Practical Guide To Principal Component Methods In R

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Practical Guide To Principal Component Methods In R

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Author : Alboukadel KASSAMBARA
language : en
Publisher: STHDA
Release Date : 2017-08-23

Practical Guide To Principal Component Methods In R written by Alboukadel KASSAMBARA and has been published by STHDA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-23 with Education categories.

Although there are several good books on principal component methods (PCMs) and related topics, we felt that many of them are either too theoretical or too advanced. This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods in R. The visualization is based on the factoextra R package that we developed for creating easily beautiful ggplot2-based graphs from the output of PCMs. This book contains 4 parts. Part I provides a quick introduction to R and presents the key features of FactoMineR and factoextra. Part II describes classical principal component methods to analyze data sets containing, predominantly, either continuous or categorical variables. These methods include: Principal Component Analysis (PCA, for continuous variables), simple correspondence analysis (CA, for large contingency tables formed by two categorical variables) and Multiple CA (MCA, for a data set with more than 2 categorical variables). In Part III, you'll learn advanced methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA). Part IV covers hierarchical clustering on principal components (HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variables.

Practical Guide To Cluster Analysis In R

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Author : Alboukadel Kassambara
language : en
Publisher: STHDA
Release Date : 2017-08-23

Practical Guide To Cluster Analysis In R written by Alboukadel Kassambara and has been published by STHDA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-23 with Education categories.

Although there are several good books on unsupervised machine learning, we felt that many of them are too theoretical. This book provides practical guide to cluster analysis, elegant visualization and interpretation. It contains 5 parts. Part I provides a quick introduction to R and presents required R packages, as well as, data formats and dissimilarity measures for cluster analysis and visualization. Part II covers partitioning clustering methods, which subdivide the data sets into a set of k groups, where k is the number of groups pre-specified by the analyst. Partitioning clustering approaches include: K-means, K-Medoids (PAM) and CLARA algorithms. In Part III, we consider hierarchical clustering method, which is an alternative approach to partitioning clustering. The result of hierarchical clustering is a tree-based representation of the objects called dendrogram. In this part, we describe how to compute, visualize, interpret and compare dendrograms. Part IV describes clustering validation and evaluation strategies, which consists of measuring the goodness of clustering results. Among the chapters covered here, there are: Assessing clustering tendency, Determining the optimal number of clusters, Cluster validation statistics, Choosing the best clustering algorithms and Computing p-value for hierarchical clustering. Part V presents advanced clustering methods, including: Hierarchical k-means clustering, Fuzzy clustering, Model-based clustering and Density-based clustering.

Complete Guide To 3d Plots In R

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Author : Alboukadel KASSAMBARA
language : en
Publisher: Alboukadel KASSAMBARA
Release Date :

Complete Guide To 3d Plots In R written by Alboukadel KASSAMBARA and has been published by Alboukadel KASSAMBARA this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

This book provides a complete guide for visualizing a data in 3 dimensions (3D) using R software. It contains 2 main parts and 7 chapters describing how to draw static and interactive 3D plots. - The chapter 1 is about data preparation for 3D plot - In chapter 2, we describe how to create easily basic static 3D scatter plots. We provide R codes for changing: 1) main and axis titles; 2) the appearance of the plot (point colors, labels and shapes, legend position, ...) - Chapter 3 presents how to create advanced static 3D plots including 3D scatter plots with confidence interval, 3D line plots, 3D texts, 3D barplots, 3D histograms and 3D arrows. - Chapter 4 describes the required package for drawing interactive 3D plots. - In chapter 5, we show how to transform easily an existing static 3D plot into aninteractive 3D plot. - Chapter 6 provides many examples of R codes for creating interactive 3D scatter plotswith 3D regression surfaces and concentration ellipsoids. We describe also how to exportthese graphs as png or pdf files. - Chapter 7 presents a complete guide to RGL 3D visualization device system. We provide also R codes for creating a movie from RGL 3D scene and for exporting plot into an interactive HTML web file. Each chapter is organized as an independent quick start guide. This means that, you don’tneed to read the different chapters in sequence.

Applied Unsupervised Learning With R

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Author : Alok Malik
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-03-27

Applied Unsupervised Learning With R written by Alok Malik and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-27 with Computers categories.

Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data. Key FeaturesBuild state-of-the-art algorithms that can solve your business' problemsLearn how to find hidden patterns in your dataRevise key concepts with hands-on exercises using real-world datasetsBook Description Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions. This book begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. By the end of this book, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection. What you will learnImplement clustering methods such as k-means, agglomerative, and divisiveWrite code in R to analyze market segmentation and consumer behaviorEstimate distribution and probabilities of different outcomesImplement dimension reduction using principal component analysisApply anomaly detection methods to identify fraudDesign algorithms with R and learn how to edit or improve codeWho this book is for Applied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning. Although the book is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this book, you should also know basic mathematical concepts, including exponents, square roots, means, and medians.

