mra-package               package:mra               R Documentation

_M_R_A - _M_a_r_k _R_e_c_a_p_t_u_r_e _A_n_a_l_y_s_i_s

_D_e_s_c_r_i_p_t_i_o_n:

     *Description* -  This package contains analysis functions, and
     associated routines, to conduct  analyses of mark-recapture
     (capture-recapture) data using individual,  time, and
     individual-time varying covariates. In general, these routines 
     relate vectors of capture histories to vectors of covariates using
      a regression approach (Amstrup et al. 2005, Ch 9).  All capture,
     survival,  transition, etc. parameters are functions of individual
     and time  specific covariates, and the estimated parameters  are
     coefficients in logistic-linear equations. 

     *Relationship to MARK* -  For the most part, these routines
     perform a subset of the analyses available in  program MARK or via
     the MARK front-end package, RMark.   However, there are
     differences.  The most significant difference between this package
     and MARK is parameterization.  The parameterization used here does
     not utilize triangular  "parameter information matrices" (PIMs) as
     MARK (and RMark) does.   Because of this, the "design" matrix
     utilized by  this package is not parallel to the "design" matrix
     of program MARK.  For those new  to mark-recapture analysis, this
     parameterization difference will be inconsequential.   The
     approach taken here provides equivalent modeling flexibility, yet
     is  easier to grasp and visualize, in our opinion.  For those
     already familiar with the PIMs used  by program MARK, it is
     helpful to view the "PIMs" of this package as  rectangular
     matrices of the real parameters. I.e., the "PIMs" of this package
     are  rectangular matrices where cell (i,j) contains the real
     parameter (capture or survival)  for individual i at capture
     occasion j. 

     Analyses available here that are _not_ included in program MARK
     include:

        *  Estimation of population size from open population CJS
           models via  the Horvitz-Thompson estimator. 

        *  Residuals, goodness of fit tests, and associated plots for
           assessing model fit in open CJS models.

     *History* -  These routines grew from consulting work on multiple
     mark-recapture projects.  The original Fortran code, upon which
     the package is based, was written by Dr. Bryan Manly for a
     northern spotted owl similation project in 1991.   Dr. Manly is
     the one  who originally envisioned and programed the non-PIM (or
     rectangular PIM, if you  prefer) approach.  However, Dr. Manly did
     not realize what he had done.  In 1997, Dr. Trent McDonald almost
     completely revised the original  Fortran routines for use on a
     polar bear mark-recapture project.  At that time,  the routines
     were stand-alone Fortran executables.  Subsequent revisions 
     required by other projects included addition of closed-form
     variance estimates (originally,  variances were estimated by the
     bootstrap), the Horvitz-Thompson size estimates,  and goodness of
     fit testing.   In 2003, it finally dawned on Dr. McDonald how  to
     call a Fortran DLL from S-Plus and R, thus  eliminating the
     executable version and  allowing S-Plus or R to do front-end data
     manipulation and plotting.   S-Plus was abandoned in favor of R in
     2004.  After publication of Amstrup et al. (2005),  Dr. McDonald
     realized that an official R package with documentation was 
     needed, and learned how to make a package (not an easy process for
     him).  Version 1.X of MRA contained routines for open population
     CJS models only. Version 2.X of MRA added closed population
     Huggins model estimation routines. In future,  addition of a major
     model type will increment the primary version number, revisions 
     to routines for existing models will increment the secondary
     version number.  Thus,  the number of major model types available
     in MRA will be the primary version number. Throughout the process,
     several statisticians, including Dr. Manly,  Dr. Jeff Laake and
     Dr. Gary White, have  provided comments that helped shape the
     approach.

     *Ways You Can Help* -  It is a no-brainer that others have R
     routines that perform various mark-recapture analyzes.  The author
     of MRA views this effort as collaborative and  welcomes routines,
     comments, and assistance in developing MRA. The overall goal  is
     to develop MRA into a comprehensive repository for  mark-recapture
     analyzes in R.  Indeed, collaboration is the strength of R and
     open source code. 

     Along these lines, the author of MRA would especially welcome
     routines that  perform the following analyzes:

        *  Continuous time models.  Especially those that allow
           inclusion of covariates.

        *  Band recovery models.

        *  Baysian models.

        *  Joint live-dead recovery models.

        *  MCMC methods or routines that can be applied to exiting
           models. 

        *  Plotting methods for exiting models.

        *  Model selection methods for existing models. 

        *  Simulation methods and routines.

     The above is only a partial list.  All routines are welcome and
     will be considered.   Original authors  will of course be
     acknowledged in the routine's documentation.  Assistance in
     formating  documentation files is available.  I.e., either the
     author of MRA will write the  documentation or the routine's
     original author will be sent a text file to  edit that contains
     all the necessary mark-up  and sectioning. 

     If you have a routine that would be useful, email MRA's maintainer
      at the address below.  Supporting papers and data sets can be
     distributed  with MRA, and are encouraged.

_D_e_t_a_i_l_s:


       Package:  mra
       Type:     Package
       License:  GNU General Public License

     List of routines:


     F.cjs.covars            Returns matricies that can be used to fit
     a CJS model
     F.cjs.estim             Estimation of Cormack-Jolly-Seber (CJS)
     open population model
     F.cjs.gof               Goodness-of-fit tests for CJS models
     F.cr.model.matrix       Function that returns two 3-D design
     matricies.
     F.3d.model.matrix       Function that expands a formula into the
     type of 3-D array
                             needed for estimation.                             
     F.huggins.estim         Estimation of Huggin's closed population
     model
     F.sat.lik               Returns the saturated likelihood for a CJS
     model that does 
                             not contain individual covariates
     dipper.data             European Dipper data set
     lines.cjs               Lines method for cjs objects
     plot.cjs                Plot method for cjs objects
     predict.cjs             Predicted values for active cells of a CJS
     model. 
     print.cjs               Print method for cjs objects
     print.hug               Print method for hug (Huggin's model)
     objects
     print.nhat              Pring method for size estimates from a CJS
     model
     residuals.cjs           Residuals for CJS models
     ivar                    Function for specifying individual varying
     effects
     tvar                    Function for specifying time varying
     effects


_A_u_t_h_o_r(_s):

     Trent McDonald

     Maintainer: Trent McDonald <tmcdonald@west-inc.com>

_R_e_f_e_r_e_n_c_e_s:

     Amstrup, S.C., T.L. McDonald, and B.F.J. Manly. 2005. _Handbook of
      Capture-Recapture Analysis_, Princeton: Princeton University
     Press.

