phenology (version 4.0.4)

plot.mcmcComposite: Plot the result of a MCMC search

Description

Plot the result of a MCMC search. The parameters to use can be called by: parameters="all" parameters=1:4 parameters=c("PAR1", "PAR2", "PAR5") parameters=c(TRUE, TRUE, FALSE, TRUE)

Usage

## S3 method for class 'mcmcComposite':
plot(x, ..., chain = 1, parameters = 1,
  scale.prior = FALSE, legend = TRUE)

Arguments

x

A mcmcComposite object obtained after phenology_fonctionMCMC()

...

Graphical parameters to be send to hist()

chain

The chain to use

parameters

Name of parameters or their number (see description)

scale.prior

If TRUE, the prior is scaled at the same size as posterior

legend

If FALSE, the legend is not shown

Value

• None

Details

plot.mcmcComposite plots 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))