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().
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© 2015 Thomas Yee
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Yee, T.W. (2015). Constrained Additive Ordination. In: Vector Generalized Linear and Additive Models. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2818-7_7
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DOI: https://doi.org/10.1007/978-1-4939-2818-7_7
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4939-2817-0
Online ISBN: 978-1-4939-2818-7
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