embryogrowth (version 9.5)

HatchingSuccess.fit: Fit a hatching success model to data using maximum likelihood

Description

Set of functions to study the hatching success.
The first version of the model was published in:
Laloë, J.-O., Monsinjon, J., Gaspar, C., Touron, M., Genet, Q., Stubbs, J., Girondot, M. & Hays, G.C. (2020) Production of male hatchlings at a remote South Pacific green sea turtle rookery: conservation implications in a female-dominated world. Marine Biology, 167, 70.
The version available here is enhanced by using a double flexit model rather than a double logistic model. The flexit model is described here:
Abreu-Grobois, F.A., Morales-Mérida, B.A., Hart, C.E., Guillon, J.-M., Godfrey, M.H., Navarro, E. & Girondot, M. (2020) Recent advances on the estimation of the thermal reaction norm for sex ratios. PeerJ, 8, e8451.

Usage

HatchingSuccess.fit(
  par = NULL,
  data = stop("data must be provided"),
  fixed.parameters = NULL,
  column.Incubation.temperature = "Incubation.temperature",
  column.Hatched = "Hatched",
  column.NotHatched = "NotHatched",
  hessian = TRUE
)

Value

Return a object of class HatchingSuccess

Arguments

par

A set of parameters.

data

A dataset in a data.frame with a least three columns: Incubation.temperature, Hatched and NotHatched

fixed.parameters

A set of parameters that must not be fitted.

column.Incubation.temperature

Name of the column with incubation temperatures

column.Hatched

Name of the column with hatched number

column.NotHatched

Name of the column with not hatched number

hessian

Should Hessian matrix be estimated?

Author

Marc Girondot

Details

HatchingSuccess.fit fits a hatching success model to data

See Also

Other Hatching success: HatchingSuccess.MHmcmc(), HatchingSuccess.MHmcmc_p(), HatchingSuccess.lnL(), HatchingSuccess.model(), logLik.HatchingSuccess(), nobs.HatchingSuccess(), predict.HatchingSuccess()

Examples

if (FALSE) {
library(embryogrowth)
totalIncubation_Cc <- subset(DatabaseTSD, 
                             Species=="Caretta caretta" & 
                               Note != "Sinusoidal pattern" & 
                               !is.na(Total) & Total != 0)

par <- c(S.low=0.5, S.high=0.3, 
         P.low=25, deltaP=10, MaxHS=0.8)
         
g.logistic <- HatchingSuccess.fit(par=par, data=totalIncubation_Cc)
         
HatchingSuccess.lnL(par=g.logistic$par, data=totalIncubation_Cc)

plot(g.logistic)

par <- c(S.low=0.5, S.high=0.3, 
         P.low=25, deltaP=10, 
         K1.low=1, K2.low=-1, K1.high=1, K2.high=-1, 
         MaxHS=0.8)

g.flexit <- HatchingSuccess.fit(par=par, data=totalIncubation_Cc)

HatchingSuccess.lnL(par=g.flexit$par, data=totalIncubation_Cc)

compare_AICc(logistic=g.logistic, flexit=g.flexit)
plot(x=g.logistic, what = c("observations", "ML", "CI"), replicates=10000)

pMCMC <- HatchingSuccess.MHmcmc_p(result = g.logistic, accept = TRUE)
MCMC <- HatchingSuccess.MHmcmc(result = g.logistic, parametersMCMC = pMCMC,
                            n.iter = 100000, 
                           adaptive = TRUE)

plot(MCMC, parameters = "S.low")
plot(MCMC, parameters = "S.high")
plot(MCMC, parameters = "P.low")
plot(MCMC, parameters = "deltaP")
plot(MCMC, parameters = "MaxHS")

plot(x=g.logistic, what = c("observations", "ML", "CI"), 
        replicates=10000, resultmcmc=MCMC)

####### Exemple with Chelonia mydas
totalIncubation_Cm <- subset(DatabaseTSD, 
Species=="Chelonia mydas" & 
  Note != "Sinusoidal pattern" & 
  !is.na(Total) & Total != 0 & 
  !is.na(NotHatched) & !is.na(Hatched))
  
  totalIncubation_Cm$NotHatched <- totalIncubation_Cm$NotHatched + 
  ifelse(!is.na(totalIncubation_Cm$Undeveloped), totalIncubation_Cm$Undeveloped, 0)
  
  
  plot(x=totalIncubation_Cm$Incubation.temperature, 
  y=totalIncubation_Cm$Hatched/totalIncubation_Cm$Total, bty="n", las=1, 
  xlab="Constant incubation temperature", ylab="Proportion of hatching")

par <- c(S.low=0.5, S.high=0.3, 
         P.low=25, deltaP=10, MaxHS=0.8)

g.logistic <- HatchingSuccess.fit(par=par, data=totalIncubation_Cm)
plot(g.logistic)

pMCMC <- HatchingSuccess.MHmcmc_p(g.logistic, accept=TRUE)
mcmc <- HatchingSuccess.MHmcmc(result=g.logistic, parameters = pMCMC, 
                  adaptive=TRUE, n.iter=100000, trace=1000)
par <- as.parameters(mcmc)
par <- as.parameters(mcmc, index="median")

plot(mcmc, parameters=c("P.low"))
plot(mcmc, parameters=c("deltaP"))
plot(mcmc, parameters=c("S.low"))
plot(mcmc, parameters=c("S.high"))
plot(mcmc, parameters=c("MaxHS"))

plot(g.logistic, resultmcmc=mcmc, what = c("observations", "CI"))
}