Linear Mixed-Effects Models Using R

Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), the book is ideal for research scientists, final-year undergraduate or graduate-level students of applied statistics, and practising statisticians. It is both for learning and for reference. This third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests.

This textbook for a second course in basic statistics for undergraduates or first-year graduate students introduces linear regression models and describes other linear models including Poisson regression, logistic regression, proportional hazards regression, and nonparametric regression. Numerous examples drawn from the news and current events with an emphasis on health issues illustrate these concepts. Assuming only a pre-calculus background, the author keeps equations to a minimum and demonstrates all computations using SAS. Most of the programs and output are displayed in a self-contained way, with an emphasis on the interpretation of the output in terms of how it relates to the motivating example. Plenty of exercises conclude every chapter. All of the datasets and SAS programs are available from the book's website, along with other ancillary material.

Presenting a rigorous treatment of the physical and mechanical basis for the modelling of sedimentary basins, this book supplies geoscientists with practical tools for creating their own models. It begins with a thorough grounding in properties of porous media, linear elasticity, continuum mechanics and rock compressibility. Chapters on heat flow, subsidence, rheology, flexure and gravity consider sedimentary basins in the context of the Earth's lithosphere, and the book concludes with coverage of pore space cementation, compaction and fluid flow. The volume introduces basic, state-of-the-art models and demonstrates how to reproduce results using tools like MATLAB® and Octave. Main equations are derived from first principles, and their basic solutions are obtained and then applied. Separate notes sections supply more technical details, and the text is illustrated throughout with real-world examples, applications and test exercises. This is an accessible introduction to quantitative mod

Linear models courses are often presented as either theoretical or applied. Consequently, students may find themselves either proving theorems or using high-level procedures like PROC GLM to analyze d

The theory of empirical processes provides valuable tools for the development of asymptotic theory in (nonparametric) statistical models, and makes possible the unified treatment of a number of them. This book reveals the relation between the asymptotic behaviour of M-estimators and the complexity of parameter space. Virtually all results are proved using only elementary ideas developed within the book; there is minimal recourse to abstract theoretical results. To make the results concrete, a detailed treatment is presented for two important examples of M-estimation, namely maximum likelihood and least squares. The theory also covers estimation methods using penalties and sieves. Many illustrative examples are given, including the Grenander estimator, estimation of functions of bounded variation, smoothing splines, partially linear models, mixture models and image analysis. Graduate students and professionals in statistics as well as those with an interest in applications, to such area

This book demonstrates how to estimate and interpret fixed-effects models in a variety of different modeling contexts: linear models, logistic models, Poisson models, Cox regression models, and struc

Weeds are a major constraint to agricultural production, particularly in the developing world. Cost-efficient biological control is a self-sustaining way to reduce this problem, and produces fewer non-target effects than chemical methods, which can cause serious damage to the environment. This book covers the origin, distribution, and ecology of twenty model invasive weed species, which occur in habitats from tropical to temperate to aquatic. Sustainable biological control of each weed using one or more arthropods is discussed. The aim is to provide ecological management models for use across the tropical world, and to assist in the assessment of potential risks to native and economic plants. This is a valuable resource for scientists and policy makers concerned with the biological control of invasive tropical plants.

This is the first book on applied econometrics using the R system for statistical computing and graphics. It presents hands-on examples for a wide range of econometric models, from classical linear re

The chemostat is a basic piece of laboratory apparatus, yet it has occupied an increasingly central role in ecological studies. The ecological environment created by a chemostat is one of the few completely controlled experimental systems for testing microbial growth and competition. As a tool in biotechnology, the chemostat plays an important role in bioprocessing. This book presents the theory of the chemostat as a model for larger ecological problems such as food chains, competition along a gradient, competition in the presence of an inhibitor, and the effects of time varying inputs. Models which take account of size structure, variable yields, and diffusion are also considered. The basic phenomena are modelled and analysed using the dynamical systems approach. Directions for research and open problems are discussed. Six appendices provide an elementary description of the necessary mathematical tools. Teachers, researchers, and students in applied mathematics, chemical engineering a

