Agronomy and Horticulture, Department of
Analysis of Augmented Block Design Using R, Part 3: Analyzing Treatments as Random Effects
Document Type
Learning Object
Date of this Version
2014
Citation
Plant and Soil Sciences eLibrary (PASSeL) Lessons.
Abstract
Overview
When planning any science research project to gain information for a particular goal or objective, much thought is placed into what is called the experimental design or layout of the experiment. This is important to ensure the researcher is able to obtain useful data which can later be analyzed and provide information as to whether or not the scientific hypothesis being tested is supported or rejected. In this eLesson series we will look at a plant breeding experiment which uses the Augmented Design and utilize tools in R to analyze the data.
Objectives
After completing this third part of the eLesson series, you will be able to :
- Apply concepts of R programming for data preparation and analysis in a plant breeding experiment using an augmented design.
- Distinguish between a fixed effects parameter and a random effects parameter designation.
- Formulate the underlying linear model in R and fit a linear mixed model.
- Utilize Computational Thinking as a fundamental skill for problem-solving process using a real world problem from the field of agronomy/plant breeding.
Modules:
COinS
Comments
Copyright 2014, Plant and Soil Sciences eLibrary. Used by permission.
This project was supported in part by the National Research Initiative Competitive Grants CAP project 2011-68002-30029 from the USDA National Institute of Food and Agriculture, administered by the University of California-Davis and by the National Science Foundation (NSF), Division of Undergraduate Education, National SMETE Digital Library Program, Award #0938034, administered by the University of Nebraska. Any opinions, findings, conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the USDA or NSF.
This eLesson was supported in part by the National Research Initiative Competitive Grants CAP project 2011-68002-30029 from the USDA National Institute of Food and Agriculture, administered by the University of California-Davis. Any opinions, findings, conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the USDA-NIFA.