phenology (version 10.1)

result_Gratiot_mcmc: Result of the mcmc for Leatherback nest counts

Description

Result of the mcmc for Leatherback nest counts from Gratiot et al. (2006) Figure 1 The phenology has been fitted with MinE, MinB, Max, Flat, LengthB, LengthE, Peak, Theta.

Usage

result_Gratiot_mcmc

Format

A mcmcComposite object with mcmc result.

Author

Marc Girondot

Details

Result of the mcmc for Leatherback nest counts from Gratiot et al. (2006) Figure 1

References

Gratiot, N., Gratiot, J., de Thoisy, B. & Kelle, L. 2006. Estimation of marine turtles nesting season from incomplete data ; statistical adjustment of a sinusoidal function. Animal Conservation, 9, 95-102.

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(), phenology(), phenology2fitRMU(), phenology_MHmcmc(), phenology_MHmcmc_p(), plot.phenology(), plot.phenologymap(), plot_delta(), plot_phi(), print.phenology(), print.phenologymap(), print.phenologyout(), remove_site(), result_Gratiot, result_Gratiot1, result_Gratiot2, result_Gratiot_Flat, summary.phenology(), summary.phenologymap(), summary.phenologyout()

Examples

if (FALSE) {
library(phenology)
data(result_Gratiot)
# Read a file with data
data(Gratiot)
# Generate a formatted list nammed 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 data for mcmc run
pmcmc <- phenology_MHmcmc_p(result_Gratiot, accept = TRUE)
result_Gratiot_mcmc <- phenology_MHmcmc(result = result_Gratiot, 
     n.iter = 10000, 
     adaptive=TRUE,
     parametersMCMC = pmcmc, 
     n.chains = 1, n.adapt = 0, thin = 1, trace = FALSE)
# Read a file with result
data(result_Gratiot_mcmc)
1-rejectionRate(as.mcmc(result_Gratiot_mcmc))

summary(result_Gratiot, resultmcmc=result_Gratiot_mcmc)
}