js: Fitting function for Jolly-Seber model using Schwarz-Arnason...

In marked: Mark-Recapture Analysis for Survival and Abundance Estimation

Fitting function for Jolly-Seber model using Schwarz-Arnason POPAN formulation

Description

A function for computing MLEs for a specified Jolly-Seber open population capture-recapture model for processed dataframe x with user specified formulas in parameters that create list of design matrices dml. This function can be called directly but is most easily called from crm that sets up needed arguments.

Usage

js(
  x,
  ddl,
  dml,
  model_data = NULL,
  parameters,
  accumulate = TRUE,
  initial = NULL,
  method = "BFGS",
  hessian = FALSE,
  debug = FALSE,
  chunk_size = 1e+07,
  refit,
  itnmax = NULL,
  control = NULL,
  scale,
  ...
)

Arguments

x	processed dataframe created by process.data
ddl	list of dataframes for design data; created by call to make.design.data
dml	list of design matrices created by create.dm from formula and design data
model_data	a list of all the relevant data for fitting the model including imat, Phi.dm,p.dm,Phi.fixed,p.fixed, and time.intervals. It is used to save values and avoid accumulation again if the model was re-rerun with an additional call to js when using autoscale or re-starting with initial values. It is stored with returned model object.
parameters	equivalent to model.parameters in crm
accumulate	if TRUE will accumulate capture histories with common value and with a common design matrix for Phi and p to speed up execution
initial	initial values for parameters if desired; if named vector from previous run it will match to columns with same name
method	method to use for optimization; see optimx
hessian	if TRUE will compute and return the hessian
debug	if TRUE will print out information for each iteration
chunk_size	specifies amount of memory to use in accumulating capture histories; amount used is 8*chunk_size/1e6 MB (default 80MB)
refit	non-zero entry to refit
itnmax	maximum number of iterations
control	control string for optimization functions
scale	vector of scale values for parameters
...	any remaining arguments are passed to additional parameters passed to optimx or js.lnl

Details

It is easiest to call js through the function crm. Details are explained there.

Be cautious with this function at present. It does not include many checks to make sure values like fixed values will remain in the specified range of the data. Normally this would not be a big problem but because js.lnl calls an external FORTRAN subroutine via cjs.lnl, if it gets a subscirpt out of bounds, it will cause R to terminate. So make sure to save your workspace frequently if you use this function in its current implementation.

Value

The resulting value of the function is a list with the class of crm,js such that the generic functions print and coef can be used.

beta	named vector of parameter estimates
lnl	-2*log likelihood
AIC	lnl + 2* number of parameters
convergence	result from optimx; if 0 optimx thinks it converged
count	optimx results of number of function evaluations
reals	dataframe of data and real Phi and p estimates for each animal-occasion excluding those that occurred before release
vcv	var-cov matrix of betas if hessian=TRUE was set

Author(s)

Jeff Laake

References

Schwarz, C. J., and A. N. Arnason. 1996. A general methodology for the analysis of capture-recapture experiments in open populations. Biometrics 52:860-873.

marked documentation built on Oct. 19, 2023, 5:06 p.m.