phenology (version 5.1)

plot.phenologymap: Plot a likelihood map with Delta and Phi varying.

Description

This function plots a likelihood map obtained after map_phenology.

Usage

"plot"(x, ..., col = heat.colors(128), xlab = "Phi", ylab = "Delta")

Arguments

x

A map generated with map_phenology.

...

not used

col

Colors could be heat.colors(128) or rainbow(64) or col=gray(c(seq(0, 1, length.out=128)))

xlab

Label for x axis

ylab

Label for y axis

Value

Return None

Details

plot.phenologymap plots a likelihood map with Delta and Phi varying.

Examples

## Not run: 
# 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))
# ## End(Not run)