phenology (version 4.0.4)

summary.mcmcComposite: Summarize the result of a MCMC search

Description

Summary for the result of a MCMC search

Usage

## S3 method for class 'mcmcComposite':
summary(object, ..., chain = NULL)

Arguments

object

A mcmcComposite object obtained after MHmcmc()

...

Internal use

chain

The chain to use

Value

• A summary of the result

Details

summary.mcmcComposite get info on the result of a MCMC search

Examples

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))