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This revised edition maintains the clear, nontechnical format of the first, and covers the infectious diseases of shade trees, the major pathogens that cause them, and noninfectious diseases and their agents. Special topics include nonpathogenic conditions, disease diagnosis, and tree injection and implantation. Comprehensive disease control protocols, a detailed discussion of disease diagnosis, and tree diseases of the western and southern United States are among the useful additions to this widely used text and reference. This is an essential book for arboriculturists and students of aboriculture.- Only text available that deals exclusively with the diseases of shade trees- Emphasizes abiotic problems of shade trees, which account for most of the losses in the urban environment

Survival analysis generally deals with analysis of data arising from clinical trials. Censoring, truncation, and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties, essentially asymptotic ones, of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades, survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way.Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis.Features:Classical survival analysis techniques for estimating statistical functional and hypotheses testingRegression methods covering the popular Cox relative risk regression model, Aalen’s additive hazards model, etc.Information criteria to facilitate model selection including Akaike, Bayes, and FocusedPenalized methodsSurvival trees and ensemble techniques of bagging, boosting, and random survival forestsA brief exposure of neural networks for survival dataR program illustration throughout the book

Survival analysis generally deals with analysis of data arising from clinical trials. Censoring, truncation, and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties, essentially asymptotic ones, of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades, survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way.

Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis.

Features:

Classical survival analysis techniques for estimating statistical functional and hypotheses testing Regression methods covering the popular Cox relative risk regression model, Aalen’s additive hazards model, etc. Information criteria to facilitate model selection including Akaike, Bayes, and Focused Penalized methods Survival trees and ensemble techniques of bagging, boosting, and random survival forests A brief exposure of neural networks for survival data R program illustration throughout the book

Survival analysis generally deals with analysis of data arising from clinical trials. Censoring, truncation, and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties, essentially asymptotic ones, of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades, survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way.

Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis.

Features:

Classical survival analysis techniques for estimating statistical functional and hypotheses testing Regression methods covering the popular Cox relative risk regression model, Aalen’s additive hazards model, etc. Information criteria to facilitate model selection including Akaike, Bayes, and Focused Penalized methods Survival trees and ensemble techniques of bagging, boosting, and random survival forests A brief exposure of neural networks for survival data R program illustration throughout the book

This collection of over fifty essays provides a comprehensive overview of the Pacific theater of World War II. Scholars and practitioners from around the globe unite their expertise to explore the interaction of the belligerents, with multiple chapters co-written by Japanese and American authors.Part I provides the political, economic, military, strategic, and legal backdrop. Part II highlights operational and strategic concepts and approaches: assessment, deterrence, strategic culture, war gaming and planning, access and anti-access campaigns, cumulative and sequential operations, strategic and operational intelligence, and logistics. Part III focuses on turning points: the Axis alliance, wartime mobilization, naval buildups, the key campaigns of Malaya, Midway, Guadalcanal, and Leyte Gulf, unrestricted submarine warfare, and aerial bombing. Part IV turns to the warzone societies with chapters on Australia, China, Indonesia, India, Indochina, Japan, Malaysia, and the Philippines. Part V concludes with the war’s diverse legacies: the Communist victory in China, suspicions of a Pearl Harbor coverup, the transformation of international law, the fate of veterans, the teaching of the war, its operational and doctrinal lessons, and the many movies and museums about it.The handbook offers original and enduring analyses that will interest scholars, students, policymakers, and history buffs alike.

Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models.Key features: Integrates R basics with statistical conceptsProvides graphical presentations inclusive of mathematical expressionsAids understanding of limit theorems of probability with and without the simulation approachPresents detailed algorithmic development of statistical models from scratchIncludes practical applications with over 50 data sets

Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models.

Key features:

Integrates R basics with statistical concepts Provides graphical presentations inclusive of mathematical expressions Aids understanding of limit theorems of probability with and without the simulation approach Presents detailed algorithmic development of statistical models from scratch Includes practical applications with over 50 data sets

Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models.

