book

JMP for Mixed Models

by Ruth Hummel, Elizabeth A. Claassen, Russell D. Wolfinger

June 2021

Intermediate to advanced

262 pages

6h 8m

English

SAS Institute

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1.1 What is a Mixed Model?1.2 Cell Viability Example1.3 Mixed Model Assumptions1.4 Nominal and Continuous Variables1.5 Experimental Units and Blocking, Cell Growth Example1.6 Confounding1.7 JMP and JMP Pro1.8 Exercises
2.1 Motivating Examples2.2 Blocking Designs and Skeleton ANOVA2.3 Metal Bond Breaking Example2.4 Balanced Incomplete Blocks Example2.5 Exercises
3.1 Motivating Examples3.2 Conceptual Background3.3 RCBD with Factorial Treatments, Tensile Strength Example3.4 Split-Plot Design, Greenhouse Example3.5 What About Interactions Between Fixed and Random Effects?3.6 Nested Design, Semiconductor Example3.7 Exercises
4.1 Motivating Examples4.2 Conceptual Background4.3 Latin Square - Blocking in Two Orthogonal Directions4.4 Mouse Condition: Negative Block Variance Example4.5 Exercises
5.1 Motivating Examples5.2 Conceptual Background5.3 Stability Trial5.4 Student Achievement Example5.5 Exercises
6.1 Motivating Example6.2 Conceptual Background6.3 Repeated Measures Skeleton ANOVA and Statistical Model6.4 Respiratory Ability6.5 When Might We Choose Other Models?6.6 Exercises
7.1 Motivating Examples7.2 Conceptual Background7.3 Hazardous Waste Example7.4 Alliance Wheat Trial7.5 Further Statistical Details7.6 Exercises

8.1 Motivating Examples8.2 Simulation for Precision and Power8.3 Type I Error Control Using the Semiconductor Design8.4 Power Using the Semiconductor Design8.5 Confidence Interval Coverage Using the Winter Wheat Example8.6 Simulating Mixed Model Data Directly with JSL8.7 Exercises
9.1 Motivating Examples9.2 Conceptual Background9.3 Binomial Response: Shrub Coverage9.4 Binary Response: Salamander Mating9.5 Count Response: Manufacturing Imperfections9.6 Exercises
10.1 Statistical Pragmatism10.2 Fixed versus Random Effects10.3 Frequentist versus Bayesian, p-values10.4 Causality versus Association10.5 Explanation versus Prediction10.6 Randomized Experiments versus Observational Studies10.7 Exercises

Content preview from JMP for Mixed Models

Chapter 9Generalized Linear Mixed Models

Throughout this book we have thus far considered examples for which the response is continuous, assuming that the random effects and residual errors follow a normal (Gaussian) distribution. While this is arguably the most common type of mixed model, in many cases the response is discrete. In this chapter, we address mixed models for non-normally distributed data.

9.1 Motivating Examples

The three most common examples of non-normal data are:

Binomial — the number of successes, y, out of n trials. The Shrub Coverage example from Parks Canada measures the number of sampling points out of 100 that contain species from a plant functional group during a given year. A "success" in this case is the presence of ...