This practical book is designed for applied researchers who want to use mixed models with their data. It discusses the basic principles of mixed model analysis, including two-level and three-level structures, and covers continuous outcome variables, dichotomous outcome variables…
This practical book is designed for applied researchers who want to use mixed models with their data. It discusses the basic principles of mixed model analysis, including two-level and three-level structures, and covers continuous outcome variables, dichotomous outcome variables…
Provides an introduction to the important subject of nonlinear mixture models from a Bayesian perspective. This title contains background material, a brief description of Markov chain theory, as well as novel algorithms and their applications.
"Highly recommended by JASA, Technometrics, and other journals, the first edition of this bestseller showed how to easily perform complex linear mixed model (LMM) analyses via a variety of software programs. Linear Mixed Models: A Practical Guide Using Statistical Software, Seco…
1.7 Comparison of the tests in a mixed model with a test of lack of fit; 1.8 The use of REsidual Maximum Likelihood (REML) to fit the mixed model; 1.9 Equivalence of the different analyses when the number of observations per group is constant; 1.10 Testing the assumptions of the…
This book first introduces the criterion of REstricted Maximum Likelihood (REML) for the fitting of a mixed model to data before illustrating how to apply mixed model analysis to a wide range of situations, how to estimate the variance due to each random-effect term in the model…
"The book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing va…