mra-package: MRA - Mark Recapture Analysis
Description
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) is the real parameter (capture or survival)
for individual i at capture occasion j.
Currently, 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 out of consulting work on 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 did an almost complete revision of 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).
Throughout the process, several statisticians, including Dr. Manly,
Dr. Jeff Laake and Dr. Gary White, have
provided comments that helped shape the approach.Details
ll{
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 A function called by other routines that returns a
3-D design matrix.
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.nhat Pring method for size estimates from a CJS model
residuals.cjs Residuals for CJS modelsReferences
Amstrup, S.C., T.L. McDonald, and B.F.J. Manly. 2005. Handbook of
Capture-Recapture Analysis, Princeton: Princeton University Press.