phenology (version 7.2)

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: 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 A, B and C are -logit(pA), -logit(pB) and -logit(pC) of the corresponding daily probabilities (p) of tag loss. delta is used as: p = p * invlogit(-delta) The use of delta parameter is complicated. 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 D2+D3D2 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.

See Also

Other Model of Tag-loss: Tagloss_LengthObs, Tagloss_L, Tagloss_daymax, Tagloss_format, Tagloss_model, Tagloss_simulate, logLik.Tagloss, plot.Tagloss

Examples

# NOT RUN {
library(phenology)
# Example
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 <- 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"))
# }