Key features:

Integrates R basics with statistical concepts Provides graphical presentations inclusive of mathematical expressions Aids understanding of limit theorems of probability with and without the simulation approach Presents detailed algorithmic development of statistical models from scratch Includes practical applications with over 50 data sets

Diseases of Shade Trees provides an introduction to tree diseases for students and others concerned with the care of shade and ornamental trees. Diseases of woody plants fall into two major categories: infectious diseases and noninfectious diseases. Part I of the book presents the infectious pathogens and the diseases they cause. These include bacteria, mycoplasmas, nematodes, seed plants, and viruses. The nature of the fungi is also presented, with separate chapters for leaf, root, rust, stem, and wilt diseases caused by fungi. Wound diseases, which are associated with both fungi and bacteria that invade wounds, are included in this section. Part II deals with noninfectious agents and the diseases they cause. Noninfectious agents are separated into environmental stress, animal injury, and people-pressure diseases. Diebacks and declines—complex diseases, which are often caused by a combination of both infectious and noninfectious agents are also included in this section. Part III on special topics includes discussions of nonpathogenic conditions often mistaken for diseases; diagnosis of tree diseases; and living hazard trees.

The Second Edition of this classic text is completely up-to-date with new chapters, new information on diseases, updated citations, and revised taxonomy and terminology of the fungi, bacteria, and other organisms that affect trees. Field and Laboratory Guide to Tree Pathology presents field and laboratory techniques as well as basic information for students, foresters, plant scientists, and arboriculturalists on tree disease pathology. The revised edition includes expanded historical documentation, updated taxonomy and terminology for both pests and diseases, an entirely new introduction, new chapters on tree biology, general control strategies, and diagnostic techniques. A new section of color plates will help readers in the identification of tree pathogens. All the references have been comprehensively updated, and the exercises included for students have been revised, making this guide a useful tool for students, teachers, and practitioners interested in tree disease.- Contains new chapters on tree biology, general control strategies, and diagnostic techniques- Includes additional information on the histories of disease- Provides thoroughly updated citations- Contains comprehensively revised taxonomy and terminology

Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniquesKey Features[] Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packages[] Understand how to apply useful data analysis techniques in R for real-world applications[] An easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysisBook DescriptionThis cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently.The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration.In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction.By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.What you will learn[] Get to know the functional characteristics of R language[] Extract, transform, and load data from heterogeneous sources[] Understand how easily R can confront probability and statistics problems[] Get simple R instructions to quickly organize and manipulate large datasets[] Create professional data visualizations and interactive reports[] Predict user purchase behavior by adopting a classification approach[] Implement data mining techniques to discover items that are frequently purchased together[] Group similar text documents by using various clustering methodsWho this book is forThis book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages.

Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques

About This Book

Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packagesUnderstand how to apply useful data analysis techniques in R for real-world applicationsAn easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysis

Who This Book Is For

This book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages.

What You Will Learn

Get to know the functional characteristics of R languageExtract, transform, and load data from heterogeneous sourcesUnderstand how easily R can confront probability and statistics problemsGet simple R instructions to quickly organize and manipulate large datasetsCreate professional data visualizations and interactive reportsPredict user purchase behavior by adopting a classification approachImplement data mining techniques to discover items that are frequently purchased togetherGroup similar text documents by using various clustering methods

In Detail

This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently.

The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration.

In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction.

By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.

Style and approach

This easy-to-follow guide is full of hands-on examples of data analysis with R. Each topic is fully explained beginning with the core concept, followed by step-by-step practical examples, and concluding with detailed explanations of each concept used.

Over 85 recipes to help you complete real-world data science projects in R and Python

About This Book

Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your dataGet beyond the theory and implement real-world projects in data science using R and PythonEasy-to-follow recipes will help you understand and implement the numerical computing concepts

Who This Book Is For

If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python.

What You Will Learn

Learn and understand the installation procedure and environment required for R and Python on various platformsPrepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and PythonBuild a predictive model and an exploratory modelAnalyze the results of your model and create reports on the acquired dataBuild various tree-based methods and Build random forest

In Detail

As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don''t. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use.

Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python.

