View source: R/Likelihood_phenology.R
likelihood_phenology | R Documentation |
This function is used to estimate the likelihood based on a set of parameters.
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
)
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 |
likelihood_phenology estimate likelihood for a set of parameters.
The likelihood of the data with the parameters
Marc Girondot
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()
## 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)
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