Tagloss_model: Return the daily rate of tag loss.

In phenology: Tools to Manage a Parametric Function that Describes Phenology and More

Return the daily rate of tag loss.

Description

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.

Usage

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

Details

Tagloss_model returns the daily rate of tag loss.

Value

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.

Author(s)

Marc Girondot [e-mail]

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()

Examples

## 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)

phenology documentation built on Sept. 11, 2024, 6:07 p.m.