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JMP for Mixed Models - Book Chapter 1

This summary is excerpted from JMP for Mixed Models by Ruth Hummel, Elizabeth Claassen, and Russell Wolfinger. Copyright 2021, SAS Institute Inc., Cary, NC, USA. All Rights Reserved. Reproduced with permission of SAS Institute Inc., Cary, NC.

 

Download Chapter 1 to learn more. 

 

Mixed models are now the mainstream method of choice for analyzing experimental data. Why? They are arguably the most straightforward and powerful way to handle correlated observations in designed experiments. Reaching well beyond standard linear models, mixed models enable you to make accurate and precise inferences about your experiments and to gain deeper understanding of sources of signal and noise in the system under study. Well-formed fixed and random effects generalize well and help you make the best data-driven decisions.

 

JMP for Mixed Models  brings together two of the strongest traditions in SAS software: mixed models and JMP. JMP's groundbreaking philosophy of tight integration of statistics with dynamic graphics is an ideal milieu within which to learn and apply mixed models, also known as hierarchical linear or multilevel models. If you are a scientist or engineer, the methods described herein can revolutionize how you analyze experimental data without the need to write code.

 

Inside you'll find a rich collection of examples and a step-by-step approach to mixed model mastery. Topics include:

• Learning how to appropriately recognize, set up, and interpret fixed and random effects
• Extending analysis of variance (ANOVA) and linear regression to numerous mixed model designs
• Understanding how degrees of freedom work using Skeleton ANOVA
• Analyzing randomized block, split-plot, longitudinal, and repeated measures designs
• Introducing more advanced methods such as spatial covariance and generalized linear mixed models
• Simulating mixed models to assess power and other important sampling characteristics
• Providing a solid framework for understanding statistical modeling in general
• Improving perspective on modern dilemmas around Bayesian methods, p-values, and causal inference.

 

 

 

 

Basic Data Analysis and ModelingDesign of ExperimentsRe: JMP for Mixed Models - Book Chapter 1

So, I have a controversial question for you guys. 

I am running mixed models (repeated measurements in animal field to be more specific - @russ_wolfinger know the subject a bit). 

I have normal data and non-parametric data for this trial. So, mixed model and GLMM with an interaction and random effects. 

After running the models I get the means and the LSMeans. When doing Tukey's test, it is based on the LSMeans; however, when I do a graph builder it is the means.  

Then the huuuge issue it comes when we run a poisson model - cels counts where my LSmeans are 10 time less than my means and the estimates are closer to the means but not the same. 

Could you please help my ignorance on the models or in the stat... Which is the correct? and how to explain with a logical way?