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

In phenology: Tools to Manage a Parametric Function that Describes Phenology and More

Plot a likelihood map with Delta and Phi varying.

Description

This function plots a likelihood map obtained after map_phenology.

Usage

## S3 method for class 'phenologymap'
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

Details

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

Value

Return None

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(), likelihood_phenology(), logLik.phenology(), map_Gratiot, map_phenology(), par_init(), phenology2fitRMU(), phenology_MHmcmc_p(), phenology_MHmcmc(), phenology(), 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

## Not run: 
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
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
map_Gratiot<-map_phenology(data=data_Gratiot, 
		Phi=seq(from=0.1, to=20, length.out=100), 
		fitted.parameters=parg2, fixed.parameters=pfixed)
data(map_Gratiot)
# Plot the map
plot(map_Gratiot, col=heat.colors(128))

## End(Not run)

phenology documentation built on Oct. 16, 2023, 9:06 a.m.