# Generate set of parameters for different forms of thermal norm of reaction 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. ```r 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 ## Returns A vector with parameters or a result object formatted with new parameters is result is non null ## Details ChangeSSM convert different forms of thermal norm of reaction ## Examples ```r ## 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) ``` ## Author(s) Marc Girondot

ChangeSSM function