embryogrowth (version 6.1.1)

embryogrowth-package: The package embryogrowth

Description

Tools to analyze the embryo growth and the sexualisation thermal reaction norms.

Details

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

ll{ Package: embryogrowth Type: Package Version: 6.1.1 - build 467 Date: 2015-10-16 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

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