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Last data update: 2014.03.03

R: Fit the parameters that best represent nest incubation data.

searchR

R Documentation

Fit the parameters that best represent nest incubation data.

Description

Fit the parameters that best represent data.
test can be a list with two elements Mean and SD and each element is a named vector with the nest name.

Usage

searchR(parameters = stop("Initial set of parameters must be provided"),
  fixed.parameters = NULL,
  temperatures = stop("Formated temperature must be provided !"),
  derivate = dydt.Gompertz, test = c(Mean = 39.33, SD = 1.92), M0 = 1.7,
  saveAtMaxiter = FALSE, fileName = "intermediate", weight = NULL,
  hessian = TRUE, control = list(trace = 1, REPORT = 100, maxit = 500))

Arguments

`parameters`	A set of parameters used as initial point for searching
`fixed.parameters`	A set of parameters that will not be changed
`temperatures`	Timeseries of temperatures after formated using FormatNests()
`derivate`	Function used to fit embryo growth: dydt.Gompertz, dydt.exponential or dydt.linear
`test`	A vector with Mean and SD of size of hatchlings, ex. test=c(Mean=39, SD=3)
`M0`	Measure of hatchling size or mass proxi at laying date
`saveAtMaxiter`	If True, each time number of interation reach maxiter, current data are saved in file with filename name
`fileName`	The intermediate results are saved in file with fileName.Rdata name
`weight`	A named vector of the weight for each nest for likelihood estimation
`hessian`	If TRUE, the hessian matrix is estimated and the SE of parameters estimated.
`control`	List for control parameters for optimx

Details

searchR fits the parameters that best represent nest incubation data.

Value

A result object

Author(s)

Marc Girondot

Examples

## Not run: 
library(embryogrowth)
data(nest)
formated <- FormatNests(nest)
# The initial parameters value can be:
# "T12H", "DHA",  "DHH", "Rho25"
# Or
# "T12L", "DT", "DHA",  "DHH", "DHL", "Rho25"
# K for Gompertz must be set as fixed parameter or being a constant K  
# or relative to the hatchling size rK
x <- structure(c(106.59891311201, 614.181133951497, 306.267053513175, 
120.327257089974), .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)
plot(resultNest_4p, xlim=c(0,70), ylimT=c(22, 32), ylimS=c(0,45), series=1)
x <- structure(c(106.567809092008, 527.359011254683, 614.208632495199, 
2720.94506457237, 306.268259715624, 120.336791245212), .Names = c("DHA", 
"DHH", "DHL", "DT", "T12L", "Rho25"))
pfixed <- c(rK=2.093313)
resultNest_6p <- searchR(parameters=x, fixed.parameters=pfixed, 
	temperatures=formated, derivate=dydt.Gompertz, M0=1.7, 
	test=c(Mean=39.33, SD=1.92))
data(resultNest_6p)
pMCMC <- TRN_MHmcmc_p(resultNest_6p, accept=TRUE)
# Take care, it can be very long, sometimes several days
result_mcmc_6p <- GRTRN_MHmcmc(result=resultNest_6p,  
	parametersMCMC=pMCMC, n.iter=10000, n.chains = 1, n.adapt = 0,  
	thin=1, trace=TRUE)
data(result_mcmc_6p)
# compare_AIC() is a function from the package "HelpersMG"
compare_AIC(test1=resultNest_4p, test2=resultNest_6p)
############ with new parametrization
data(resultNest_4p)
x0 <- resultNest_4p$par
t <- hist(resultNest_4p, plot=FALSE)
x <- c(3.4, 3.6, 5.4, 5.6, 7.6, 7.5, 3.2)
names(x) <- seq(from=range(t$temperatures)[1], to=range(t$temperatures)[2], 
     length.out=7)
newx <- ChangeSSM(temperatures = (200:350)/10, parameters = x0, 
       initial.parameters = x, 
       control=list(maxit=5000))
pfixed <- c(rK=2.093313)
resultNest_newp <- searchR(parameters=newx, fixed.parameters=pfixed,
 temperatures=formated, derivate=dydt.Gompertz, M0=1.7,
 test=c(Mean=39.33, SD=1.92))
plotR_hist(resultNest_newp, ylim=c(0,0.3), xlimR=c(23, 34), ylimH=c(0, 0.3))
compare_AIC(test4p=resultNest_4p, 
            test6p=resultNest_6p, 
            testAnchor=resultNest_newp)

## End(Not run)