Suchergebnisse

1. Introduction. 2. Theoretical underpinnings of regularization methods. 3. Regularization methods for linear models. 4. Regularization methods for generalized linear models. 5. Regularization methods for multivariate linear models. 6. Regularization methods for cluster analysis and principal components analysis. 7. Regularization methods for latent variable models. 8. Regularization methods for multilevel models. 9. Advanced topics in feature selection.

"This text demonstrates how to conduct latent variable modeling in R. Techniques that can be analyzed using the free program R are showcased including exploratory and confirmatory factor analysis, structural equation modeling (SEM), latent growth curve modeling, item response theory (IRT), and latent class analysis. Easy to follow demonstrations of how to conduct latent variable modeling in R are provided along with descriptions of the major features of the models,their specialized uses, and a full interpretation of the results. Every R command necessary for conducting the analyses is described so readers can directly apply the R functions to their own data. Each chapter features a complete analysis of one or more example datasets including a demonstration of the analysis of the data using R, along with a discussion of relevant theory that includes a full description of the models, the assumptions underlying each model, and statistical details of estimation, hypothesis testing, and more to help readers better understand the models and interpret the results. Some of the examples represent data that is not perfectly "behaved" so as to provide a more realistic view of situations readers will likely encounter with their own data. Detailed explanations of input statements help readers generalize what they learn to their own analyses. Each chapter features an introduction, summary, and exercises involving the application of the model(s), and a list of further readings with an emphasis on related texts that provide more detailed theoretical coverage. A full glossary of the key terms, a cheat sheet that reviews the key R commands, and answers to half of the exercises are provided at the end of the book"--

Like its bestselling predecessor, Multilevel Modeling Using R, Second Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research.