✦ LIBER ✦

Allometry and Model II Non-linear Regression

✍ Scribed by Thomas A. Ebert; Michael P. Russell

Publisher: Elsevier Science
Year: 1994
Tongue: English
Weight: 264 KB
Volume: 168
Category: Article
ISSN: 0022-5193
DOI: 10.1006/jtbi.1994.1116

No coin nor oath required. For personal study only.

✦ Synopsis

For many allometry problems, morphological variables, (x) and (y), can be transformed using logarithms and linear techniques used to estimate parameters and compare samples. Because both (x) and (y) are subject to errors, Model II regression has been advocated for such analyses. When data, such as gonad weight or egg number, are analyzed using a more complex allometry equation such as (y=\alpha x^{\beta}+\gamma) or (y=\alpha(x-y)^{B}), non-linear regression techniques must be used. We present a Model II non-linear analog of reduced major-axis (RMA) regression that minimizes areas similar to triangles that are minimized in RMA regression. Data for two tropical sea urchins, Salmacis belli and Heterocentrotus mammillatus, illustrate the method.

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