# Return the daily rate of tag loss. This function compute a model of daily tag loss rate for days t based on a set of parameters, par or a fitted tag loss model in x. Parameters are described in `Tagloss_fit`. ```r Tagloss_model( t = NULL, par = NULL, Hessian = NULL, mcmc = NULL, model_before = NULL, model_after = NULL, model = stop("You must specify which tag loss rate you want."), method = NULL, replicates = NULL, x = NULL ) ``` ## Arguments - `t`: Time for which values of model must be estimated - `par`: Parameters - `Hessian`: Hessian matrix of parameters - `mcmc`: A mcmc result - `model_before`: Function to be used before estimation of daily tagloss rate - `model_after`: Function to be used after estimation of daily tagloss rate - `model`: The model of parameter to be used, can be 1, 2, L1, L2, R1 or R2 - `method`: Can be NULL, "delta", "SE", "Hessian", "MCMC", or "PseudoHessianFromMCMC" - `replicates`: Number of replicates to estimate se of output - `x`: A Tagloss fitted model ## Returns Return the daily rate of tag loss if hessian is null or a data.frame with distribution of daily rate of tag loss if hessian is not null. ## Details Tagloss_model returns the daily rate of tag loss. ## Examples ```r ## Not run: library(phenology) # Example t <- 1:1000 par <- c(D1=200, D2D1=100, D3D2=200, A=-logit(0.02), B=-logit(0.05), C=-logit(0.07)) y <- Tagloss_model(t, par, model="1") plot(x=t, y, type="l") par <- c(D1_1=200, D2D1_1=100, D3D2_1=200, A_1=-logit(0.02), B_1=-logit(0.05), C_1=-logit(0.07)) y <- Tagloss_model(t, par, model="1") phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="1") # Fig1A in Rivalan et al. 2005 (note an error for a0; a0 must be negative) par <- c(a0=-1E5, a1=-2000, a2=0, a3=2*max(t), a4=0.1) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Fig1B in Rivalan et al. 2005 par <- c(a0=-0.5, a1=-2000, a2=-0.001, a3=0, a4=0.1) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Fig1C in Rivalan et al. 2005 par <- c(a0=-1, a1=-6, a2=0, a3=0, a4=0) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Fig1D in Rivalan et al. 2005 par <- c(a0=-1, a1=-6, a2=0, a3=0, a4=0.1) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Fig1E in Rivalan et al. 2005 par <- c(a0=-0.1, a1=-10, a2=-0.2, a3=60, a4=0.1) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Fig1F in Rivalan et al. 2005 par <- c(a0=-0.1, a1=-10, a2=0.2, a3=60, a4=0.1) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Example with fitted data data_f_21 <- Tagloss_format(outLR, model="21") # Without the N20 the computing is much faster data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) par <- c('D1_2' = 49.086835072129126, 'D2D1_2' = 1065.0992647723231, 'D3D2_2' = 6.15531475922079, 'A_2' = 5.2179675647973758, 'B_2' = 8.0045560376751386, 'C_2' = 8.4082505219581876, 'D1_1' = 177.23337287498103, 'D2D1_1' = 615.42690323741033, 'D3D2_1' = 2829.0806609455867, 'A_1' = 28.500118091731551, 'B_1' = 10.175426055942701, 'C_1' = 6.9616630417169398) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par) t <- 1:10 y <- Tagloss_model(t, o$par, model="1") y <- Tagloss_model(t, x=o, method=NULL, model="1") y <- Tagloss_model(t, x=o, method="Hessian", model="1", replicates=1000) ## End(Not run) ``` ## See Also Other Model of Tag-loss: `Tagloss_L()`, `Tagloss_LengthObs()`, `Tagloss_cumul()`, `Tagloss_daymax()`, `Tagloss_fit()`, `Tagloss_format()`, `Tagloss_mcmc()`, `Tagloss_mcmc_p()`, `Tagloss_simulate()`, `logLik.Tagloss()`, `o_4p_p1p2`, `plot.Tagloss()`, `plot.TaglossData()` ## Author(s) Marc Girondot marc.girondot@gmail.com

Tagloss_model function