This is the only book actuaries need to understand generalized linear models (GLMs) for insurance applications. GLMs are used in the insurance industry to support critical decisions. Until now, no text has introduced GLMs in this context or addressed the problems specific to insurance data. Using insurance data sets, this practical, rigorous book treats GLMs, covers all standard exponential family distributions, extends the methodology to correlated data structures, and discusses recent developments which go beyond the GLM. The issues in the book are specific to insurance data, such as model selection in the presence of large data sets and the handling of varying exposure times. Exercises and data-based practicals help readers to consolidate their skills, with solutions and data sets given on the companion website. Although the book is package-independent, SAS code and output examples feature in an appendix and on the website. In addition, R code and output for all the examples are pro

With a focus on analyzing and modeling linear dynamic systems using statistical methods, Time Series Analysis formulates various linear models, discusses their theoretical characteristics, and explore

Filtering and system identification are powerful techniques for building models of complex systems. This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models derived using these techniques. Emphasis is on the least squares approach as applied to the linear state-space model, and problems of increasing complexity are analyzed and solved within this framework, starting with the Kalman filter and concluding with the estimation of a full model, noise statistics and state estimator directly from the data. Key background topics, including linear matrix algebra and linear system theory, are covered, followed by different estimation and identification methods in the state-space model. With end-of-chapter exercises, MATLAB simulations and numerous illustrations, this book will appeal to graduate students and researchers in electrical, mechanical and aerospace engineering. It is also useful for practitioners. Additional resources for this title,

This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models, and it presents an up-to-date account of theory and methods in analysis of these mo

Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition presents analyses

Statistical analysis is ubiquitous in modern medical research. Logistic regression, generalized linear models, random effects models, and Cox's regression all have become commonplace in the medical li

Environmental external effects are evidence of the inability of market prices to reflect the interdependence of economic activities undertaken within a common environment. They are an essential - not a peripheral - feature of all market economics. This essay shows how external effects are produced by the interaction of the economy with its environment, using a classical mass-balance model. No matter how efficient the market may seem to be, the use of market prices to determine depletion and pollution decisions creates more problems than it solves. Far from sharing the stable or relatively stable equilibrium properties of most economic models, the market economy is shown to be forced by its environment through a seemingly chaotic sequence of states. Reliance on the market to accommodate each change of direction merely exaggerates the general instability of the system. The 'market solution' to environmental problems is shown to generate only increasing uncertainty, a progressive myopia,

Do you have data that is not normally distributed and don't know how to analyze it using generalized linear models (GLM)? Beginning with a discussion of fundamental statistical modeling concepts in a

This book provides an introduction to nonequilibrium statistical physics via lattice models. Beginning with an introduction to the basic driven lattice gas, the early chapters discuss the relevance of this lattice model to certain natural phenomena and examine simulation results in detail. Several possible theoretical approaches to the driven lattice gas are presented. In the next two chapters, absorbing-state transitions are discussed in detail. The later chapters examine a variety of systems subject to dynamic disorder before returning to look at the more surprising effects of multiparticle rules, nonunique absorbing-states and conservation laws. Examples are given throughout the book, the emphasis being on using simple representations of nature to describe ordering in real systems. The use of methods such as mean-field theory, Monte Carlo simulation, and the concept of universality to study and interpret these models is described. Detailed references are included.

This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mix

Proper conduct of monetary policy requires understanding the monetary transmission mechanism, to monitor the economy, make decisions on the stance of policy, and explain the policy actions to the public. Hence, gathering evidence on the monetary transmission mechanism in the euro area has been a priority for the Eurosystem. This 2003 book presents the results of a multi-year collaborative project conducted by the European Central Bank and the other Eurosystem central banks. First, macro data are consistently investigated with both VARs and structural models for the area as a whole and for individual countries. Second, the book contains an unprecedented set of studies on the effects of monetary policy using bank and firm panel data. The results described in country case studies and overview essays by central bank economists, along with a discussion chapter by eminent academics, provide an essential contribution to research on the subject.

