embryogrowth (version 6.4)

result_mcmc_4p_weight: Result of the mcmc using the nest database

Description

Fit using the nest database

Usage

result_mcmc_4p_weight

Format

A list of class mcmcComposite with mcmc result for data(nest) with 4 parameters and Gompertz model of growth weigted to maximized entropy

Details

Result of the mcmc using the nest database with weight

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)
# w <- weightmaxentropy(formated, control_plot=list(xlim=c(20,36)))
# x <- structure(c(118.768297442004, 475.750095909406, 306.243694918151, 
# 116.055824800264), .Names = c("DHA", "DHH", "T12H", "Rho25"))
# # pfixed <- c(K=82.33) or rK=82.33/39.33
# pfixed <- c(rK=2.093313)
# # K or rK are not used for dydt.linear or dydt.exponential
# resultNest_4p_weight <- searchR(parameters=x,  
# 	fixed.parameters=pfixed, temperatures=formated,  
# 	derivate=dydt.Gompertz, M0=1.7, test=c(Mean=39.33, SD=1.92),  
# 	method = "BFGS", weight=w)
# data(resultNest_4p_weight)
# pMCMC <- TRN_MHmcmc_p(resultNest_4p_weight, accept=TRUE)
# # Take care, it can be very long, sometimes several days
# result_mcmc_4p_weight <- GRTRN_MHmcmc(result=resultNest_4p_weight,  
# 	parametersMCMC=pMCMC, n.iter=10000, n.chains = 1, n.adapt = 0,  
# 	thin=1, trace=TRUE)
# data(result_mcmc_4p_weight)
# plot(result_mcmc_4p_weight, parameter="T12H", main="", xlim=c(290, 320), bty="n")
# plotR(resultNest_4p_weight, SE=result_mcmc_4p_weight$SD, 
#  ylim=c(0,0.3), las=1)
# data(resultNest_4p)
# data(result_mcmc_4p)
# par(xpd=TRUE)
# plotR(list(resultNest_4p_weight, resultNest_4p), 
#  SE=list(result_mcmc_4p_weight$SD, result_mcmc_4p$SD), 
#  ylim=c(0,0.4), las=1, col=list("red", "black"), 
#  legend=list("Maximum entropy weighted", "Not weighted"))
# ## End(Not run)