Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Thomas W. Yee, Jakub Stoklosa, Richard M. Huggins
Title: The VGAM Package for Capture-Recapture Data Using the Conditional Likelihood
Abstract: It is well known that using individual covariate information (such as body weight or gender) to model heterogeneity in capture-recapture (CR) experiments can greatly enhance inferences on the size of a closed population. Since individual covariates are only observable for captured individuals, complex conditional likelihood methods are usually required and these do not constitute a standard generalized linear model (GLM) family. Modern statistical techniques such as generalized additive models (GAMs), which allow a relaxing of the linearity assumptions on the covariates, are readily available for many standard GLM families. Fortunately, a natural statistical framework for maximizing conditional likelihoods is available in the Vector GLM and Vector GAM classes of models. We present several new R functions (implemented within the VGAM package) specifically developed to allow the incorporation of individual covariates in the analysis of closed population CR data using a GLM/GAM-like approach and the conditional likelihood. As a result, a wide variety of practical tools are now readily available in the VGAM object oriented framework. We discuss and demonstrate their advantages, features and flexibility using the new VGAM CR functions on several examples.

Page views:: 3770. Submitted: 2013-12-23. Published: 2015-06-01.
Paper: The VGAM Package for Capture-Recapture Data Using the Conditional Likelihood     Download PDF (Downloads: 2445)
VGAM_0.9-8.tar.gz: R source package Download (Downloads: 180; 2MB)
v65i05.R: R example code from the paper Download (Downloads: 208; 24KB)
GAM_th_sims_prog.R: Supplementary R source code Download (Downloads: 190; 5KB)

DOI: 10.18637/jss.v065.i05

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.