phenology (version 3.40)

map_phenology: Generate a likelihood map varying Phi and Delta.

Description

This function generates a map of likelihood varying Phi and Delta.

Usage

map_phenology(data = NULL, parametersfit = NULL,
    parametersfixed = NA,
    Phi = seq(from = 0.2, to = 20, length.out = 100),
    Delta = NULL, method_incertitude = 2,
    zero_counts = TRUE, help = FALSE)

Arguments

data

dataset generated with add_format

parametersfixed

Set of fixed parameters

parametersfit

Set of parameters to be fitted

Phi

Phi values to be analyzed

Delta

Delta value to be analyzed

method_incertitude

2 [default] is the correct one from a statistical point of view; 0 is an aproximate method more rapid; 1 is an alternative more rapid but biased.

zero_counts

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

help

If TRUE, an help is displayed

Value

• Display a likelihood map

Details

map_phenology generates a likelihood map.

Examples

library("phenology")
# Read a file with data
# Gratiot<-read.delim("http://max2.ese.u-psud.fr/epc/conservation/BI/Complete.txt", , header=FALSE)
data(Gratiot)
# Generate a formatted list nammed data_Gratiot
data_Gratiot<-add_format(origin=NULL, add=Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y")
# Generate initial points for the optimisation
parg<-par_init(data_Gratiot, parametersfixed=NULL)
# Run the optimisation
# result_Gratiot<-fit_phenology(data=data_Gratiot, parametersfit=parg, parametersfixed=NULL, trace=1)
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
# map_Gratiot<-map_phenology(data=data_Gratiot, Phi=seq(from=0.1, to=20, length.out=100), parametersfit=parg2, parametersfixed=pfixed)
data(map_Gratiot)
# Plot the map
plot_map(map=map_Gratiot, pdf=FALSE, col=heat.colors(128))
# Plot the min(-Ln L) for Phi varying at any delta value
plot_phi(map=map_Gratiot, pdf=FALSE)
# Plot the min(-Ln L) for Delta varying with Phi equal to the value for maximum likelihood
plot_delta(map=map_Gratiot, pdf=FALSE)
# Plot the min(-Ln L) for Delta varying with Phi the nearest to 15
plot_delta(map=map_Gratiot, Phi=15, pdf=FALSE)