batchMarkOptim {extBatchMarking}

Marked model only.

Description

batchMarkOptim function provides the batch marking function to be optimized.

Usage

batchMarkOptim(
  par = NULL,
  data,
  choiceModel = c("model1", "model2", "model3", "model4"),
  method = c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B"),
  parallel = FALSE,
  lowerBound = -Inf,
  cores = 1,
  hessian = FALSE,
  control,
  ...
)

Arguments

par

Initial values for the parameters to be optimized over.

data

A capture-recapture data matrix or data frame

choiceModel

This chooses among different models and allow for model selection

method

The method to be used. See optim for details.

parallel

Logical. Should the algorithm be run in parallel? This will be implemented in a future version.

lowerBound

Lower bounds on the variables for the "L-BFGS-B" method.

cores

The number of cores for parallelization

hessian

Logical. Should a numerically differentiated Hessian matrix be returned?

control

A list of control parameters. See optim for details.

...

Further arguments to be passed by user which goes into the optim function.

Details

Note that arguments after ... must be matched exactly. batchMarkOptim depends on optim function to optimize the parameters of the marked model only. By default optim performs minimization.

Value

For batchMarkOptim, a list with components:

phi

The survival probability and remaining in the population between occasion t and t+1.

p

The capture probability at occasion time t.

ll

The optimized log-likelihood value of marked model.

hessian

The hessian matrix.

AIC

The Akaike Information Criteria for model selection.

References

Laura L. E. Cowen, Panagiotis Besbeas, Byron J. T. Morgan, 2017.: Hidden Markov Models for Extended Batch Data, Biometrics, 73, 1321-1331. DOI: 10.1111/biom.12701.

Examples

# Load the package
library(extBatchMarking)

# Load the WeatherLoach data from Cowen et al., 2017.
data(WeatherLoach)

# Initial parameter values
theta <- c(0, -1)


mod1 <- batchMarkOptim(
           par         = theta,
           data        = WeatherLoach,
           choiceModel = "model4",
           method      = "BFGS",
           parallel    = FALSE,
           hessian     = TRUE,
           control     = list(trace = 1)
     )

 # Survival probability
 mod1$phi
 # Capture probability
 mod1$p
 # Optimized log-likelihood
 mod1$ll
 # The Hessian matrix
 mod1$hessian
 # The Aikaike Information Criteria
 mod1$AIC
 

 
 mod2 <- batchMarkOptim(
           par         = theta,
           data        = WeatherLoach,
           choiceModel = "model4",
           method      = "L-BFGS-B",
           parallel    = FALSE,
           hessian     = TRUE,
           control     = list(trace = 1))

 # Survival probability
 mod2$phi
 # Capture probability
 mod2$p
 # Optimized log-likelihood
 mod2$ll
 # The Hessian matrix
 mod2$hessian
 # The Akaike Information Criteria
 mod2$AIC