generateCF: Generate a set of data to test Clutch Frequency for marine...

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Generate a set of data to test Clutch Frequency for marine turtles.

Description

This function generates a dataframe to test fitCF().
This model is an enhanced version of the one published by Briane et al. (2007).
Parameters are mu and sd being the parameters of a distribution used to model the clutch frequency.
This distribution is used only as a guide but has not statistical meaning.
The parameter p is the -logit probability that a female is seen on the beach for a particular nesting event. It includes both the probability that it is captured but also the probability that it uses that specific beach.
Several categories of females can be included in the model using index after the name of the parameter, for example mu1, sd1 and mu2, sd2 indicates that two categories of females with different clutch frequencies distribution are present. Similarly p1 and p2 indicates that two categories of females with different capture probabilities are present.
If more than one category is used, then it is necessary to include the parameter OTN to indicate the relative frequencies of each category. If two categories are used, one OTN parameter named ONT1 must be included. The OTN2 is forced to be 1. Then the relative frequency for category 1 is OTN1/(OTN1+1) and for category 2 is 1/(OTN1+1). Same logic must be applied for 3 and more categories with always the last one being fixed to 1.

if p or a (logit of the capture probability) are equal to -Inf, the probability of capture is 0 and if they are equal to +Inf, the probability is 1.

The value of p out of the period of nesting must be set to +Inf (capture probability=1) to indicate that no turtle is nesting in this period.

p must be set to -Inf (capture probability=0) to indicate that no monitoring has been done during a specific period of the nesting season.

The best way to indicate capture probability for 3D model (OCF, ECF, Period) is to indicate p.period common for all categories and a1, a2, etc for each category. The capture probability for category 1 will be p.period * a1, and for category 2 will be p.period * a2, etc.

In this case, the parameters p.period should be indicated in fitted parameters as well as a1, but a2 must be fixed to +Inf in fixed.parameters. Then the capture probability for category 2 will be p.period and for category 1 a1 * p.period.

Usage

generateCF(
  x = c(mu = 4, sd = 1, p = +Inf, mu_season = 13.8360591186578, sd_season =
    0.17440085345944),
  MeanDaysBetween2Nests = 9.8,
  date0 = as.Date("2020-01-01"),
  n = 1,
  verbose = TRUE
)

Arguments

x	Initial parameters to be used
MeanDaysBetween2Nests	Number of days in average between two nests
date0	Initial date to generate data
n	Number of individuals to model
verbose	If TRUE, give information about each animal.

Details

generateCF generates set of data to test fitCF.

Value

Return a list with 4 elements: Category, CF, Beginning and Observations being a dataframe of individuals.

Author(s)

Marc Girondot

See Also

Briane J-P, Rivalan P, Girondot M (2007) The inverse problem applied to the Observed Clutch Frequency of Leatherbacks from Yalimapo beach, French Guiana. Chelonian Conservation and Biology 6:63-69

Fossette S, Kelle L, Girondot M, Goverse E, Hilterman ML, Verhage B, Thoisy B, de, Georges J-Y (2008) The world's largest leatherback rookeries: A review of conservation-oriented research in French Guiana/Suriname and Gabon. Journal of Experimental Marine Biology and Ecology 356:69-82

Other Model of Clutch Frequency: ECFOCF_full(), ECFOCF_f(), TableECFOCF(), fitCF_MHmcmc_p(), fitCF_MHmcmc(), fitCF(), lnLCF(), logLik.ECFOCF(), plot.ECFOCF(), plot.TableECFOCF()

Examples

## Not run: 
library(phenology)
# Example

par <- c(mu = 2.4911638591178051, 
         sd = 0.96855483039640977, 
         mu_season = 13.836059118657793, 
         sd_season = 0.17440085345943984, 
         p.10 = 1.3348233607728222, 
         p.11 = 1.1960387774393837, 
         p.12 = 0.63025680979544774, 
         p.13 = 0.38648155002707452, 
         p.14 = 0.31547864054366048, 
         p.15 = 0.19720001827017075, 
         p.16 = 0.083199496372073328, 
         p.17 = 0.32969130595897905, 
         p.18 = 0.36582777525265819, 
         p.19 = 0.30301248314170637, 
         p.20 = 0.69993987591518514, 
         p.21 = 0.13642423871641118, 
         p.22 = -1.3949268190534629, 
         p=+Inf)

o_mu1p1season1 <- generateCF(x=par, n=1, verbose=TRUE)
o_mu1p1season1 <- generateCF(x=par, n=1000)
plot(o_mu1p1season1$CF)
hist(o_mu1p1season1$Beginning)

## End(Not run)

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