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Constrained Additive Ordination

  • Thomas W. Yee
Part of the Springer Series in Statistics book series (SSS)

Abstract

This chapter describes a nonparametric extension of RR-VGLMs and QRR-VGLMs, called RR-VGAMs, which enable constrained additive ordination (CAO) to be performed. RR-VGAMs are VGAMs fitted to a set of latent variables and some other explanatory variables \(\mbox{ $\boldsymbol{x}$}_{1}\). Unfortunately, only rank-1 models are currently implemented in VGAM, and to \(\mbox{ $\boldsymbol{x}$}_{1} = 1\), and to binary responses and Poisson counts only. Applied to multispecies data, one can see the ‘real’ shape of species’ response curves as a function of the dominant gradient—e.g., they are not constrained to be symmetric bell-shaped, as with QRR-VGLMs. A few practical suggestions are given to aid the use of the modelling function cao().

Keywords

Component Function Site Score Approximate Standard Error Hunting Spider Nonlinear Degree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Copyright information

© Thomas Yee 2015

Authors and Affiliations

  • Thomas W. Yee
    • 1
  1. 1.Department of StatisticsUniversity of AucklandAucklandNew Zealand

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