Quantum mechanics is one of the most fundamental yet difficult subjects in physics. Nonrelativistic quantum theory is presented here in a clear and systematic fashion, integrating Born's probabilistic interpretation with Schrödinger dynamics. Basic quantum principles are illustrated with simple examples requiring no mathematics beyond linear algebra and elementary probability theory. The quantum measurement process is consistently analyzed using fundamental quantum principles without referring to measurement. These same principles are used to resolve several of the paradoxes that have long perplexed physicists, including the double slit and Schrödinger's cat. The consistent histories formalism used here was first introduced by the author, and extended by M. Gell-Mann, J. Hartle and R. Omnès. Essential for researchers yet accessible to advanced undergraduate students in physics, chemistry, mathematics, and computer science, this book is supplementary to standard textbooks. It will also

This book explains the financial appraisal of capital budgeting projects. The coverage extends from the development of basic concepts, principles and techniques to the application of them in increasingly complex and real-world situations. Identification and estimation (including forecasting) of cash flows, project appraisal formulae, and the application of net present value (NPV), internal rate of return (IRR) and other project evaluation criteria are illustrated with a variety of calculation examples. Risk analysis is extensively covered by the use of risk adjusted discount rate, certainty equivalent, sensitivity, simulation and Monte Carlo analysis. The NPV and IRR models are further applied to forestry, property and international investments. Resource constraints are introduced to the capital budgeting decisions with a variety of worked examples using linear programming technique. All calculations are extensively supported by Excel workbooks on the Web, and each chapter is well revi

This book explains the financial appraisal of capital budgeting projects. The coverage extends from the development of basic concepts, principles and techniques to the application of them in increasingly complex and real-world situations. Identification and estimation (including forecasting) of cash flows, project appraisal formulae, and the application of net present value (NPV), internal rate of return (IRR) and other project evaluation criteria are illustrated with a variety of calculation examples. Risk analysis is extensively covered by the use of risk adjusted discount rate, certainty equivalent, sensitivity, simulation and Monte Carlo analysis. The NPV and IRR models are further applied to forestry, property and international investments. Resource constraints are introduced to the capital budgeting decisions with a variety of worked examples using linear programming technique. All calculations are extensively supported by Excel workbooks on the Web, and each chapter is well revi

This volume reports the results of a rigorous and quantitative assessment of the impacts of climate change on US agriculture. It advances work on climate impacts by using scenarios from state-of-the-art climate models. It describes the forces likely to shape US agriculture over the next century, including policy and economic issues, and their interaction with climate change. It offers directions for future research and implications of climate change for agricultural policy. The basic findings are that climate change is positive for agriculture production although southern parts of the US could be harmed. Negative consequences of climate change include effects on pesticide use, run-off of nutrients into coastal areas, and potential regional conflicts for some groundwater sources. This authoritative assessment will be accessible to a broad readership of students, researchers, and policymakers.

This book describes the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students. It is unusual in presenting Bayesian statistics with a practical flavor and an emphasis on mainstream statistics, showing how to infer scientific, medical, and social conclusions from numerical data. The authors draw on many years of experience with practical and research programs and describe many statistical methods, not readily available elsewhere. A first chapter on Fisherian methods, together with a strong overall emphasis on likelihood, makes the text suitable for mainstream statistics courses whose instructors wish to follow mixed or comparative philosophies. The other chapters contain important sections relating to many areas of statistics such as the linear model, categorical data analysis, time series and forecasting, mixture models, survival analysis, Bayesian smoothing, and non-linear random effects models. The text includes a large number of practi

