likelihood_phenology: Estimate the likelihood of timeseries based on a set of...

Description Usage Arguments Details Value Author(s) Examples

Description

This function is used to estimate the likelihood based on a set of parameters.

Usage

1
2
3
likelihood_phenology(data = NULL, parametersfit = NULL,
  parametersfixed = NULL, zero_counts = NULL, method_incertitude = NULL,
  result = NULL, infinite = 200, zero = 1e-09)

Arguments

data

Dataset generated with add_format

parametersfit

Set of parameters to be fitted

parametersfixed

Set of fixed parameters

zero_counts

example c(TRUE, TRUE, FALSE) indicates whether the zeros have been recorder for each of these timeseries. Defaut is TRUE for all.

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.

result

An object obtained after fit_phenology()

infinite

Number of iterations for dSnbinom() used for method_incertitude='sum'

zero

If the theoretical nest number is under this value, this value wll be used

Details

likelihood_phenology estimate likelihood for a set of parameters.

Value

The likelihood of the data with the parameters

Author(s)

Marc Girondot

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
## Not run: 
# 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 formated 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)
# Estimate likelihood with this initial set of parameters
likelihood_phenology(data=data_Gratiot, parametersfit=parg, parametersfixed=NULL)
# Or directly from a result object
likelihood_phenology(result=result_Gratiot)

## End(Not run)


Search within the phenology package
Search all R packages, documentation and source code

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.