phenology (version 3.72)

map_phenology: Generate a likelihood map varying Phi and Delta.

Description

This function generates a map of likelihood varying Phi and Delta. When Delta is not given, the same precision as Phi is used.

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, progressbar = 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 recorded for each of these timeseries. Defaut is TRUE for all.

progressbar

If FALSE, do not show the progress bar

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_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, 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_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)