BookDOI
Design and Analysis of Experiments with R
Reads0
Chats0
TLDR
In this paper, the authors present an example of a two-factor Factorial Plan in R with fixed and random factors for estimating variance components of two-Factor Factorial Plans.Abstract:
Introduction Statistics and Data Collection Beginnings of Statistically Planned Experiments Definitions and Preliminaries Purposes of Experimental Design Types of Experimental Designs Planning Experiments Performing the Experiments Use of R Software Completely Randomized Designs with One Factor Introduction Replication and Randomization A Historical Example Linear Model for Completely Randomized Design (CRD) Verifying Assumptions of the Linear Model Analysis Strategies When Assumptions Are Violated Determining the Number of Replicates Comparison of Treatments after the F-Test Factorial Designs Introduction Classical One at a Time versus Factorial Plans Interpreting Interactions Creating a Two-Factor Factorial Plan in R Analysis of a Two-Factor Factorial in R Factorial Designs with Multiple Factors-Completely Randomized Factorial Design (CRFD) Two-Level Factorials Verifying Assumptions of the Model Randomized Block Designs Introduction Creating a Randomized Complete Block (RCB) Design in R Model for RCB An Example of a RCB Determining the Number of Blocks Factorial Designs in Blocks Generalized Complete Block Design Two Block Factors Latin Square Design (LSD) Designs to Study Variances Introduction Random Sampling Experiments (RSE) One-Factor Sampling Designs Estimating Variance Components Two-Factor Sampling Designs-Factorial RSE Nested SE Staggered Nested SE Designs with Fixed and Random Factors Graphical Methods to Check Model Assumptions Fractional Factorial Designs Introduction to Completely Randomized Fractional Factorial (CRFF) Half Fractions of 2k Designs Quarter and Higher Fractions of 2k Designs Criteria for Choosing Generators for 2k-p Designs Augmenting Fractional Factorials Plackett-Burman (PB) Screening Designs Mixed-Level Fractional Factorials Orthogonal Array (OA) Definitive Screening Designs Incomplete and Confounded Block Designs Introduction Balanced Incomplete Block (BIB) Designs Analysis of Incomplete Block Designs Partially Balanced Incomplete Block (PBIB) Designs-Balanced Treatment Incomplete Block (BTIB) Row Column Designs Confounded 2k and 2k-p Designs Confounding 3 Level and p Level Factorial Designs Blocking Mixed-Level Factorials and OAs Partially CBF Split-Plot Designs Introduction Split-Plot Experiments with CRD in Whole Plots (CRSP) RCB in Whole Plots (RBSP) Analysis Unreplicated 2k Split-Plot Designs 2k-p Fractional Factorials in Split Plots (FFSP) Sample Size and Power Issues for Split-Plot Designs Crossover and Repeated Measures Designs Introduction Crossover Designs (COD) Simple AB, BA Crossover Designs for Two Treatments Crossover Designs for Multiple Treatments Repeated Measures Designs Univariate Analysis of Repeated Measures Design Response Surface Designs Introduction Fundamentals of Response Surface Methodology Standard Designs for Second-Order Models Creating Standard Response Surface Designs in R Non-Standard Response Surface Designs Fitting the Response Surface Model with R Determining Optimum Operating Conditions Blocked Response Surface (BRS) Designs Response Surface Split-Plot (RSSP) Designs Mixture Experiments Introduction Models and Designs for Mixture Experiments Creating Mixture Designs in R Analysis of Mixture Experiment Constrained Mixture Experiments Blocking Mixture Experiments Mixture Experiments with Process Variables Mixture Experiments in Split-Plot Arrangements Robust Parameter Design Experiments Introduction Noise Sources of Functional Variation Product Array Parameter Design Experiments Analysis of Product Array Experiments Single Array Parameter Design Experiments Joint Modeling of Mean and Dispersion Effects Experimental Strategies for Increasing Knowledge Introduction Sequential Experimentation One-Step Screening and Optimization An Example of Sequential Experimentation Evolutionary Operation Concluding Remarks Appendix: Brief Introduction to R Answers to Selected Exercises Bibliography Index A Review and Exercises appear at the end of each chapter.read more
Citations
More filters
Journal ArticleDOI
A review of experiments in tourism and hospitality.
Giampaolo Viglia,Sara Dolnicar +1 more
TL;DR: The benefits of experimental designs over alternative research approaches for the social sciences, advantages and disadvantages of different types of experiments, review existing experimental studies specific to tourism and hospitality, and offer guidance to researchers who wish to conduct such studies are discussed.
Journal ArticleDOI
Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies
Marynka Ulaszewska,Christoph H. Weinert,Alessia Trimigno,Reto Portmann,Cristina Andres Lacueva,René Badertscher,Lorraine Brennan,Carl Brunius,Achim Bub,Francesco Capozzi,Marta Cialiè Rosso,Chiara Cordero,Hannelore Daniel,Stéphanie Durand,Bjoern Egert,Paola G. Ferrario,Edith J. M. Feskens,Pietro Franceschi,Mar Garcia-Aloy,Franck Giacomoni,Pieter Giesbertz,Raúl González-Domínguez,Kati Hanhineva,Lieselot Hemeryck,Joachim Kopka,Sabine E. Kulling,Rafael Llorach,Claudine Manach,Fulvio Mattivi,Carole Migné,Linda H. Münger,Beate Ott,Gianfranco Picone,Grégory Pimentel,Estelle Pujos-Guillot,Samantha Riccadonna,Manuela J. Rist,Caroline Rombouts,Josep Rubert,Thomas Skurk,Pedapati S. C. Sri Harsha,Lieven Van Meulebroek,Lynn Vanhaecke,Rosa Vázquez-Fresno,David S. Wishart,Guy Vergères +45 more
TL;DR: A methodological description of nutritional metabolomics is provided that reflects on the state-of-the-art techniques used in the laboratories of the Food Biomarker Alliance as well as points of reflections to harmonize this field.
Book
Online Evaluation for Information Retrieval
TL;DR: This survey provides an overview of online evaluation techniques for information retrieval, and shows how online evaluation is used for controlled experiments, segmenting them into experiment designs that allow absolute or relative quality assessments.
Journal ArticleDOI
Two apples a day lower serum cholesterol and improve cardiometabolic biomarkers in mildly hypercholesterolemic adults: a randomized, controlled, crossover trial.
A. Koutsos,Samantha Riccadonna,Maria M. Ulaszewska,Pietro Franceschi,Kajetan Trošt,Amanda Galvin,Tanya Braune,Francesca Fava,Daniele Perenzoni,Fulvio Mattivi,Kieran Tuohy,Julie A. Lovegrove +11 more
TL;DR: These data support beneficial hypocholesterolesmic and vascular effects of the daily consumption of PA-rich apples by mildly hypercholesterolemic individuals.
Journal ArticleDOI
Mixture Experiments in R Using mixexp
John Lawson,Cameron Willden +1 more
TL;DR: Mixexp as discussed by the authors provides functions for creating mixture designs composed of extreme vertices and edge and face centroids in constrained mixture regions where components are subject to upper, lower and linear constraints.