Style and approach

This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization

Software Implementation Illustrated with R and PythonAbout This Book• Learn the nature of data through software which takes the preliminary concepts right away using R and Python.• Understand data modeling and visualization to perform efficient statistical analysis with this guide.• Get well versed with techniques such as regression, clustering, classification, support vector machines and much more to learn the fundamentals of modern statistics.Who This Book Is ForIf you want to have a brief understanding of the nature of data and perform advanced statistical analysis using both R and Python, then this book is what you need. No prior knowledge is required. Aspiring data scientist, R users trying to learn Python and vice versaWhat You Will Learn• Learn the nature of data through software with preliminary concepts right away in R• Read data from various sources and export the R output to other software• Perform effective data visualization with the nature of variables and rich alternative options• Do exploratory data analysis for useful first sight understanding building up to the right attitude towards effective inference• Learn statistical inference through simulation combining the classical inference and modern computational power• Delve deep into regression models such as linear and logistic for continuous and discrete regressands for forming the fundamentals of modern statistics• Introduce yourself to CART – a machine learning tool which is very useful when the data has an intrinsic nonlinearityIn DetailStatistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions.This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world.You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python.The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics.By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.Style and approachDeveloping better and smarter ways to analyze data. Making better decisions/future predictions. Learn how to explore, visualize and perform statistical analysis. Better and efficient statistical and computational methods. Perform practical examples to master your learning

Software Implementation Illustrated with R and Python

About This Book

Learn the nature of data through software which takes the preliminary concepts right away using R and Python.Understand data modeling and visualization to perform efficient statistical analysis with this guide.Get well versed with techniques such as regression, clustering, classification, support vector machines and much more to learn the fundamentals of modern statistics.

Who This Book Is For

If you want to have a brief understanding of the nature of data and perform advanced statistical analysis using both R and Python, then this book is what you need. No prior knowledge is required. Aspiring data scientist, R users trying to learn Python and vice versa

What You Will Learn

Learn the nature of data through software with preliminary concepts right away in RRead data from various sources and export the R output to other softwarePerform effective data visualization with the nature of variables and rich alternative optionsDo exploratory data analysis for useful first sight understanding building up to the right attitude towards effective inferenceLearn statistical inference through simulation combining the classical inference and modern computational powerDelve deep into regression models such as linear and logistic for continuous and discrete regressands for forming the fundamentals of modern statisticsIntroduce yourself to CART – a machine learning tool which is very useful when the data has an intrinsic nonlinearity

In Detail

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions.

This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world.

You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python.

The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics.

By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.

Style and approach

Developing better and smarter ways to analyze data. Making better decisions/future predictions. Learn how to explore, visualize and perform statistical analysis. Better and efficient statistical and computational methods. Perform practical examples to master your learning

Explore powerful R packages to create predictive models using ensemble methodsKey FeaturesImplement machine learning algorithms to build ensemble-efficient modelsExplore powerful R packages to create predictive models using ensemble methodsLearn to build ensemble models on large datasets using a practical approachBook DescriptionEnsemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy.Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques – bagging, random forest, and boosting – then you'll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models.By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples.What you will learnCarry out an essential review of re-sampling methods, bootstrap, and jackknifeExplore the key ensemble methods: bagging, random forests, and boostingUse multiple algorithms to make strong predictive modelsEnjoy a comprehensive treatment of boosting methodsSupplement methods with statistical tests, such as ROCWalk through data structures in classification, regression, survival, and time series dataUse the supplied R code to implement ensemble methodsLearn stacking method to combine heterogeneous machine learning modelsWho this book is forThis book is for you if you are a data scientist or machine learning developer who wants to implement machine learning techniques by building ensemble models with the power of R. You will learn how to combine different machine learning algorithms to perform efficient data processing. Basic knowledge of machine learning techniques and programming knowledge of R would be an added advantage.

Explore powerful R packages to create predictive models using ensemble methods

Key Features

Implement machine learning algorithms to build ensemble-efficient modelsExplore powerful R packages to create predictive models using ensemble methodsLearn to build ensemble models on large datasets using a practical approach

Book Description

Ensemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy.

Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques – bagging, random forest, and boosting – then you''ll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models.

By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples.

What you will learn

Carry out an essential review of re-sampling methods, bootstrap, and jackknifeExplore the key ensemble methods: bagging, random forests, and boostingUse multiple algorithms to make strong predictive modelsEnjoy a comprehensive treatment of boosting methodsSupplement methods with statistical tests, such as ROCWalk through data structures in classification, regression, survival, and time series dataUse the supplied R code to implement ensemble methodsLearn stacking method to combine heterogeneous machine learning models

Who this book is for

This book is for you if you are a data scientist or machine learning developer who wants to implement machine learning techniques by building ensemble models with the power of R. You will learn how to combine different machine learning algorithms to perform efficient data processing. Basic knowledge of machine learning techniques and programming knowledge of R would be an added advantage.