phenology (version 10.1)

likelihood_phenology: Estimate the likelihood of timeseries based on a set of parameters.

Description

This function is used to estimate the likelihood based on a set of parameters.

Usage

likelihood_phenology(
  data = NULL,
  fitted.parameters = NULL,
  fixed.parameters = NULL,
  parallel = TRUE,
  result = NULL,
  model_before = NULL,
  cofactors = NULL,
  add.cofactors = NULL,
  zero = 1e-09,
  out = TRUE
)

Value

The likelihood of the data with the parameters

Arguments

data

Dataset generated with add_format

fitted.parameters

Set of parameters to be fitted

fixed.parameters

Set of fixed parameters

parallel

If TRUE, parallel computing is used.

result

An object obtained after fit_phenology()

model_before

The change of parameters before to estimate daily counts.

cofactors

data.frame with a column Date and a column for each cofactor

add.cofactors

Names of the column of parameter cofactors to use as a cofactor

zero

If the theoretical nest number is under this value, this value wll be used

out

If TRUE, return the global likelihood; if FALSE, the likelihood for each series

Author

Marc Girondot

Details

likelihood_phenology estimate likelihood for a set of parameters.

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(), 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, result_Gratiot_mcmc, summary.phenology(), summary.phenologymap(), summary.phenologyout()

Examples

if (FALSE) {
# Read a file with data
data(Gratiot)
# Generate a formated 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)
# Estimate likelihood with this initial set of parameters
likelihood_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL)
# Or directly from a result object
likelihood_phenology(result=result_Gratiot)
# With new parametrization based on Omeyer et al. (2022)
# Omeyer, L. C. M., McKinley, T. J., Bréheret, N., Bal, G., Balchin, G. P., Bitsindou, A., 
# Chauvet, E., Collins, T., Curran, B. K., Formia, A., Girard, A., Girondot, M., Godley, B. J., 
# Mavoungou, J.-G., Poli, L., Tilley, D., VanLeeuwe, H. & Metcalfe, K. 2022. Missing data in 
# sea turtle population monitoring: a Bayesian statistical framework accounting for incomplete 
# sampling Front. Mar. Sci. (IF 3.661), 9, 817014.

parg <- c(tp=unname(parg["Peak"]), tf=unname(parg["Flat"]), 
          s1=unname(parg["LengthB"])/4.8, s2=unname(parg["LengthE"])/4.8, 
          alpha=unname(parg["Max_Complete"]), Theta=unname(parg["Theta"]))
likelihood_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL)
}