), .Dimnames = list(c("LengthB",
"Peak", "LengthE", "Flat", "Max_Gratiot", "MinB_Gratiot", "MinE_Gratiot",
"Theta"), c("Density", "Prior1", "Prior2", "SDProp", "Min", "Max",
"Init")))
## not run
# res_mcmc <- phenology_MHmcmc(result = result_Gratiot, n.iter = 10000,
# parametersMCMC = pmcmc, n.chains = 1, n.adapt = 0, thin = 1, trace = FALSE)
## end not run