Tagloss_fit: fit a model of tag loss using a CMR database.

Description

This function fits a model of tag loss using a CMR database. The names of parameters can be: Model Girondot and Pfaller (in preparation):

Left tag lost when 2 are present: D1_L2, D2D1_L2, D3D2_L2, A_L2, B_L2, C_L2, delta_L2
Right tag lost when 2 are present: D1_R2, D2D1_R2, D3D2_R2, A_R2, B_R2, C_R2, delta_R2
Left tag lost when 1 is present: D1_L1, D2D1_L1, D3D2_L1, A_L1, B_L1, C_L1, delta_L1
Right tag lost when 1 is present: D1_R1, D2D1_R1, D3D2_R1, A_R1, B_R1, C_R1, delta_R1
One tag lost when 2 are present: D1_2, D2D1_2, D3D2_2, A_2, B_2, C_2, delta_2
One tag lost when 1 is present: D1_1, D2D1_1, D3D2_1, A_1, B_1, C_1, delta_1

pA, pB and pC are the daily probabilities of tag loss with pA=-logit(A), pB=-logit(B) and pC=-logit(C) . delta is used as: p = p + delta. Nothe that delta can be negative Tag loss rate is pA at day 1 Tag loss rate changes gradually from pA to pB that is reached at day D1 Tag loss rate is pB from day D1 to day D2=D1+D2D1 Tag loss rate changes gradually from pB to pC that is reached at day D3=D2+D3D2

When parameters from Rivalan et al. (2005) are used:

One tag lost when 2 are present: a0_2, a1_2, a2_2, a3_2, a4_2, delta_2
One tag lost when 1 is present: a0_1, a1_1, a2_1, a3_1, a4_1, delta_1

When parameters from Casale et al. (2017) are used: Model I

One tag lost when 2 are present: CasaleModelIc_2
One tag lost when 1 is present: CasaleModelIc_1

Model II

One tag lost when 2 are present: CasaleModelIIa0_2, CasaleModelIIa1_2, CasaleModelIIa4_2
One tag lost when 1 is present: CasaleModelIIa0_1, CasaleModelIIa1_1, CasaleModelIIa4_1

Model III

One tag lost when 2 are present: CasaleModelIIIa0_2, CasaleModelIIIa1_2, CasaleModelIIIa4_2
One tag lost when 1 is present: CasaleModelIIIa0_1, CasaleModelIIIa1_1, CasaleModelIIIa4_1

Model IV

One tag lost when 2 are present: CasaleModelIVa0_2, CasaleModelIVa1_2, CasaleModelIVa2_2, CasaleModelIVa3_2, CasaleModelIVa4_2
One tag lost when 1 is present: CasaleModelIVa0_1, CasaleModelIVa1_1, CasaleModelIVa2_1, CasaleModelIVa3_1, CasaleModelIVa4_1

Model V

One tag lost when 2 are present: CasaleModelVa0_2, CasaleModelVa1_2, CasaleModelVa2_2, CasaleModelVa3_2, CasaleModelVa4_2
One tag lost when 1 is present: CasaleModelVa0_1, CasaleModelVa1_1, CasaleModelVa2_1, CasaleModelVa3_1, CasaleModelVa4_1

If only one parameter is fitted, method must be "Brent" and upper and lower parameters must be set up with finite values.

Usage

Tagloss_fit(data = stop("A database formated using Tagloss_format() must be used"),
  fitted.parameters = NULL, fixed.parameters = NULL,
  model_before = NULL, model_after = NULL, control = list(trace = 1,
  maxit = 10000), method = "Nelder-Mead", lower = -Inf, upper = Inf,
  hessian = FALSE, cores = detectCores(all.tests = FALSE, logical =
  TRUE), groups = NULL)

Arguments

data

An object formated using Tagloss_format

fitted.parameters

Set of parameters to be fitted

fixed.parameters

Set of fixed parameters

model_before

Transformation of parameters before to use Tagloss_model()

model_after

Transformation of parameters after to use Tagloss_model()

control

Control parameters to be send to optim()

method

optim() method

lower

Lower value for parameter when Brent method is used

upper

Upper value for parameter when Brent method is used

hessian

Does the hessian matrix should be estimated

cores

Number of cores to use for parallel computing

groups

Number of groups for parallel computing

Value

Return a list object with the model describing tag loss.

Details

Tagloss_fit fits a model of tag loss using a CMR database.

References

Rivalan, P., Godfrey, M.H., Pr<U+00E9>vot-Julliard, A.-C., Girondot, M., 2005. Maximum likelihood estimates of tag loss in leatherback sea turtles. Journal of Wildlife Management 69, 540-548.

Casale, P., Freggi, D., Salvemini, P., 2017. Tag loss is a minor limiting factor in sea turtle tagging programs relying on distant tag returns: the case of Mediterranean loggerhead sea turtles. European Journal of Wildlife Research 63.

Examples

Run this code

# NOT RUN {
library(phenology)
# Example
data_f_21 <- Tagloss_format(outLR, model="21")

# model fitted by Rivalan et al. 2005
par <- c(a0_2=-5.43E-2, a1_2=-103.52, a4_2=5.62E-4, 
         delta_1=3.2E-4)
pfixed <- c(a2_2=0, a3_2=0, a2_1=0, a3_1=0)
model_before <- "par['a0_1']=par['a0_2'];par['a1_1']=par['a1_2'];par['a4_1']=par['a4_2']"
o <- Tagloss_fit(data=data_f_21, fitted.parameters=par, fixed.parameters=pfixed, 
                 model_before=model_before)
plot(o, t=1:1000, model="cumul")
plot(o, t=1:1000, model="1")
plot(o, t=1:1000, model="2", add=TRUE, col="red")

# Same data fitted with new model
par <- c(D1_1 = 100.15324837975547, A_1 = 5.9576927964120188, 
         B_1 = 8.769924225871069, B_2 = 8.2353860179664125)
pfixed <- c(D2D1_1 = 2568, D3D2_1 = 2568, D2D1_2 = 2568, D3D2_2 = 2568)
o_4p_p1p2 <- Tagloss_fit(data=data_f, fitted.parameters = par, 
                         fixed.parameters = pfixed, 
                         model_before = "par['C_1']=par['B_1'];
                         par['A_2']=par['A_1'];
                         par['C_2']=par['B_2'];
                         par['D1_2']=par['D1_1']", hessian=TRUE)
                         
# Without the N20 the computing is much faster
data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20)))
par <- structure(c(48.8292784204825, 1039.02842229274, -89.3162940697861, 
5.21817463244988, 8.00575451188548, 8.32971268127933, 161.265553603601, 
602.935748681661, 2643.57415102633, 16.752815732218, 10.181616195839, 
7.14279063312016), .Names = c("D1_2", "D2D1_2", "D3D2_2", "A_2", 
"B_2", "C_2", "D1_1", "D2D1_1", "D3D2_1", "A_1", "B_1", "C_1"))
o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par)
plot(o, model="1", col="red")
plot(o, model="2", col="blue", add=TRUE)
legend("topright", legend=c("2->1", "1->0"), lty=1, col=c("blue", "red"))
# }

Run the code above in your browser using DataLab