BIVARIATE LINEAR MODELS IN BIOMETRY
Bivariate linear models in biometry. Sys!. Zool. 26:201-209.-This paper focuses on the estimation of parameters in the bivariate linear model, especially in the context of bivariate size-shape relationships or allometry. Existing estimation procedures (regression, major axis, reduced major axis) all...
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| Format: | Text |
| Language: | English |
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.453.474 http://www.faculty.biol.ttu.edu/strauss/Morphometrics/Readings/KuhryMarcus1977.pdf |
| Summary: | Bivariate linear models in biometry. Sys!. Zool. 26:201-209.-This paper focuses on the estimation of parameters in the bivariate linear model, especially in the context of bivariate size-shape relationships or allometry. Existing estimation procedures (regression, major axis, reduced major axis) all depend on a priori assumptions on the ratio of the residuals, usually called "errors, " in both variables. These assumptions are reviewed and evaluated. The Bartlett method is not independent of assumptions on the residuals as has been often claimed. A method which does not require assumptions on the ratio of residuals, providing data from a third variable are available, is given. All of the methods discussed are illustrated with data measured on planktonic Foraminifera. [Bivariate; allometry; regression; major axis; Foraminifera.] The means of measurements on a sample of organisms are strongly dependent on size fluctuations due to the age distribution of the sample, environmental fluctuations, and-in fossils-sedimentary sorting. Shape factors are in general found to be more |
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