# par_init: Calculate initial set of parameters. In phenology: Tools to Manage a Parametric Function that Describes Phenology and More

## 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

 ```1 2 3 4 5``` ```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

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()`, `phenology2fitRMU()`, `phenology_MHmcmc_p()`, `phenology_MHmcmc()`, `phenology()`, `plot.phenologymap()`, `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()`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38``` ```## 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) ```