1# Library to Find Best eBook for Read
Author : Alain F. Zuur
Release : 2013
Genre : Ecology
Kind : eBook
Book Rating : 139/5 ( reviews)
Book Synopsis A Beginner's Guide to GLM and GLMM with R by : Alain F. Zuur
Download or read book A Beginner's Guide to GLM and GLMM with R written by Alain F. Zuur. This book was released on 2013. Available in PDF, EPUB and Kindle. Book excerpt: This book presents Generalized Linear Models (GLM) and Generalized Linear Mixed Models (GLMM) based on both frequency-based and Bayesian concepts.
Author : Alain F. Zuur
Release : 2017
Genre : Ecology
Kind : eBook
Book Rating : 191/5 ( reviews)
Book Synopsis Beginner's guide to spatial, temporal,and spatial-temporal ecological data analysis with R-INLA by : Alain F. Zuur
Download or read book Beginner's guide to spatial, temporal,and spatial-temporal ecological data analysis with R-INLA written by Alain F. Zuur. This book was released on 2017. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Simon Wood
Release : 2006-02-27
Genre : Mathematics
Kind : eBook
Book Rating : 746/5 ( reviews)
Book Synopsis Generalized Additive Models by : Simon Wood
Download or read book Generalized Additive Models written by Simon Wood. This book was released on 2006-02-27. Available in PDF, EPUB and Kindle. Book excerpt: Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research monograph on GAMs is largely responsible for this, there has been a long-standing need for an accessible introductory treatment of the subject that also emphasizes recent penalized regression spline approaches to GAMs and the mixed model extensions of these models. Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these very flexible tools. The author bases his approach on a framework of penalized regression splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of the freely available R software helps explain the theory and illustrates the practicalities of linear, generalized linear, and generalized additive models, as well as their mixed effect extensions. The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's add-on package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix. Concise, comprehensive, and essentially self-contained, Generalized Additive Models: An Introduction with R prepares readers with the practical skills and the theoretical background needed to use and understand GAMs and to move on to other GAM-related methods and models, such as SS-ANOVA, P-splines, backfitting and Bayesian approaches to smoothing and additive modelling.
Author : Alain F. Zuur
Release : 2014
Genre : Multilevel models (Statistics)
Kind : eBook
Book Rating : 153/5 ( reviews)
Book Synopsis A Beginner's Guide to Generalised Additive Mixed Models with R by : Alain F. Zuur
Download or read book A Beginner's Guide to Generalised Additive Mixed Models with R written by Alain F. Zuur. This book was released on 2014. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Lang Wu
Release : 2009-11-11
Genre : Mathematics
Kind : eBook
Book Rating : 086/5 ( reviews)
Book Synopsis Mixed Effects Models for Complex Data by : Lang Wu
Download or read book Mixed Effects Models for Complex Data written by Lang Wu. This book was released on 2009-11-11. Available in PDF, EPUB and Kindle. Book excerpt: Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, 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 values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.
