embryogrowth (version 7.1)

STRN_MHmcmc: Metropolis-Hastings algorithm for Sexualisation Thermal Reaction Norm

Description

Run the Metropolis-Hastings algorithm for Sexualisation Thermal Reaction Norm. The number of iterations is n.iter+n.adapt+1 because the initial likelihood is also displayed. I recommend that thin=1 because the method to estimate SE uses resampling. If initial point is maximum likelihood, n.adapt = 0 is a good solution. To get the SE of the point estimates from result_mcmc <- STRN_MHmcmc(result=try), use: result_mcmc$SD coda package is necessary for this function. The dataSTRN is a named list with the following objects:

• EmbryoGrowthTRN= result of searchR

• tsd= result of tsd

• sexed= vector with number of sexed embryos

• males= vector with number of males (could be also females=)

• Temperatures= a text of the temperatures name used as CTE

The Temperatures text for CTE can be:

• TimeWeighted.temperature.mean

• TSP.TimeWeighted.temperature.mean

• TSP.MassWeighted.temperature.mean

• TSP.STRNWeighted.temperature.mean

• TSP.MassWeighted.STRNWeighted.temperature.mean

• MiddleThird.TimeWeighted.temperature.mean

They are explained in the info.nests function. This function is not still fully described as it has not been published still. The parameters intermediate and filename are used to save intermediate results every 'intermediate' iterations (for example 1000). Results are saved in a file of name filename. The parameter previous is used to indicate the list that has been save using the parameters intermediate and filename. It permits to continue a mcmc search. These options are used to prevent the consequences of computer crash or if the run is very very long and processes at time limited.

Usage

STRN_MHmcmc(result = NULL, n.iter = 10000, parametersMCMC = NULL,
  n.chains = 1, n.adapt = 0, thin = 1, trace = NULL,
  batchSize = sqrt(n.iter), dataSTRN = NULL, adaptive = FALSE,
  adaptive.lag = 500, adaptive.fun = function(x) {     ifelse(x > 0.234,
  1.3, 0.7) }, parallel = TRUE, intermediate = NULL,
  filename = "intermediate.Rdata", previous = NULL)

Arguments

result

An object obtained after a STRN fit

n.iter

Number of iterations for each step

parametersMCMC

A set of parameters used as initial point for searching with information on priors

n.chains

Number of replicates

n.adapt

Number of iterations before to store outputs

thin

Number of iterations between each stored output

trace

True or False, shows progress

batchSize

Number of observations to include in each batch fo SE estimation

dataSTRN

A named list data used to estimate likelihoods (see further in description)

adaptive

Should an adaptive process for SDProp be used

adaptive.lag

Lag to analyze the SDProp value in an adaptive content

adaptive.fun

Function used to change the SDProp

parallel

Should parallel computing for info.nests() be used

intermediate

Period for saving intermediate result, NULL for no save

filename

If intermediate is not NULL, save intermediate result in this file

previous

Previous result to be continued. Can be the filename in which intermediate results are saved.

Value

A list with resultMCMC being mcmc.list object, resultLnL being likelihoods and parametersMCMC being the parameters used

Details

STRN_MHmcmc runs the Metropolis-Hastings algorithm for STRN (Bayesian MCMC)

Examples

# NOT RUN {
library(embryogrowth)
MedIncubation_Cc <- subset(DatabaseTSD, Species=="Caretta caretta" & 
RMU=="Mediterranean" & Sexed!=0)
Med_Cc <- with(MedIncubation_Cc, tsd(males=Males, females=Females, 
 temperatures=Incubation.temperature, par=c(P=29.5, S=-0.01)))
plot(Med_Cc, xlim=c(25, 35))
males <- c(7, 0, 0, 0, 0, 5, 6, 3, 5, 3, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0)
names(males) <- rev(rev(names(resultNest_4p_SSM4p$data))[-(1:2)])
sexed <- rep(10, length(males))
names(sexed) <- rev(rev(names(resultNest_4p_SSM4p$data))[-(1:2)])
Initial_STRN <- resultNest_4p_SSM4p$par[c("DHA", "DHH", "T12H")]
Initial_STRN <- structure(c(3460.21379985491, 588.062535503578, 2347.70617453574), 
                          .Names = c("DHA", "DHH", "T12H"))
fp <- c(Rho25=100)
fitSTRN <- STRN(Initial_STRN, EmbryoGrowthTRN=resultNest_4p_SSM4p, tsd=Med_Cc, 
                Sexed=sexed, Males=males, 
                fixed.parameters=fp,  
                Temperatures="TSP.MassWeighted.STRNWeighted.temperature.mean")
pMCMC <- TRN_MHmcmc_p(fitSTRN, accept=TRUE)
pMCMC[, "Density"] <- "dunif"
pMCMC[, "Prior2"] <- pMCMC[, "Max"]<- 10000
pMCMC[, "Prior1"] <- pMCMC[, "Min"] <- 1
outMCMC <- STRN_MHmcmc(result = fitSTRN, n.iter = 50000, parametersMCMC = pMCMC,
                n.chains = 1, n.adapt = 0, thin = 1, trace = TRUE,
                dataSTRN = list(EmbryoGrowthTRN = resultNest_4p_SSM4p, 
                   Temperatures = "TSP.STRNWeighted.temperature.mean", 
                   fixed.parameters=fitSTRN$fixed.parameters,
                   tsd=Med_Cc, 
                   Sexed=sexed, Males=males), 
                adaptive = TRUE, adaptive.lag = 500, 
                intermediate = 1000,
                filename = "intermediate_mcmcSTRN.Rdata")
plot(outMCMC, parameters=1)
plot(outMCMC, parameters=2)
plot(outMCMC, parameters=3)
1-rejectionRate(as.mcmc(x = outMCMC))
# Take care,you computer will be 100% busy as all cores will be used intensively
outMCMC_parallel <- parallel::mclapply(1:detectCores(), function(x) {
                STRN_MHmcmc(result = fitSTRN, n.iter = 50000/detectCores(), 
                parametersMCMC = pMCMC,
                n.chains = 1, n.adapt = 0, thin = 1, trace = TRUE,
                dataSTRN = list(EmbryoGrowthTRN = resultNest_4p_SSM4p, 
                   Temperatures = "TSP.STRNWeighted.temperature.mean", 
                   fixed.parameters=fitSTRN$fixed.parameters,
                   tsd=Med_Cc, 
                   Sexed=sexed, Males=males), 
                parallel=FALSE, 
                adaptive = TRUE, adaptive.lag = 500, 
                intermediate = NULL)
})
outMCMC_parallel_merge <- outMCMC_parallel[[1]]
for (i in 2:length(outMCMC_parallel)) {
  outMCMC_parallel_merge <- merge(outMCMC_parallel_merge, outMCMC_parallel[[i]])
}
plot(outMCMC_parallel_merge, parameters=1)
plot(outMCMC_parallel_merge, parameters=2)
plot(outMCMC_parallel_merge, parameters=3)

plotR(parameters = fitSTRN$par, fixed.parameters=fitSTRN$fixed.parameters, 
      curves = "MCMC quantiles", ylim=c(0, 5), resultmcmc = outMCMC_parallel_merge, 
      ylab="Relative contribution to sexualisation", xlim=c(28, 29))

# }