MyNixOS website logo
Description

Stratified-Petersen Analysis System.

The Stratified-Petersen Analysis System (SPAS) is designed to estimate abundance in two-sample capture-recapture experiments where the capture and recaptures are stratified. This is a generalization of the simple Lincoln-Petersen estimator. Strata may be defined in time or in space or both, and the s strata in which marking takes place may differ from the t strata in which recoveries take place. When s=t, SPAS reduces to the method described by Darroch (1961) <doi:10.2307/2332748>. When s<t, SPAS implements the methods described in Plante, Rivest, and Tremblay (1988) <doi:10.2307/2533994>. Schwarz and Taylor (1998) <doi:10.1139/f97-238> describe the use of SPAS in estimating return of salmon stratified by time and geography. A related package, BTSPAS, deals with temporal stratification where a spline is used to model the distribution of the population over time as it passes the second capture location. This is the R-version of the (now obsolete) standalone Windows program available at <https://home.cs.umanitoba.ca/~popan/spas/spas_home.html>.

SPAS

Stratified Petersen Analysis System in R

Versions and installation

  • CRAN Download the SPAS package

  • Github To install the latest development version from Github, install the newest version of the devtools package; then run

devtools::install_github("cschwarz-stat-sfu-ca/SPAS", dependencies = TRUE,
                        build_vignettes = TRUE)

Features

This is an R version of the Windoze program SPAS to estimate population abundance using a Stratified Petersen Estimator (Darroch 1961; Plante et al 1998; Schwarz and Taylor, 1998)

The user is allows to pool rows and/or columns prior to analysis but the number of rows must be less than or equal to the number of columns (s <= t). The conditional likelihood formulation of Plante et al (1998) is used to estimate the parameters.

A good discussion of how to decide on pooling rows/columns is found in Schwarz and Taylor (1998). The row.physical.pool parameter allows you to choose between physical pooling of rows, or logical pooling of rows (the underlying data table is unchanged, but capture probabilities for the pooled rows are forced equal). It is not possible to do logical pooling of columns and only physical pooling is possible. See the help() function for details.

If the data are physically pooled prior to analysis, it is not possible to compare different poolings to see which is most appropriate using AIC or likelihood ratio tests. If you do logical pooling of rows, you can compare poolings using AIC or likelihood ratio methods.

Optimization is now done using the TMB package (a relative of ADMB) which seems to have fixed the convergence issues that plagued earlier versions of SPAS-R.

References

Darroch, J. N. (1961). The two-sample capture-recapture census when tagging and sampling are stratified. Biometrika, 48, 241-260. https://www.jstor.org/stable/2332748

Plante, N., L.-P Rivest, and G. Tremblay. (1988). Stratified Capture-Recapture Estimation of the Size of a Closed Population. Biometrics 54, 47-60. https://www.jstor.org/stable/2533994

Schwarz, C. J., & Taylor, C. G. (1998). The use of the stratified-Petersen estimator in fisheries management with an illustration of estimating the number of pink salmon (Oncorhynchus gorbuscha) that return to spawn in the Fraser River. Canadian Journal of Fisheries and Aquatic Sciences, 55, 281-296. https://doi.org/10.1139/f97-238

Metadata

Version

2024.1.31

License

Unknown

Platforms (75)

    Darwin
    FreeBSD
    Genode
    GHCJS
    Linux
    MMIXware
    NetBSD
    none
    OpenBSD
    Redox
    Solaris
    WASI
    Windows
Show all
  • aarch64-darwin
  • aarch64-genode
  • aarch64-linux
  • aarch64-netbsd
  • aarch64-none
  • aarch64_be-none
  • arm-none
  • armv5tel-linux
  • armv6l-linux
  • armv6l-netbsd
  • armv6l-none
  • armv7a-darwin
  • armv7a-linux
  • armv7a-netbsd
  • armv7l-linux
  • armv7l-netbsd
  • avr-none
  • i686-cygwin
  • i686-darwin
  • i686-freebsd
  • i686-genode
  • i686-linux
  • i686-netbsd
  • i686-none
  • i686-openbsd
  • i686-windows
  • javascript-ghcjs
  • loongarch64-linux
  • m68k-linux
  • m68k-netbsd
  • m68k-none
  • microblaze-linux
  • microblaze-none
  • microblazeel-linux
  • microblazeel-none
  • mips-linux
  • mips-none
  • mips64-linux
  • mips64-none
  • mips64el-linux
  • mipsel-linux
  • mipsel-netbsd
  • mmix-mmixware
  • msp430-none
  • or1k-none
  • powerpc-netbsd
  • powerpc-none
  • powerpc64-linux
  • powerpc64le-linux
  • powerpcle-none
  • riscv32-linux
  • riscv32-netbsd
  • riscv32-none
  • riscv64-linux
  • riscv64-netbsd
  • riscv64-none
  • rx-none
  • s390-linux
  • s390-none
  • s390x-linux
  • s390x-none
  • vc4-none
  • wasm32-wasi
  • wasm64-wasi
  • x86_64-cygwin
  • x86_64-darwin
  • x86_64-freebsd
  • x86_64-genode
  • x86_64-linux
  • x86_64-netbsd
  • x86_64-none
  • x86_64-openbsd
  • x86_64-redox
  • x86_64-solaris
  • x86_64-windows