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

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/Likelihood_phenology.R

Description

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

Usage

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likelihood_phenology(
  data = NULL,
  fitted.parameters = NULL,
  fixed.parameters = NULL,
  zero_counts = NULL,
  parallel = TRUE,
  result = NULL,
  cofactors = NULL,
  add.cofactors = NULL,
  tol = 1e-06,
  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

zero_counts

example c(TRUE, TRUE, FALSE) indicates whether the zeros have been recorder for each of these timeseries. Defaut is TRUE for all.

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

tol

Tolerance of recurrence for dSnbinom() used for convolution of negative binomial distribution

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

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## 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)

## End(Not run)

phenology documentation built on Oct. 23, 2020, 7:22 p.m.