embryogrowth (version 6.4)

embryogrowth-package: The package embryogrowth

Description

Tools to analyze the embryo growth and the sexualisation thermal reaction norms. The lastest version of this package can always been installed using: install.packages("http://www.ese.u-psud.fr/epc/conservation/CRAN/embryogrowth.tar.gz", repos=NULL, type="source")

Details

Fit a parametric function that describes dependency of embryo growth to temperature

Package:

embryogrowth

Type:

Package

Version:

6.4 - build 534

Date:

2016-10-02

License:

GPL (>= 2)

LazyLoad:

yes

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.

See Also

Delmas, V., Prevot-Julliard, A.-C., Pieau, C. & Girondot, M. 2008. A mechanistic model of temperature-dependent sex determination in a Chelonian, the European pond turtle. Functional Ecology, 22, 84-93.

Girondot, M., Ben Hassine, S., Sellos, C., Godfrey, M. & Guillon, J.-M. 2010. Modeling thermal influence on animal growth and sex determination in Reptiles: being closer of the target gives new views. Sexual Development, 4, 29-38.

Girondot, M. 1999. Statistical description of temperature-dependent sex determination using maximum likelihood. Evolutionary Ecology Research, 1, 479-486.

Girondot, M., & Kaska, Y. (2014). Nest temperatures in a loggerhead- nesting beach in Turkey is more determined by sea surface temperature than air temperature. Journal of Thermal Biology, 47, 13-18.

Examples

## Not run: 
# library("embryogrowth")
# packageVersion("embryogrowth")
# data(nest)
# formated <- FormatNests(nest)
# # The initial parameters value can be:
# # "T12H", "DHA",  "DHH", "Rho25"
# # Or
# # "T12L", "DT", "DHA",  "DHH", "DHL", "Rho25"
# x <- structure(c(115.758929130522, 428.649022170996, 503.687251738993, 
# 12.2621455821612, 306.308841227278, 116.35048615105), .Names = c("DHA", 
# "DHH", "DHL", "DT", "T12L", "Rho25"))
# # or
# x <- structure(c(118.431040984352, 498.205702157603, 306.056280989839, 
# 118.189669472381), .Names = c("DHA", "DHH", "T12H", "Rho25"))
# # pfixed <- c(K=82.33) or rK=82.33/39.33
# pfixed <- c(rK=2.093313)
# resultNest_4p <- searchR(parameters=x, fixed.parameters=pfixed, 
# 	temperatures=formated, derivate=dydt.Gompertz, M0=1.7, 
# 	test=c(Mean=39.33, SD=1.92))
# data(resultNest_4p)
# pMCMC <- TRN_MHmcmc_p(resultNest_4p, accept=TRUE)
# # Take care, it can be very long, sometimes several days
# result_mcmc_4p <- GRTRN_MHmcmc(result=resultNest_4p,  
# 	parametersMCMC=pMCMC, n.iter=10000, n.chains = 1, n.adapt = 0,  
# 	thin=1, trace=TRUE)
# data(result_mcmc_4p)
# out <- as.mcmc(result_mcmc_4p)
# # This out obtained after as.mcmc can be used with coda package
# # plot() can use the direct output of GRTRN_MHmcmc() function.
# plot(result_mcmc_4p, parameters=1, xlim=c(0,550))
# plot(result_mcmc_4p, parameters=3, xlim=c(290,320))
# # summary() permits to get rapidly the standard errors for parameters
# summary(result_mcmc_4p)
# se <- result_mcmc_4p$SD
# ## End(Not run)