linear mixed effects models using r a step by step approach springer texts in statistics

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Linear Mixed Effects Models Using R

Author : Andrzej Gałecki
ISBN : 9781461439004
Genre : Mathematics
File Size : 45. 94 MB
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Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data are encountered in a variety of fields including biostatistics, public health, psychometrics, educational measurement, and sociology. This book aims to support a wide range of uses for the models by applied researchers in those and other fields by providing state-of-the-art descriptions of the implementation of LMMs in R. To help readers to get familiar with the features of the models and the details of carrying them out in R, the book includes a review of the most important theoretical concepts of the models. The presentation connects theory, software and applications. It is built up incrementally, starting with a summary of the concepts underlying simpler classes of linear models like the classical regression model, and carrying them forward to LMMs. A similar step-by-step approach is used to describe the R tools for LMMs. All the classes of linear models presented in the book are illustrated using real-life data. The book also introduces several novel R tools for LMMs, including new class of variance-covariance structure for random-effects, methods for influence diagnostics and for power calculations. They are included into an R package that should assist the readers in applying these and other methods presented in this text.

Negation In Early English

Author : Phillip W. Wallage
ISBN : 9781107114296
Genre : Language Arts & Disciplines
File Size : 23. 72 MB
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Informed by detailed analysis of data from large-scale diachronic corpora, this book is a comprehensive account of changes to the expression of negation in English. Its methodological approach brings together up-to-date techniques from corpus linguistics and minimalist syntactic analysis to identify and characterise a series of interrelated changes affecting negation during the period 800-1700. Phillip Wallage uses cutting-edge statistical techniques and large-scale corpora to model changes in English negation over a period of nine hundred years. These models provide crucial empirical evidence which reveals the specific processes of syntactic and functional change affecting early English negation, and identifies diachronic relationships between these processes.

Applied Mixed Model Analysis

Author : Jos W. R. Twisk
ISBN : 9781108480574
Genre : Mathematics
File Size : 48. 4 MB
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Emphasizing interpretation of results, this hands-on guide explains why, when, and how to use mixed models with your data.

Mixed Effects Models For The Population Approach

Author : Marc Lavielle
ISBN : 9781482226508
Genre : Mathematics
File Size : 30. 29 MB
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Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects Models Mixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools presents a rigorous framework for describing, implementing, and using mixed effects models. With these models, readers can perform parameter estimation and modeling across a whole population of individuals at the same time. Easy-to-Use Techniques and Tools for Real-World Data Modeling The book first shows how the framework allows model representation for different data types, including continuous, categorical, count, and time-to-event data. This leads to the use of generic methods, such as the stochastic approximation of the EM algorithm (SAEM), for modeling these diverse data types. The book also covers other essential methods, including Markov chain Monte Carlo (MCMC) and importance sampling techniques. The author uses publicly available software tools to illustrate modeling tasks. Methods are implemented in Monolix, and models are visually explored using Mlxplore and simulated using Simulx. Careful Balance of Mathematical Representation and Practical Implementation This book takes readers through the whole modeling process, from defining/creating a parametric model to performing tasks on the model using various mathematical methods. Statisticians and mathematicians will appreciate the rigorous representation of the models and theoretical properties of the methods while modelers will welcome the practical capabilities of the tools. The book is also useful for training and teaching in any field where population modeling occurs.

Mixed Effects Models And Extensions In Ecology With R

Author : Alain Zuur
ISBN : 0387874585
Genre : Science
File Size : 90. 45 MB
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This book discusses advanced statistical methods that can be used to analyse ecological data. Most environmental collected data are measured repeatedly over time, or space and this requires the use of GLMM or GAMM methods. The book starts by revising regression, additive modelling, GAM and GLM, and then discusses dealing with spatial or temporal dependencies and nested data.

Amstat News

Author :
ISBN : UOM:39015085193624
Genre : Statistics
File Size : 41. 41 MB
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A Modern Approach To Regression With R

Author : Simon Sheather
ISBN : 9780387096070
Genre : Mathematics
File Size : 54. 87 MB
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This book focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models. Plots are shown to be an important tool for both building regression models and assessing their validity. We shall see that deciding what to plot and how each plot should be interpreted will be a major challenge. In order to overcome this challenge we shall need to understand the mathematical properties of the fitted regression models and associated diagnostic procedures. As such this will be an area of focus throughout the book. In particular, we shall carefully study the properties of resi- als in order to understand when patterns in residual plots provide direct information about model misspecification and when they do not. The regression output and plots that appear throughout the book have been gen- ated using R. The output from R that appears in this book has been edited in minor ways. On the book web site you will find the R code used in each example in the text.

Mixed Effects Models In S And S Plus

Author : José C. Pinheiro
ISBN : 9781441903174
Genre : Computers
File Size : 82. 3 MB
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R, linear models, random, fixed, data, analysis, fit.

Probabilistic Graphical Models For Genetics Genomics And Postgenomics

Author : Christine Sinoquet
ISBN : 9780191019203
Genre : Science
File Size : 30. 99 MB
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Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity. These data will only allow insights into this wealth of so-called 'omics' data if represented by flexible and scalable models, prior to any further analysis. At the interface between statistics and machine learning, probabilistic graphical models (PGMs) represent a powerful formalism to discover complex networks of relations. These models are also amenable to incorporating a priori biological information. Network reconstruction from gene expression data represents perhaps the most emblematic area of research where PGMs have been successfully applied. However these models have also created renewed interest in genetics in the broad sense, in particular regarding association genetics, causality discovery, prediction of outcomes, detection of copy number variations, and epigenetics. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest. A salient feature of bioinformatics, interdisciplinarity, reaches its limit when an intricate cooperation between domain specialists is requested. Currently, few people are specialists in the design of advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers such models so that their perceived difficulty no longer hinders their use and focuses on fifteen illustrations showing the mechanisms behind the models. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics covers six main themes: (1) Gene network inference (2) Causality discovery (3) Association genetics (4) Epigenetics (5) Detection of copy number variations (6) Prediction of outcomes from high-dimensional genomic data. Written by leading international experts, this is a collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. The self-contained chapters provide an enlightened account of the pros and cons of applying these powerful techniques.

Canadian Journal Of Forest Research

Author :
ISBN : UCR:31210018598449
Genre : Forests and forestry
File Size : 82. 23 MB
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