phenology (version 10.1)

map_phenology: Generate a likelihood map varying Phi and Delta.

Description

This function generates a map of likelihood varying Phi and Delta.
Parameters are the same than for the fit_phenology() function except for trace that is disabled.
If Alpha, Beta or Tau are not indicated, Alpha and Tau are set to 0 and 1 and Beta is fitted.
Only one set of Alpha, Beta, Tau, Phi and Delta are used for all timeseries present in data.
Note that it is possible to fit or fixed Alpha[n], Beta[n], Tau[n], Phi[n] and Delta[n] with [n]=1 or 2 and then it is possible to use this function to establish the likelihood map for a second or third sinusoids added to the global pattern.
If Delta is not specified, it is estimated from Phi and the same precision as Phi is used.

Usage

map_phenology(
  data = NULL,
  fitted.parameters = NULL,
  fixed.parameters = NA,
  Phi = seq(from = 0.2, to = 20, length.out = 100),
  Delta = NULL,
  progressbar = any(installed.packages()[, "Package"] == "pbapply"),
  cofactors = NULL,
  add.cofactors = NULL,
  zero = 1e-09
)

Value

Display a likelihood map

Arguments

data

dataset generated with add_format

fitted.parameters

Set of parameters to be fitted

fixed.parameters

Set of fixed parameters

Phi

Phi values to be analyzed

Delta

Delta value to be analyzed

progressbar

If FALSE, do not show the progress bar

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

Author

Marc Girondot

Details

map_phenology generates a likelihood map.

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, 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

library("phenology")
# 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
if (FALSE) {
result_Gratiot <- fit_phenology(data = data_Gratiot, 
		fitted.parameters = parg, fixed.parameters = NULL)
}
data(result_Gratiot)
# Extract the fitted parameters
parg1 <- extract_result(result_Gratiot)
# Add constant Alpha and Tau values 
# [day d amplitude=(Alpha+Nd*Beta)^Tau with Nd being the number of counts for day d]
pfixed <- c(parg1, Alpha=0, Tau=1)
pfixed <- pfixed[-which(names(pfixed)=="Theta")]
# The only fitted parameter will be Beta
parg2 <- c(Beta=0.5, parg1["Theta"])
# Generate a likelihood map 
# [default Phi=seq(from=0.1, to=20, length.out=100) but it is very long]
# Take care, it takes 20 hours ! The data map_Gratiot has the result
if (FALSE) {
library(phenology)
map_Gratiot <- map_phenology(data = data_Gratiot, 
                             Phi = seq(from=0.1, to=30, length.out=100), 
		                          fitted.parameters = parg2, 
		                          fixed.parameters = pfixed)
}
data(map_Gratiot)
# Plot the map
plot(map_Gratiot, col = heat.colors(128))
# Plot the min(-Ln L) for Phi varying at any delta value
plot_phi(map = map_Gratiot)
# Plot the min(-Ln L) for Delta varying with Phi equal to the value for maximum likelihood
plot_delta(map = map_Gratiot)
# Plot the min(-Ln L) for Delta varying with Phi the nearest to 15
plot_delta(map = map_Gratiot, Phi = 15)