| phenology_MHmcmc_p {phenology} | R Documentation |
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"),
default.density = "dunif",
accept = FALSE
)
Arguments
result |
An object obtained after a fit_phenology() fit |
default.density |
The default density, "dnorm" or "dunif' |
accept |
If TRUE, does not wait for use interaction |
Details
phenology_MHmcmc_p generates set of parameters to be used with phenology_MHmcmc()
Value
A matrix with the parameters
Author(s)
Marc Girondot
See Also
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology(),
plot.phenologymap(),
plot.phenology(),
plot_delta(),
plot_phi(),
print.phenologymap(),
print.phenologyout(),
print.phenology(),
remove_site(),
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
result_Gratiot,
summary.phenologymap(),
summary.phenologyout(),
summary.phenology()
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)