Workshops & Symposia
Nutrition Modeling in R is an important component of nutritional research as diet formulation is a quantitative process. To design diets that meet or exceed the nutrient requirements of the species of interest, one must be able to predict the absorbed nutrient supply and the animal needs for those nutrients, or more robustly, animal responses to the varying supply of the nutrients. Thus the construction and parameterization of models is prevalent in animal nutrition research. In the Nutrition Models Workshop, you will learn modeling approaches required to construct, parameterize, and evaluate a model.
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Tutorial on R. Veridiana Daley, Land O'Lakes. |
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Model construction. Timothy Hackmann, University of California, Davis. |
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Evaluating model predictions. Henk van Lingen, University of California, Davis. |
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Meta-analysis. Veridiana Daley, Land O'Lakes, Davis. |
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Building a nutrient requirement model. Mark Hanigan, Virginia Tech. |
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Linear Models and Meta-Regression (Theoretical background including instructions for practical, followed by hands-on examples) Dr. Kristan Reed Cornell University, USA |
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Mechanistic Models (Theoretical background including instructions for practical, followed by hands-on examples). Dr. Mark Hanigan, Virginia Tech |
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Install R and RStudio - NANP and Virginia Tech Dr. VL Daley and Members of Dr. Hanigan's Lab (Xinbei, Alvaro, Leticia, Jacquelyn, Alexis) |
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ASAS-NANP Symposium Introduction |
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Introduction to mathematical models, Mark D. Hanigan Virginia Tech, Virginia, USA |
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Building Models for Animal Production and Management with System Dynamics Modeling. Benjamin L. Turner, Texas A&M University-Kingsville |
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Introduction to R and R Scripting Robin R. White, Department of Animal and Poultry Sciences, Virginia Tech. |
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Assessing the predictive adequacy of simple and complex mathematical models Luis O. Tedeschi, Texas A&M University. |
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Overview and Case Studies of Cutting Edge Artificial Intelligence Techniques. Hector Menendez, Texas A&M University. |
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Welcoming remarks. John McNamara, Washington State University. |
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Tutorial on R.Timothy Hackmann, University of California, Davis. |
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Estimation of Parameter Values: lecture. Mark Hanigan, Virginia Tech. |
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Lesson 1: Estimation of Parameter Values: exercises. Mark Hanigan, Virginia Tech. |
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Bootstrap and Cross-Validation: lecture. Ranga Appuhamy, Iowa State University Ames. |
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Lesson 2: Bootstrap and Cross-Validation: exercises. Ranga Appuhamy, Iowa State University Ames. |
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Automated Model Selection: lecture. Veridiana L. Daley, University of Kentucky. |
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Lesson 3: Automated Model Selection: exercises. Veridiana L. Daley, University of Kentucky. |
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Molly and other dynamic models: lecture. Heidi Rossow, University of California, Davis. |
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Lesson 4: Molly and other dynamic models: exercises. Heidi Rossow, University of California, Davis. |
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Relevance and Collaboration with the National Research Council. Merlin D. Lindemann, University of Kentucky. The National Animal Nutrition Program (NANP). Profile |
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Food and Agriculture Cyberinformatics and Tools. Charlotte Kirk Baer. National Program Leader. National Institute of Food and Agriculture US Department of Agriculture. Profile |
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The evolution of mathematical models for animal nutrition: what to expect next? Luis Tedeschi, Texas A&M University. Profile |
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Combining simplicity and complexity: creating user-applications from mechanistic nutritional models. Jaap van Milgen, INRA. Profile |
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Modeling the impact of climate change on whole farm systems. Andrew D. Moore, Digiscape Future Science Platform, CSIRO. Profile |
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Decision support for foot-and-mouth disease emergency preparedness: the use of computer modeling and visual analytical tools to evaluate control strategies. Lindsey Holmstrom, USDA-NAHMS. Profile |
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Modeling complex problems with system dynamics: Applications in Animal Agriculture. Charles Nicholson, College of Agriculture and Life Sciences. Profile |
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Innovative ways to see data. John C. Hart, University of Illinois at Urbana-Champaign. Profile |
2018 NANP Nutrition Models Workshop. ADSA® Annual Meeting - Knoxville, Tennessee
Download here
1) Download and Install R
2) Download and Install R Studio (after install the R)
3) Tutorial: Install R packages
In RStudio, you will need to install several packages (caret, deSolve, epiR, ggplot2, lmerTest, lme4, MuMIn, plyr, reshape2). This tutorial explains how to install the packages (as well as how to install R and RStudio).
4) Download the R script and install the packages