Allometry and Model II Non-linear Regression
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 wei...
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| Published in | Journal of theoretical biology Vol. 168; no. 4; pp. 367 - 372 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.01.1994
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0022-5193 1095-8541 |
| DOI | 10.1006/jtbi.1994.1116 |
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| Summary: | 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 = α
x
β + γ or
y = α(
x - γ)
β, 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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| Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
| ISSN: | 0022-5193 1095-8541 |
| DOI: | 10.1006/jtbi.1994.1116 |