comparison of methods of fitting several models to nutritional response data

A variety of models have been proposed to fit nutritional input-output response data. The models are typically nonlinear; therefore, fitting the models usually requires sophisticated statistical software and training to use it. An alternative tool for fitting nutritional response models was develope...

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Bibliographic Details
Published in:Journal of animal science 2008-02, Vol.86 (2), p.500-507
Main Authors: Vedenov, D, Pesti, G.M
Format: Article
Language:English
Subjects:
Algorithms
Animal Feed
animal nutrition
Animal Nutritional Physiological Phenomena - physiology
Animal productions
Animals
Biological and medical sciences
Comparative analysis
computer software
Computers
data analysis
data collection
Fundamental and applied biological sciences. Psychology
Mathematical models
Mathematics
Models, Biological
Models, Statistical
nonlinear models
nutrient requirements
Nutrition
Nutritional Requirements
research methods
Software
Spreadsheets
statistical analysis
Terrestrial animal productions
Vertebrates
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Summary:A variety of models have been proposed to fit nutritional input-output response data. The models are typically nonlinear; therefore, fitting the models usually requires sophisticated statistical software and training to use it. An alternative tool for fitting nutritional response models was developed by using widely available and easier-to-use Microsoft Excel software. The tool, implemented as an Excel workbook (NRM.xls), allows simultaneous fitting and side-by-side comparisons of several popular models. This study compared the results produced by the tool we developed and PROC NLIN of SAS. The models compared were the broken line (ascending linear and quadratic segments), saturation kinetics, 4-parameter logistics, sigmoidal, and exponential models. The NRM.xls workbook provided results nearly identical to those of PROC NLIN. Furthermore, the workbook successfully fit several models that failed to converge in PROC NLIN. Two data sets were used as examples to compare fits by the different models. The results suggest that no particular nonlinear model is necessarily best for all nutritional response data.
ISSN:0021-8812
1525-3163
DOI:10.2527/jas.2007-0536