Format:
1 online resource (398 pages)
Edition:
1st ed.
ISBN:
9781785885877
Content:
About This BookLearn five different simulation techniques (Monte Carlo, Discrete Event Simulation, System Dynamics, Agent-Based Modeling, and Resampling) in-depth using real-world case studiesA unique book that teaches you the essential and fundamental concepts in statistical modeling and simulationWho This Book Is For This book is for users who are familiar with computational methods and R. If you want to learn about the advanced features of R, with the computer-intense Monte Carlo methods and tools for statistical simulation, then this book is for you. What You Will LearnExplore advanced R features for data simulation and resampling purposes in order to extract insights from your dataGet to know the advanced features of R including high-performance computing and advanced data manipulationSee random number simulation used to simulate distributions, data sets, and populationsSimulate close-to-reality data as the basis for agent-based micro-, model- and, design-based simulationsWork with applications to design statistical solutions with R for solving scientific and real-world problemsGet comprehensive coverage of several R statistical packages such as boot, simPop, VIM, data.table, dplyr, cvTools, deSolve, and many moreExplore many examples and implementation of methods in R exclusively available in this bookIn Detail Simulation for Data Science with R aims to teach you how to begin performing data science tasks by taking advantage of R's powerful ecosystem of packages and software environments. R is the most widely used programming language and, when used with data science, it can be a great combination to solve the problems involved with varied data sets in the real world. This book will provide a computational and practical framework for statistical simulation to the users. You will get in grips with the software environment R. After learning
Content:
about the background of popular methods in the area of statistics, you will see some applications in R to find out about the methods as well as gain experience of working with real-world data. This book helps uncover the large-scale patterns in complex systems where interdependencies and variation are critical. You will learn to structure a simulation project to aid in the decision-making process and the presentation of results. Toward the end of this book, you will get in touch with the software environment R. After getting familiar with the popular methods in the area, you will see R applications in order to better know the methods and to gain experience when working on real-world data and problems
Note:
Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Introduction -- What is simulation and where is it applied? -- Why use simulation? -- Simulation and big data -- Choosing the right simulation technique -- Summary -- References -- Chapter 2: R and High Performance Computing -- The R statistical environment -- Basics in R -- Some very basic stuff about R -- Installation and updates -- Help -- The R workspace and the working directory -- Data types -- Vectors in R -- Factors in R -- list -- data.frame -- array -- Missing values -- Generic functions, methods, and classes -- Data manipulation in R -- Apply and friends with basic R -- Basic data manipulation with the dplyr package -- dplyr - creating a local data frame -- dplyr - selecting lines -- dplyr - order -- dplyr - selecting columns -- dplyr - uniqueness -- dplyr - creating variables -- dplyr - grouping and aggregates -- dplyr - window functions -- Data manipulation with the data.table package -- data.table - variable construction -- data.table - indexing or subsetting -- data.table - keys -- data.table - fast subsetting -- data.table - calculations in groups -- High performance computing -- Profiling to detect computationally slow functions in code -- Further benchmarking -- Parallel computing -- Interfaces to C++ -- Visualizing information -- The graphics system in R -- The graphics package -- Warm-up example - a high-level plot -- Control of graphics parameters -- The ggplot2 package -- References -- Chapter 3: The Discrepancy between Pencil-driven Theory and Data-driven Computational Solutions -- Machine numbers and rounding problems -- Example - the 64-bit representation of numbers -- Convergence in the deterministic case -- Example - convergence -- Condition of problems -- Summary -- References
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A motivating example with odds ratios -- Why the bootstrap works -- A closer look at the bootstrap -- The plug-in principle -- Estimation of standard errors with bootstrapping -- An example of a complex estimation using the bootstrap -- The parametric bootstrap -- Estimating bias with bootstrap -- Confidence intervals by bootstrap -- The jackknife -- Disadvantages of the jackknife -- The delete-d jackknife -- Jackknife after bootstrap -- Cross-validation -- The classical linear regression model -- The basic concept of cross validation -- Classical cross validation - 70/30 method -- Leave-one-out cross validation -- k-fold cross validation -- Summary -- References -- Chapter 8: Applications of Resampling Methods and Monte Carlo Tests -- The bootstrap in regression analysis -- Motivation to use the bootstrap -- The most popular but often worst method -- Bootstrapping by draws from residuals -- Proper variance estimation with missing values -- Bootstrapping in time series -- Bootstrapping in the case of complex sampling designs -- Monte Carlo tests -- A motivating example -- The permutation test as a special kind of MC test -- A Monte Carlo test for multiple groups -- Hypothesis testing using a bootstrap -- A test for multivariate normality -- Size of the test -- Power comparisons -- Summary -- References -- Chapter 9: The EM Algorithm -- The basic EM algorithm -- Some prerequisites -- Formal definition of the EM algorithm -- Introductory example for the EM algorithm -- The EM algorithm by example of k-means clustering -- The EM algorithm for the imputation of missing values -- Summary -- References -- Chapter 10: Simulation with Complex Data -- Different kinds of simulation and software -- Simulating data using complex models -- A model-based simple example -- A model-based example with mixtures -- Model-based approach to simulate data
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An example of simulating high-dimensional data -- Simulating finite populations with cluster or hierarchical structures -- Model-based simulation studies -- Latent model example continued -- A simple example of model-based simulation -- A model-based simulation study -- Design-based simulation -- An example with complex survey data -- Simulation of the synthetic population -- Estimators of interest -- Defining the sampling design -- Using stratified sampling -- Adding contamination -- Performing simulations separately on different domains -- Inserting missing values -- Summary -- References -- Chapter 11: System Dynamics and Agent-Based Models -- Agent-based models -- Dynamics in love and hate -- Dynamic systems in ecological modeling -- Summary -- References -- Index
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Chapter 4: Simulation of Random Numbers -- Real random numbers -- Simulating pseudo random numbers -- Congruential generators -- Linear and multiplicative congruential generators -- Lagged Fibonacci generators -- More generators -- Simulation of non-uniform distributed random variables -- The inversion method -- The alias method -- Estimation of counts in tables with log-linear models -- Rejection sampling -- Truncated distributions -- Metropolis - Hastings algorithm -- A few words on Markov chains -- The Metropolis sampler -- The Gibbs sampler -- The two-phase Gibbs sampler -- The multiphase Gibbs sampler -- Application in linear regression -- The diagnosis of MCMC samples -- Tests for random numbers -- The evaluation of random numbers - an example of a test -- Summary -- References -- Chapter 5: Monte Carlo Methods for Optimization Problems -- Numerical optimization -- Gradient ascent/descent -- Newton-Raphson methods -- Further general-purpose optimization methods -- Dealing with stochastic optimization -- Simplified procedures (Star Trek, Spaceballs, and Spaceballs princess) -- Metropolis-Hastings revisited -- Gradient-based stochastic optimization -- Summary -- References -- Chapter 6: Probability Theory Shown by Simulation -- Some basics on probability theory -- Probability distributions -- Discrete probability distributions -- Continuous probability distributions -- Winning the lottery -- The weak law on large numbers -- Emperor penguins and your boss -- Limits and convergence of random variables -- Convergence of the sample mean - weak law of large numbers -- Showing the weak law of large numbers by simulation -- The central limit theorem -- Properties of estimators -- Properties of estimators -- Confidence intervals -- A note on robust estimators -- Summary -- References -- Chapter 7: Resampling Methods -- The bootstrap
Additional Edition:
Print version Templ, Matthias Simulation for Data Science with R Birmingham : Packt Publishing, Limited,c2016
Language:
English
Keywords:
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