embryogrowth (version 6.4)

result_mcmc_newp: Result of the mcmc using the nest database with anchored parameters

Description

Fit using the nest database with anchored parameters

Usage

result_mcmc_newp

Format

A list of class mcmcComposite with mcmc result for data(nest) with anchored parameters

Details

Result of the mcmc using the nest database with anchored parameters

References

Girondot, M., & Kaska, Y. (2014). A model to predict the thermal reaction norm for the embryo growth rate from field data. Journal of Thermal Biology, 45, 96-102. doi: 10.1016/j.jtherbio.2014.08.005

Examples

## Not run: 
# library(embryogrowth)
# data(nest)
# formated <- FormatNests(nest)
# newp <- GenerateAnchor(nests=formated, number.anchors=7)
# pfixed <- c(rK=2.093313)
# resultNest_newp <- searchR(parameters=newp, fixed.parameters=pfixed, 
#   temperatures=formated, derivate=dydt.Gompertz, M0=1.7, 
#   test=c(Mean=39.33, SD=1.92))
# data(resultNest_newp)
# pMCMC <- TRN_MHmcmc_p(resultNest_newp, accept=TRUE)
# # Take care, it can be very long, sometimes several days
# result_mcmc_newp <- GRTRN_MHmcmc(result=resultNest_newp,  
#   parametersMCMC=pMCMC, n.iter=1000, n.chains = 1, n.adapt = 0,  
# 	thin=1, trace=TRUE)
# data(result_mcmc_newp)
# data(resultNest_4p)
# newp <- GenerateAnchor(nests=resultNest_4p, number.anchors=7)
# # Here the confidence interval is built based on anchored parameters
# plotR_hist(resultNest_4p, parameters=newp, SE=result_mcmc_newp$SD, 
#  ylim=c(0,0.4), ylimH=c(0,0.4))
# # Here the confidence interval is built based on parametric SSM equation
# data(result_mcmc_4p)
# plotR_hist(resultNest_4p, SE=result_mcmc_4p$SD,
#  ylim=c(0,0.4), ylimH=c(0,0.4))
# plot(result_mcmc_newp, las=1, xlim=c(0,30), parameters="294", 
# breaks=c(0, 1.00095, 2.0009, 3.00085, 4.0008, 5.00075, 6.0007, 7.00065, 8.0006, 9.00055, 
# 10.0005, 11.00045, 12.0004, 13.00035, 14.0003, 15.00025, 16.0002, 17.00015, 18.0001, 
# 19.00005, 20))
# plot(result_mcmc_newp, las=1, xlim=c(0,30), parameters="296.333333333333")
# plot(result_mcmc_newp, las=1, xlim=c(0,30), parameters=3)
# # Confidence interval based on quantiles
# plotR_hist(resultNest_4p, parameters=newp, SE=NULL, 
#  ylim=c(0,0.4), ylimH=c(0,0.4))
# CI <- apply(result_mcmc_newp$resultMCMC[[1]], 2, quantile, probs=c(0.025, 0.5, 0.975))
# plot_add(as.numeric(colnames(CI))-273.15, CI[1,], lty=2, type="l")
# plot_add(as.numeric(colnames(CI))-273.15, CI[2,], lty=2, type="l")
# ## End(Not run)