ChangeSSM {embryogrowth}

Generate set of parameters for different forms of thermal norm of reaction

Description

Generate a set of parameters for thermal reaction norm model.
If initial.parameters is NULL and resultmcmc is not NULL, it will generate parameters and SE based on the average of the curves.

Usage

ChangeSSM(
  result = NULL,
  resultmcmc = NULL,
  temperatures = seq(from = 20, to = 35, by = 0.1),
  parameters = NULL,
  initial.parameters = NULL,
  fixed.parameters = NULL,
  outmcmc = "quantiles",
  progressbar = TRUE,
  ...
)

Arguments

result

A result obtained by searchR()

resultmcmc

A result obtained by GRTRN_MHmcmc()

temperatures

A vector with incubation temperatures in degrees Celsius

parameters

A vector of parameters for model to be converted. Not necessary if result is provided.

initial.parameters

NULL or a vector of parameters for initial model model to be fited

fixed.parameters

NULL of a vector of parameters to be used but fixed

outmcmc

What statistic will be estimated if a mcmc is provided. Can be "mean-sd" or "quantiles".

progressbar

If TRUE, a progressbar is shown

...

A control list to be used with optim, see ?optim

Details

ChangeSSM convert different forms of thermal norm of reaction

Value

A vector with parameters or a result object formatted with new parameters is result is non null

Author(s)

Marc Girondot

Examples

## Not run: 
data(resultNest_6p_SSM)
x1 <- resultNest_6p_SSM$par
data(resultNest_4p_SSM)
x2 <- resultNest_4p_SSM$par
temperaturesC <- (200:350)/10
s <- ChangeSSM(temperatures=temperaturesC, parameters=x1, initial.parameters=x2)
sY <- plotR(resultNest_6p_SSM, ylim=c(0,3), col="black", curve = "ML")
plotR(resultNest_4p_SSM, col="red", scaleY=sY, new=FALSE)
plotR(s$par, col="green", scaleY=sY, new=FALSE, curve = "ML")
legend("topleft", legend=c("r function to mimic", "Initial new r function", 
"Fitted new r function"), lty=c(1, 1, 1), col=c("black", "red", "green"))
# Other example to fit anchored parameters
data(resultNest_4p_SSM)
x0 <- resultNest_4p_SSM$par
t <- hist(resultNest_4p_SSM, 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))
 # Example on how to generate a set of SSM parameters from anchored parameters
 xanchor <- GenerateAnchor(nests=resultNest_4p_SSM)
 x <- resultNest_4p_SSM$par
 xanchor["294"] <- 0
 xanchor["308"] <- 2.3291035
 x <- ChangeSSM(parameters = xanchor,
                     initial.parameters = x, control=list(maxit=5000))
 sY <- plotR(resultNest_4p_SSM$par, ylim = c(0,3), curve="ML")
 plotR(xprime$par, col="red", scaleY=sY, new=FALSE, curve="ML") 
 legend("topleft", legend=c("Fitted parameters", "Constrainted parameters"), lty=1, 
        col=c("black", "red"))
 # Weibull model
 x <- ChangeSSM(temperatures = (200:350)/10,
                parameters = resultNest_4p_SSM$par,
                initial.parameters = structure(c(73, 300, 26), 
                                               .Names = c("k", "lambda", "scale")), 
                control=list(maxit=1000))
 # normal asymmetric model
 x <- ChangeSSM(temperatures = (200:350)/10,
               parameters = resultNest_4p_SSM$par,
               initial.parameters = structure(c(3, 10, 8, 32), 
               .Names = c("Scale", "sdL", "sdH", "Peak")), 
               control=list(maxit=1000))
 # trigonometric model
 x <- ChangeSSM(temperatures = (200:350)/10,
               parameters = resultNest_4p_SSM$par,
               initial.parameters = structure(c(3, 20, 40, 32), 
               .Names = c("Max", "LengthB", "LengthE", "Peak")), 
               control=list(maxit=1000))

 # example with a mcmc object, CI being 2.SD
 # Note the symmetric CI
data(resultNest_mcmc_4p_SSM)
new_result <- ChangeSSM(resultmcmc = resultNest_mcmc_4p_SSM, result = resultNest_4p_SSM,
                        temperatures = seq(from = 20, to = 35, by = 0.1), 
                        outmcmc = "mean-sd", 
                        initial.parameters = NULL)

plotR(new_result, ylim=c(0, 3), curve="ML")
 # example with a mcmc object, CI being defined by 2.5%-97.5% quantiles
 # Note the asymmetric CI
data(resultNest_mcmc_4p_SSM)
new_result <- ChangeSSM(resultmcmc = resultNest_mcmc_4p_SSM, result = resultNest_4p_SSM,
                        temperatures = seq(from = 20, to = 35, by = 0.1), 
                        outmcmc = "quantiles", 
                        initial.parameters = NULL)
 
plotR(new_result, ylim=c(0, 3), curve="ML")
plotR(new_result, ylim=c(0, 3), curve="ML quantiles")

# A little trick
# to convert SSM4 to SSM6, you can use:

x4 <- c('DHA' = 69.718935117894063, 
         'DHH' = 497.81709040501079, 
         'T12H' = 308.95543713889509, 
         'Rho25' = 255.24186073771696)

x6 <- c(x4["DHA"], 
.        x4["DHH"], 
.        'DHL' = x4[["DHH"]], 
.        'DT' = 0.5, 
.        'T12L' = x4[["T12H"]], 
.        x4['Rho25'])

## End(Not run)