Machine Learning Essentials

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Author : Alboukadel Kassambara
language : en
Publisher: STHDA
Release Date : 2018-03-10

Machine Learning Essentials written by Alboukadel Kassambara and has been published by STHDA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-10 with Computers categories.

Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring real word data sets, as well as, for building predictive models. The main parts of the book include: A) Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. B) Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. C) Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. D) Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). E) Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. F) Model validation and evaluation techniques for measuring the performance of a predictive model. G) Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers. Key features: - Covers machine learning algorithm and implementation - Key mathematical concepts are presented - Short, self-contained chapters with practical examples.

Handbook Of Inter Rater Reliability 4th Edition

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Author : Kilem L. Gwet
language : en
Publisher: Advanced Analytics, LLC
Release Date : 2014-09-07

Handbook Of Inter Rater Reliability 4th Edition written by Kilem L. Gwet and has been published by Advanced Analytics, LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-07 with Medical categories.

The third edition of this book was very well received by researchers working in many different fields of research. The use of that text also gave these researchers the opportunity to raise questions, and express additional needs for materials on techniques poorly covered in the literature. For example, when designing an inter-rater reliability study, many researchers wanted to know how to determine the optimal number of raters and the optimal number of subjects that should participate in the experiment. Also, very little space in the literature has been devoted to the notion of intra-rater reliability, particularly for quantitative measurements. The fourth edition of this text addresses those needs, in addition to further refining the presentation of the material already covered in the third edition. Features of the Fourth Edition include: New material on sample size calculations for chance-corrected agreement coefficients, as well as for intraclass correlation coefficients. The researcher will be able to determine the optimal number raters, subjects, and trials per subject.The chapter entitled “Benchmarking Inter-Rater Reliability Coefficients” has been entirely rewritten.The introductory chapter has been substantially expanded to explore possible definitions of the notion of inter-rater reliability.All chapters have been revised to a large extent to improve their readability.

Hands On Machine Learning With R

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Author : Brad Boehmke
language : en
Publisher: CRC Press
Release Date : 2019-11-07

Hands On Machine Learning With R written by Brad Boehmke and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-07 with Business & Economics categories.

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.

R For Political Data Science

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Author : Francisco Urdinez
language : en
Publisher: CRC Press
Release Date : 2020-11-17

R For Political Data Science written by Francisco Urdinez and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-17 with Political Science categories.

R for Political Data Science: A Practical Guide is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was written by political scientists, thinking about the many real-world problems faced in their work. The book has 16 chapters and is organized in three sections. The first, on the use of R, is for those users who are learning R or are migrating from another software. The second section, on econometric models, covers OLS, binary and survival models, panel data, and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation, fuzzy merge of large datasets, web mining, quantitative text analysis, network analysis, mapping, spatial cluster analysis, and principal component analysis. Key features: Each chapter has the most up-to-date and simple option available for each task, assuming minimal prerequisites and no previous experience in R Makes extensive use of the Tidyverse, the group of packages that has revolutionized the use of R Provides a step-by-step guide that you can replicate using your own data Includes exercises in every chapter for course use or self-study Focuses on practical-based approaches to statistical inference rather than mathematical formulae Supplemented by an R package, including all data As the title suggests, this book is highly applied in nature, and is designed as a toolbox for the reader. It can be used in methods and data science courses, at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD, political consultants, or a public official, all of whom need to transform their datasets into substantive and easily interpretable conclusions.

R Graphics Essentials For Great Data Visualization

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Author : Alboukadel Kassambara
language : en
Publisher: STHDA
Release Date : 2017-11-14

R Graphics Essentials For Great Data Visualization written by Alboukadel Kassambara and has been published by STHDA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-14 with Computers categories.

Data visualization is one of the most important part of data science. Many books and courses present a catalogue of graphics but they don't teach you which charts to use according to the type of the data. In this book, we start by presenting the key graphic systems and packages available in R, including R base graphs, lattice and ggplot2 plotting systems. Next, we provide more than 200 practical examples to create great graphics for the right data using either the ggplot2 package and extensions or the traditional R graphics. With this book, you 'll learn: - How to quickly create beautiful graphics using ggplot2 packages - How to properly customize and annotate the plots - Type of graphics for visualizing categorical and continuous variables - How to add automatically p-values to box plots, bar plots and alternatives - How to add marginal density plots and correlation coefficients to scatter plots - Key methods for analyzing and visualizing multivariate data - R functions and packages for plotting time series data - How to combine multiple plots on one page to create production-quality figures.