par_init: Calculate initial set of parameters.

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

Calculate initial set of parameters.

Description

This function is used to generate an initial set of parameters for fitting that is expected to be not to far from the final.
The parameters can be:

• Min, MinE, MinB, PMin, PMinB, PMinE;

• Max;

• Begin, Peak, Flat, End;

• Length, LengthB, LengthE;

• Theta;

• Alpha, Beta, Tau, Phi, Delta;

• Alpha1, Beta1, Tau1, Phi1, Delta1;

• Alpha2, Beta2, Tau2, Phi2, Delta2;

• Alpha3, Beta3, Tau3, Phi3, Delta3;

And the name of level if a cofactor is used.
The parameters Max, Min, MinE, MinB, Length, LengthB, LengthE, and Peak can be followed with _ and part of the name of the rookery.
The model for scale effect of sinusoid is: Alpha + Beta * n(t) ^ Tau where n(t) is the expected number for the day t without the sinusoid effect.

Usage

par_init(
  data = stop("A dataset must be provided"),
  fixed.parameters = NULL,
  add.cofactors = NULL
)

Arguments

data	Dataset generated with add_phenology()
fixed.parameters	Set of fixed parameters
add.cofactors	Names of cofactors that will be used (see fit_phenology)

Details

par_init calculates initial set of parameters.

Value

The initial set of parameters

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(), 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, summary.phenology(), summary.phenologymap(), summary.phenologyout()

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)
# Plot the phenology and get some stats
output<-plot(result_Gratiot)

## When a series has only 0, it should be used in two steps
## Let see an example

# Let create a times series with only 0
data0 <- data.frame(Date=c("11/3/2015", "12/3/2015", "13/3/2015-18/3/2015", "25/3/2015"), 
                    Number=c(0, 0, 0, 0), 
                    Beach=rep("Site0", 4), stringsAsFactors=FALSE)
data1 <- data.frame(Date=c("15/3/2015", "16/3/2015", "20/3/2015-22/3/2015", "25/3/2015"), 
                    Number=c(1, 0, 3, 0), 
                    Beach=rep("Site1", 4), stringsAsFactors=FALSE)
data <- rbind(data0, data1)

# Here I include timeseries with no observation
try1 <- add_phenology(data, format="%d/%m/%Y", month_ref=1, include0=TRUE)
pfixed <- c(Min=0, Flat=0)
parg <- par_init(try1, fixed.parameters=pfixed)
# The Max value for the series without observations should not be fitted. The ML is for Max being 0
pfixed <- c(pfixed, parg[(substr(names(parg), 1, 4)=="Max_") & (parg == 0)])
parg <- parg[!(names(parg) %in% names(pfixed))]


## End(Not run)

phenology documentation built on June 17, 2025, 9:08 a.m.