phenology (version 5.1)

phenology_MHmcmc_p: Generates set of parameters to be used with phenology_MHmcmc()

Description

Interactive script used to generate set of parameters to be used with phenology_MHmcmc().

Usage

phenology_MHmcmc_p(result = stop("An output from fit_phenology() must be provided"), accept = FALSE)

Arguments

result

An object obtained after a fit_phenology() fit

accept

If TRUE, does not wait for use interaction

Value

A matrix with the parameters

Details

phenology_MHmcmc_p generates set of parameters to be used with MHmcmc()

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, parametersfixed=NULL)
# # Run the optimisation
# result_Gratiot<-fit_phenology(data=data_Gratiot, 
# 		parametersfit=parg, parametersfixed=NULL, trace=1)
# # 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)