js {marked}

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.