In this book, first published in 2001, Kenneth Schultz explores the effects of democratic politics on the use and success of coercive diplomacy. He argues that open political competition between the government and opposition parties influences the decision to use threats in international crises, how rival states interpret those threats, and whether or not crises can be settled short of war. The relative transparency of their political processes means that, while democratic governments cannot easily conceal domestic constraints against using force, they can also credibly demonstrate resolve when their threats enjoy strong domestic support. As a result, compared to their non-democratic counterparts, democracies are more selective about making threats, but those they do make are more likely to be successful - that is, to gain a favorable outcome without resort to war. Schultz develops his argument through a series of game-theoretic models and tests the resulting hypothesis using both stat

Eric Ghysels and Denise R. Osborn provide a thorough and timely review of the recent developments in the econometric analysis of seasonal economic time series, summarizing a decade of theoretical advances in the area. The authors discuss the asymptotic distribution theory for linear nonstationary seasonal stochastic processes. They also cover the latest contributions to the theory and practice of seasonal adjustment, together with its implications for estimation and hypothesis testing. Moreover, a comprehensive analysis of periodic models is provided, including stationary and nonstationary cases. The book concludes with a discussion of some nonlinear seasonal and periodic models. The treatment is designed for an audience of researchers and advanced graduate students.

Eric Ghysels and Denise R. Osborn provide a thorough and timely review of the recent developments in the econometric analysis of seasonal economic time series, summarizing a decade of theoretical advances in the area. The authors discuss the asymptotic distribution theory for linear nonstationary seasonal stochastic processes. They also cover the latest contributions to the theory and practice of seasonal adjustment, together with its implications for estimation and hypothesis testing. Moreover, a comprehensive analysis of periodic models is provided, including stationary and nonstationary cases. The book concludes with a discussion of some nonlinear seasonal and periodic models. The treatment is designed for an audience of researchers and advanced graduate students.

Independent Component Analysis (ICA) has recently become an important tool for modelling and understanding empirical datasets. It is a method of separating out independent sources from linearly mixed data, and belongs to the class of general linear models. ICA provides a better decomposition than other well-known models such as principal component analysis. This self-contained book contains a structured series of edited papers by leading researchers in the field, including an extensive introduction to ICA. The major theoretical bases are reviewed from a modern perspective, current developments are surveyed and many case studies of applications are described in detail. The latter include biomedical examples, signal and image denoising and mobile communications. ICA is discussed in the framework of general linear models, but also in comparison with other paradigms such as neural network and graphical modelling methods. The book is ideal for researchers and graduate students in the field.

Although many of the models commonly used in empirical finance are linear, the nature of financial data suggests that non-linear models are more appropriate for forecasting and accurately describing returns and volatility. The enormous number of non-linear time series models appropriate for modeling and forecasting economic time series models makes choosing the best model for a particular application daunting. This classroom-tested advanced undergraduate and graduate textbook, first published in 2000, provides a rigorous treatment of recently developed non-linear models, including regime-switching and artificial neural networks. The focus is on the potential applicability for describing and forecasting financial asset returns and their associated volatility. The models are analysed in detail and are not treated as 'black boxes'. Illustrated using a wide range of financial data, drawn from sources including the financial markets of Tokyo, London and Frankfurt.

Although many of the models commonly used in empirical finance are linear, the nature of financial data suggests that non-linear models are more appropriate for forecasting and accurately describing returns and volatility. The enormous number of non-linear time series models appropriate for modeling and forecasting economic time series models makes choosing the best model for a particular application daunting. This classroom-tested advanced undergraduate and graduate textbook, first published in 2000, provides a rigorous treatment of recently developed non-linear models, including regime-switching and artificial neural networks. The focus is on the potential applicability for describing and forecasting financial asset returns and their associated volatility. The models are analysed in detail and are not treated as 'black boxes'. Illustrated using a wide range of financial data, drawn from sources including the financial markets of Tokyo, London and Frankfurt.

Describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using

This book describes the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students. It is unusual in presenting Bayesian statistics with a practical flavor and an emphasis on mainstream statistics, showing how to infer scientific, medical, and social conclusions from numerical data. The authors draw on many years of experience with practical and research programs and describe many statistical methods, not readily available elsewhere. A first chapter on Fisherian methods, together with a strong overall emphasis on likelihood, makes the text suitable for mainstream statistics courses whose instructors wish to follow mixed or comparative philosophies. The other chapters contain important sections relating to many areas of statistics such as the linear model, categorical data analysis, time series and forecasting, mixture models, survival analysis, Bayesian smoothing, and non-linear random effects models. The text includes a large number of practi

The often emotional debate over the impact of structural adjustment on the poor in Africa has been confused by the complexity of economic reforms and their inconsistent implementation, the diversity of prior conditions, and confounding effects of external shocks. Going beyond simple 'before and after' comparisons, in this 1998 book Professors Sahn, Dorosh, and Younger isolate from other factors the effect of specific policy measures associated with adjustment programs. The analysis draws primarily on the experience of ten African countries: Cameroon, The Gambia, Ghana, Madagascar, Malawi, Mozambique, Niger, Tanzania, and Zaire. It combines description of policy reforms and survey data, and quantitative simulations using multi-market and computable general equilibrium (CGE) models. The authors suggest that contrary to common belief, adjustment policies do not harm the poor in Africa. Reforms in fact usually benefit the poor slightly, but alone are insufficient to reduce poverty signific

This book provides an introduction to nonequilibrium statistical physics via lattice models. Beginning with an introduction to the basic driven lattice gas, the early chapters discuss the relevance of this lattice model to certain natural phenomena and examine simulation results in detail. Several possible theoretical approaches to the driven lattice gas are presented. In the next two chapters, absorbing-state transitions are discussed in detail. The later chapters examine a variety of systems subject to dynamic disorder before returning to look at the more surprising effects of multiparticle rules, nonunique absorbing-states and conservation laws. Examples are given throughout the book, the emphasis being on using simple representations of nature to describe ordering in real systems. The use of methods such as mean-field theory, Monte Carlo simulation, and the concept of universality to study and interpret these models is described. Detailed references are included.

This is a three-part text on the subject of superconductivity, an area of intense research activity worldwide. The first part covers the London, Pippard and Ginzburg-Landau theories, which are used to discuss a wide range of phenomena involving surface energies, vorticity, the intermediate and mixed states, boundaries and boundary conditions, the upper critical field in bulk, thin film and anisotropic superconductors, and surface superconductivity. The second section discusses the microscopic theory of Bardeen, Cooper and Schreiffer. The theory is used to discuss quasi particle tunneling and the Josephson effects from a microscopic point of view. The final part of the book treats non-uniform superconductors using the Bogoliubov-de Gennes approach with which it is possible to extract many important results without invoking Green's function methods. This text will be of great interest to graduate students and research workers in the fields of superconductivity, superfluidity, many body t

This 1998 study introduces the physical principles of how and why crystals grow. The first three chapters recall the fundamental properties of crystal surfaces at equilibrium. The next six chapters describe simple models and basic concepts of crystal growth including diffusion, thermal smoothing of a surface, and applications to semiconductors. Following chapters examine more complex topics such as kinetic roughness, growth instabilities, and elastic effects. A brief closing chapter looks back at the crucial contributions of crystal growth in electronics during the twentieth century. The book focuses on growth using molecular beam epitaxy. Throughout, the emphasis is on the role played by statistical physics. Informative appendices, interesting exercises and an extensive bibliography reinforce the text.

This 1998 study introduces the physical principles of how and why crystals grow. The first three chapters recall the fundamental properties of crystal surfaces at equilibrium. The next six chapters describe simple models and basic concepts of crystal growth including diffusion, thermal smoothing of a surface, and applications to semiconductors. Following chapters examine more complex topics such as kinetic roughness, growth instabilities, and elastic effects. A brief closing chapter looks back at the crucial contributions of crystal growth in electronics during the twentieth century. The book focuses on growth using molecular beam epitaxy. Throughout, the emphasis is on the role played by statistical physics. Informative appendices, interesting exercises and an extensive bibliography reinforce the text.