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.
Highlights
Note: this vignette is essentially Yee et al (2015).Capture–recapture (CR) surveys are widely used in ecology and epidemiology to estimate population sizes
We maximize the conditional likelihood models of Huggins (1989). This approach has become standard practice to carry out inferences when considering individual covariates, with several different software packages currently using this methodology, including: MARK (Cooch and White 2012), CARE-2 (Hwang and Chao 2003), and the R packages (R Core Team 2015): mra (McDonald 2012), RMark (Laake 2013) and Rcapture (Baillargeon and Rivest 2014, 2007), the latter package uses a log-linear approach, which can be shown to be equivalent to the conditional likelihood (Cormack 1989; Huggins and Hwang 2011)
Our goal is to provide users with an easy-to-use object-oriented VGAM structure, where four family-type functions based on the conditional likelihood are available to fit the eight models of Otis et al (1978)
Summary
Note: this vignette is essentially Yee et al (2015). Capture–recapture (CR) surveys are widely used in ecology and epidemiology to estimate population sizes. This approach has become standard practice to carry out inferences when considering individual covariates, with several different software packages currently using this methodology, including: MARK (Cooch and White 2012), CARE-2 (Hwang and Chao 2003), and the R packages (R Core Team 2015): mra (McDonald 2012), RMark (Laake 2013) and Rcapture (Baillargeon and Rivest 2014, 2007), the latter package uses a log-linear approach, which can be shown to be equivalent to the conditional likelihood (Cormack 1989; Huggins and Hwang 2011) These programs are quite user friendly, and allow modelling capture probabilities as linear functions of the covariates. The two appendices give some technical details relating to the first and second derivatives of the conditional log-likelihood, and the means
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
