| likelihood_phenology {phenology} | R Documentation |
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,
cofactors = NULL,
add.cofactors = NULL,
zero = 1e-09,
out = TRUE
)
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() |
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 |
Details
likelihood_phenology estimate likelihood for a set of parameters.
Value
The likelihood of the data 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(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology2fitRMU(),
phenology_MHmcmc_p(),
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:
# 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)
## End(Not run)