Mixed-Effects Regression Models in Linguistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Mixed-Effects Regression Models in Linguistics write by Dirk Speelman. This book was released on 2018-02-07. Mixed-Effects Regression Models in Linguistics available in PDF, EPUB and Kindle. When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addresses a number of common complications, misunderstandings, and pitfalls. Topics that are covered include the use of huge datasets, dealing with non-linear relations, issues of cross-validation, and issues of model selection and complex random structures. The volume features examples from various subfields in linguistics. The book also provides R code for a wide range of analyses.
Mixed-effects Regression Models in Linguistics
Mixed-effects Regression Models in Linguistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Mixed-effects Regression Models in Linguistics write by Dirk Speelman. This book was released on 2018. Mixed-effects Regression Models in Linguistics available in PDF, EPUB and Kindle. When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addresses a number of common complications, misunderstandings, and pitfalls. Topics that are covered include the use of huge datasets, dealing with non-linear relations, issues of cross-validation, and issues of model selection and complex random structures. The volume features examples from various subfields in linguistics. The book also provides R code for a wide range of analyses.
Mixed-Effects Models in S and S-PLUS
Mixed-Effects Models in S and S-PLUS - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Mixed-Effects Models in S and S-PLUS write by José C. Pinheiro. This book was released on 2009-04-15. Mixed-Effects Models in S and S-PLUS available in PDF, EPUB and Kindle. R, linear models, random, fixed, data, analysis, fit.
Regression Modeling for Linguistic Data
Regression Modeling for Linguistic Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Regression Modeling for Linguistic Data write by Morgan Sonderegger. This book was released on 2023-06-06. Regression Modeling for Linguistic Data available in PDF, EPUB and Kindle. The first comprehensive textbook on regression modeling for linguistic data offers an incisive conceptual overview along with worked examples that teach practical skills for realistic data analysis. In the first comprehensive textbook on regression modeling for linguistic data in a frequentist framework, Morgan Sonderegger provides graduate students and researchers with an incisive conceptual overview along with worked examples that teach practical skills for realistic data analysis. The book features extensive treatment of mixed-effects regression models, the most widely used statistical method for analyzing linguistic data. Sonderegger begins with preliminaries to regression modeling: assumptions, inferential statistics, hypothesis testing, power, and other errors. He then covers regression models for non-clustered data: linear regression, model selection and validation, logistic regression, and applied topics such as contrast coding and nonlinear effects. The last three chapters discuss regression models for clustered data: linear and logistic mixed-effects models as well as model predictions, convergence, and model selection. The book’s focused scope and practical emphasis will equip readers to implement these methods and understand how they are used in current work. The only advanced discussion of modeling for linguists Uses R throughout, in practical examples using real datasets Extensive treatment of mixed-effects regression models Contains detailed, clear guidance on reporting models Equal emphasis on observational data and data from controlled experiments Suitable for graduate students and researchers with computational interests across linguistics and cognitive science
Methods and Applications of Longitudinal Data Analysis
Methods and Applications of Longitudinal Data Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Methods and Applications of Longitudinal Data Analysis write by Xian Liu. This book was released on 2015-09-01. Methods and Applications of Longitudinal Data Analysis available in PDF, EPUB and Kindle. Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include: descriptive methods for delineating trends over time linear mixed regression models with both fixed and random effects covariance pattern models on correlated errors generalized estimating equations nonlinear regression models for categorical repeated measurements techniques for analyzing longitudinal data with non-ignorable missing observations Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data. Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists. From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysis Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples.
